Supporting Information Linking and Discovery Across Organizations Using the VIVO Semantic Web Software Suite

NASA Astrophysics Data System (ADS)

Mayernik, M. S.; Daniels, M. D.; Maull, K. E.; Khan, H.; Krafft, D. B.; Gross, M. B.; Rowan, L. R.

2016-12-01

Geosciences research is often conducted using distributed networks of researchers and resources. To better enable the discovery of the research output from the scientists and resources used within these organizations, UCAR, Cornell University, and UNAVCO are collaborating on the EarthCollab (http://earthcube.org/group/earthcollab) project which seeks to leverage semantic technologies to manage and link scientific data. As part of this effort, we have been exploring how to leverage information distributed across multiple research organizations. EarthCollab is using the VIVO semantic software suite to lookup and display Semantic Web information across our project partners.Our presentation will include a demonstration of linking between VIVO instances, discussing how to create linkages between entities in different VIVO instances where both entities describe the same person or resource. This discussion will explore how we designate the equivalence of these entities using "same as" assertions between identifiers representing these entities including URIs and ORCID IDs and how we have extended the base VIVO architecture to support the lookup of which entities in separate VIVO instances may be equivalent and to then display information from external linked entities. We will also discuss how these extensions can support other linked data lookups and sources of information.This VIVO cross-linking mechanism helps bring information from multiple VIVO instances together and helps users in navigating information spread-out between multiple VIVO instances. Challenges and open questions for this approach relate to how to display the information obtained from an external VIVO instance, both in order to preserve the brands of the internal and external systems and to handle discrepancies between ontologies, content, and/or VIVO versions.

Enhancement of Chemical Entity Identification in Text Using Semantic Similarity Validation

PubMed Central

Grego, Tiago; Couto, Francisco M.

2013-01-01

With the amount of chemical data being produced and reported in the literature growing at a fast pace, it is increasingly important to efficiently retrieve this information. To tackle this issue text mining tools have been applied, but despite their good performance they still provide many errors that we believe can be filtered by using semantic similarity. Thus, this paper proposes a novel method that receives the results of chemical entity identification systems, such as Whatizit, and exploits the semantic relationships in ChEBI to measure the similarity between the entities found in the text. The method assigns a single validation score to each entity based on its similarities with the other entities also identified in the text. Then, by using a given threshold, the method selects a set of validated entities and a set of outlier entities. We evaluated our method using the results of two state-of-the-art chemical entity identification tools, three semantic similarity measures and two text window sizes. The method was able to increase precision without filtering a significant number of correctly identified entities. This means that the method can effectively discriminate the correctly identified chemical entities, while discarding a significant number of identification errors. For example, selecting a validation set with 75% of all identified entities, we were able to increase the precision by 28% for one of the chemical entity identification tools (Whatizit), maintaining in that subset 97% the correctly identified entities. Our method can be directly used as an add-on by any state-of-the-art entity identification tool that provides mappings to a database, in order to improve their results. The proposed method is included in a freely accessible web tool at www.lasige.di.fc.ul.pt/webtools/ice/. PMID:23658791

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.

PubMed

Sun, Maoyuan; Mi, Peng; North, Chris; Ramakrishnan, Naren

2016-01-01

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, "in-between", to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

PubMed

Bakal, Gokhan; Talari, Preetham; Kakani, Elijah V; Kavuluru, Ramakanth

2018-06-01

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction. Copyright © 2018 Elsevier Inc. All rights reserved.

Integration of nursing assessment concepts into the medical entities dictionary using the LOINC semantic structure as a terminology model.

PubMed

Cieslowski, B J; Wajngurt, D; Cimino, J J; Bakken, S

2001-01-01

Recent investigations have tested the applicability of various terminology models for the representing nursing concepts including those related to nursing diagnoses, nursing interventions, and standardized nursing assessments as a prerequisite for building a reference terminology that supports the nursing domain. We used the semantic structure of Clinical LOINC (Logical Observations, Identifiers, Names, and Codes) as a reference terminology model to support the integration of standardized assessment terms from two nursing terminologies into the Medical Entities Dictionary (MED), the concept-oriented, metadata dictionary at New York Presbyterian Hospital. Although the LOINC semantic structure was used previously to represent laboratory terms in the MED, selected hierarchies and semantic slots required revisions in order to incorporate the nursing assessment concepts. This project was an initial step in integrating nursing assessment concepts into the MED in a manner consistent with evolving standards for reference terminology models. Moreover, the revisions provide the foundation for adding other types of standardized assessments to the MED.

Integration of nursing assessment concepts into the medical entities dictionary using the LOINC semantic structure as a terminology model.

PubMed Central

Cieslowski, B. J.; Wajngurt, D.; Cimino, J. J.; Bakken, S.

2001-01-01

Recent investigations have tested the applicability of various terminology models for the representing nursing concepts including those related to nursing diagnoses, nursing interventions, and standardized nursing assessments as a prerequisite for building a reference terminology that supports the nursing domain. We used the semantic structure of Clinical LOINC (Logical Observations, Identifiers, Names, and Codes) as a reference terminology model to support the integration of standardized assessment terms from two nursing terminologies into the Medical Entities Dictionary (MED), the concept-oriented, metadata dictionary at New York Presbyterian Hospital. Although the LOINC semantic structure was used previously to represent laboratory terms in the MED, selected hierarchies and semantic slots required revisions in order to incorporate the nursing assessment concepts. This project was an initial step in integrating nursing assessment concepts into the MED in a manner consistent with evolving standards for reference terminology models. Moreover, the revisions provide the foundation for adding other types of standardized assessments to the MED. PMID:11825165

Semantic modeling of the structural and process entities during plastic deformation of crystals and rocks

NASA Astrophysics Data System (ADS)

Babaie, Hassan; Davarpanah, Armita

2016-04-01

We are semantically modeling the structural and dynamic process components of the plastic deformation of minerals and rocks in the Plastic Deformation Ontology (PDO). Applying the Ontology of Physics in Biology, the PDO classifies the spatial entities that participate in the diverse processes of plastic deformation into the Physical_Plastic_Deformation_Entity and Nonphysical_Plastic_Deformation_Entity classes. The Material_Physical_Plastic_Deformation_Entity class includes things such as microstructures, lattice defects, atoms, liquid, and grain boundaries, and the Immaterial_Physical_Plastic_Deformation_Entity class includes vacancies in crystals and voids along mineral grain boundaries. The objects under the many subclasses of these classes (e.g., crystal, lattice defect, layering) have spatial parts that are related to each other through taxonomic (e.g., Line_Defect isA Lattice_Defect), structural (mereological, e.g., Twin_Plane partOf Twin), spatial-topological (e.g., Vacancy adjacentTo Atom, Fluid locatedAlong Grain_Boundary), and domain specific (e.g., displaces, Fluid crystallizes Dissolved_Ion, Void existsAlong Grain_Boundary) relationships. The dynamic aspect of the plastic deformation is modeled under the dynamical Process_Entity class that subsumes classes such as Recrystallization and Pressure_Solution that define the flow of energy amongst the physical entities. The values of the dynamical state properties of the physical entities (e.g., Chemical_Potential, Temperature, Particle_Velocity) change while they take part in the deformational processes such as Diffusion and Dislocation_Glide. The process entities have temporal parts (phases) that are related to each other through temporal relations such as precedes, isSubprocessOf, and overlaps. The properties of the physical entities, defined under the Physical_Property class, change as they participate in the plastic deformational processes. The properties are categorized into dynamical, constitutive, spatial, temporal, statistical, and thermodynamical. The dynamical properties, categorized under the Dynamical_Rate_Property and Dynamical_State_Property classes, subsume different classes of properties (e.g., Fluid_Flow_Rate, Temperature, Chemical_Potential, Displacement, Electrical_Charge) based on the physical domain (e.g., fluid, heat, chemical, solid, electrical). The properties are related to the objects under the Physical_Entity class through diverse object type (e.g., physicalPropertyOf) and data type (e.g., Fluid_Pressure unit 'MPa') properties. The changes of the dynamical properties of the physical entities, described by the empirical laws (equations) modeled by experimental structural geologists, are modeled through the Physical_Property_Dependency class that subsumes the more specialized constitutive, kinetic, and thermodynamic expressions of the relationships among the dynamic properties. Annotation based on the PDO will make it possible to integrate and reuse experimental plastic deformation data, knowledge, and simulation models, and conduct semantic-based search of the source data originating from different rock testing laboratories.

Spatio-Temporal Data Model for Integrating Evolving Nation-Level Datasets

NASA Astrophysics Data System (ADS)

Sorokine, A.; Stewart, R. N.

2017-10-01

Ability to easily combine the data from diverse sources in a single analytical workflow is one of the greatest promises of the Big Data technologies. However, such integration is often challenging as datasets originate from different vendors, governments, and research communities that results in multiple incompatibilities including data representations, formats, and semantics. Semantics differences are hardest to handle: different communities often use different attribute definitions and associate the records with different sets of evolving geographic entities. Analysis of global socioeconomic variables across multiple datasets over prolonged time is often complicated by the difference in how boundaries and histories of countries or other geographic entities are represented. Here we propose an event-based data model for depicting and tracking histories of evolving geographic units (countries, provinces, etc.) and their representations in disparate data. The model addresses the semantic challenge of preserving identity of geographic entities over time by defining criteria for the entity existence, a set of events that may affect its existence, and rules for mapping between different representations (datasets). Proposed model is used for maintaining an evolving compound database of global socioeconomic and environmental data harvested from multiple sources. Practical implementation of our model is demonstrated using PostgreSQL object-relational database with the use of temporal, geospatial, and NoSQL database extensions.

Mental representation of normal subjects about the sources of knowledge in different semantic categories and unique entities.

PubMed

Gainotti, Guido; Ciaraffa, Francesca; Silveri, Maria Caterina; Marra, Camillo

2009-11-01

According to the "sensory-motor model of semantic knowledge," different categories of knowledge differ for the weight that different "sources of knowledge" have in their representation. Our study aimed to evaluate this model, checking if subjective evaluations given by normal subjects confirm the different weight that various sources of knowledge have in the representation of different biological and artifact categories and of unique entities, such as famous people or monuments. Results showed that the visual properties are considered as the main source of knowledge for all the living and nonliving categories (as well as for unique entities), but that the clustering of these "sources of knowledge" is different for biological and artifacts categories. Visual data are, indeed, mainly associated with other perceptual (auditory, olfactory, gustatory, and tactual) attributes in the mental representation of living beings and unique entities, whereas they are associated with action-related properties and tactile information in the case of artifacts.

ECO: A Framework for Entity Co-Occurrence Exploration with Faceted Navigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halliday, K. D.

2010-08-20

Even as highly structured databases and semantic knowledge bases become more prevalent, a substantial amount of human knowledge is reported as written prose. Typical textual reports, such as news articles, contain information about entities (people, organizations, and locations) and their relationships. Automatically extracting such relationships from large text corpora is a key component of corporate and government knowledge bases. The primary goal of the ECO project is to develop a scalable framework for extracting and presenting these relationships for exploration using an easily navigable faceted user interface. ECO uses entity co-occurrence relationships to identify related entities. The system aggregates andmore » indexes information on each entity pair, allowing the user to rapidly discover and mine relational information.« less

Semantic domain-specific functional integration for action-related vs. abstract concepts.

PubMed

Ghio, Marta; Tettamanti, Marco

2010-03-01

A central topic in cognitive neuroscience concerns the representation of concepts and the specific neural mechanisms that mediate conceptual knowledge. Recently proposed modal theories assert that concepts are grounded on the integration of multimodal, distributed representations. The aim of the present work is to complement the available neuropsychological and neuroimaging evidence suggesting partially segregated anatomo-functional correlates for concrete vs. abstract concepts, by directly testing the semantic domain-specific patterns of functional integration between language and modal semantic brain regions. We report evidence from a functional magnetic resonance imaging study, in which healthy participants listened to sentences with either an action-related (actions involving physical entities) or an abstract (no physical entities involved) content. We measured functional integration using dynamic causal modeling, and found that the left superior temporal gyrus was more strongly connected: (1) for action-related vs. abstract sentences, with the left-hemispheric action representation system, including sensorimotor areas; (2) for abstract vs. action-related sentences, with left infero-ventral frontal, temporal, and retrosplenial cingulate areas. A selective directionality effect was observed, with causal modulatory effects exerted by perisylvian language regions on peripheral modal areas, and not vice versa. The observed condition-specific modulatory effects are consistent with embodied and situated language processing theories, and indicate that linguistic areas promote a semantic content-specific reactivation of modal simulations by top-down mechanisms. Copyright 2008 Elsevier Inc. All rights reserved.

Contextually guided very-high-resolution imagery classification with semantic segments

NASA Astrophysics Data System (ADS)

Zhao, Wenzhi; Du, Shihong; Wang, Qiao; Emery, William J.

2017-10-01

Contextual information, revealing relationships and dependencies between image objects, is one of the most important information for the successful interpretation of very-high-resolution (VHR) remote sensing imagery. Over the last decade, geographic object-based image analysis (GEOBIA) technique has been widely used to first divide images into homogeneous parts, and then to assign semantic labels according to the properties of image segments. However, due to the complexity and heterogeneity of VHR images, segments without semantic labels (i.e., semantic-free segments) generated with low-level features often fail to represent geographic entities (such as building roofs usually be partitioned into chimney/antenna/shadow parts). As a result, it is hard to capture contextual information across geographic entities when using semantic-free segments. In contrast to low-level features, "deep" features can be used to build robust segments with accurate labels (i.e., semantic segments) in order to represent geographic entities at higher levels. Based on these semantic segments, semantic graphs can be constructed to capture contextual information in VHR images. In this paper, semantic segments were first explored with convolutional neural networks (CNN) and a conditional random field (CRF) model was then applied to model the contextual information between semantic segments. Experimental results on two challenging VHR datasets (i.e., the Vaihingen and Beijing scenes) indicate that the proposed method is an improvement over existing image classification techniques in classification performance (overall accuracy ranges from 82% to 96%).

The Semantics of Plurals: A Defense of Singularism

ERIC Educational Resources Information Center

Florio, Salvatore

2010-01-01

In this dissertation, I defend "semantic singularism", which is the view that syntactically plural terms, such as "they" or "Russell and Whitehead", are semantically singular. A semantically singular term is a term that denotes a single entity. Semantic singularism is to be distinguished from "syntactic singularism", according to which…

Semantic Web Technology for Mapping and Applying Clinical Functional Assessment Information

DTIC Science & Technology

2014-03-01

the history and treatment of the injury or illness, is used by the MEB to determine whether the member has a medical condition that is incompatible...entities being measured are often represented by external terminologies , and assessments that are abstractions conceptually closer to the notions that...that describe the semantics of functional and related data elements, their relationships to standard terminologies and classifications, models of

USI: a fast and accurate approach for conceptual document annotation.

PubMed

Fiorini, Nicolas; Ranwez, Sylvie; Montmain, Jacky; Ranwez, Vincent

2015-03-14

Semantic approaches such as concept-based information retrieval rely on a corpus in which resources are indexed by concepts belonging to a domain ontology. In order to keep such applications up-to-date, new entities need to be frequently annotated to enrich the corpus. However, this task is time-consuming and requires a high-level of expertise in both the domain and the related ontology. Different strategies have thus been proposed to ease this indexing process, each one taking advantage from the features of the document. In this paper we present USI (User-oriented Semantic Indexer), a fast and intuitive method for indexing tasks. We introduce a solution to suggest a conceptual annotation for new entities based on related already indexed documents. Our results, compared to those obtained by previous authors using the MeSH thesaurus and a dataset of biomedical papers, show that the method surpasses text-specific methods in terms of both quality and speed. Evaluations are done via usual metrics and semantic similarity. By only relying on neighbor documents, the User-oriented Semantic Indexer does not need a representative learning set. Yet, it provides better results than the other approaches by giving a consistent annotation scored with a global criterion - instead of one score per concept.

Knowledge Representation Issues in Semantic Graphs for Relationship Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barthelemy, M; Chow, E; Eliassi-Rad, T

2005-02-02

An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a ''semantic graph'', also known as a ''relational data graph'' or an ''attributed relational graph''. These graphs encode relationships as typed links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks used in AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore, each node has a set of attributes associated with it (e.g., ''age'' maymore » be an attribute of a node of type ''person''). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and is somewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operates on semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and provide some possible solutions using recently developed ideas in the field of complex networks. In particular, we use the concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting relationships. We also propose new statistical measures for semantic graphs and illustrate these semantic measures on graphs constructed from movies and terrorism data.« less

Structure Discovery in Large Semantic Graphs Using Extant Ontological Scaling and Descriptive Statistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

al-Saffar, Sinan; Joslyn, Cliff A.; Chappell, Alan R.

As semantic datasets grow to be very large and divergent, there is a need to identify and exploit their inherent semantic structure for discovery and optimization. Towards that end, we present here a novel methodology to identify the semantic structures inherent in an arbitrary semantic graph dataset. We first present the concept of an extant ontology as a statistical description of the semantic relations present amongst the typed entities modeled in the graph. This serves as a model of the underlying semantic structure to aid in discovery and visualization. We then describe a method of ontological scaling in which themore » ontology is employed as a hierarchical scaling filter to infer different resolution levels at which the graph structures are to be viewed or analyzed. We illustrate these methods on three large and publicly available semantic datasets containing more than one billion edges each. Keywords-Semantic Web; Visualization; Ontology; Multi-resolution Data Mining;« less

Representation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence

PubMed Central

Neyens, Veerle; Bruffaerts, Rose; Liuzzi, Antonietta G.; Kalfas, Ioannis; Peeters, Ronald; Keuleers, Emmanuel; Vogels, Rufin; De Deyne, Simon; Storms, Gert; Dupont, Patrick; Vandenberghe, Rik

2017-01-01

According to a recent study, semantic similarity between concrete entities correlates with the similarity of activity patterns in left middle IPS during category naming. We examined the replicability of this effect under passive viewing conditions, the potential role of visuoperceptual similarity, where the effect is situated compared to regions that have been previously implicated in visuospatial attention, and how it compares to effects of object identity and location. Forty-six subjects participated. Subjects passively viewed pictures from two categories, musical instruments and vehicles. Semantic similarity between entities was estimated based on a concept-feature matrix obtained in more than 1,000 subjects. Visuoperceptual similarity was modeled based on the HMAX model, the AlexNet deep convolutional learning model, and thirdly, based on subjective visuoperceptual similarity ratings. Among the IPS regions examined, only left middle IPS showed a semantic similarity effect. The effect was significant in hIP1, hIP2, and hIP3. Visuoperceptual similarity did not correlate with similarity of activity patterns in left middle IPS. The semantic similarity effect in left middle IPS was significantly stronger than in the right middle IPS and also stronger than in the left or right posterior IPS. The semantic similarity effect was similar to that seen in the angular gyrus. Object identity effects were much more widespread across nearly all parietal areas examined. Location effects were relatively specific for posterior IPS and area 7 bilaterally. To conclude, the current findings replicate the semantic similarity effect in left middle IPS under passive viewing conditions, and demonstrate its anatomical specificity within a cytoarchitectonic reference frame. We propose that the semantic similarity effect in left middle IPS reflects the transient uploading of semantic representations in working memory. PMID:28824405

The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

PubMed Central

2014-01-01

The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org. PMID:24602174

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration

PubMed Central

2011-01-01

Background Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information. Results Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase. Conclusions By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research. PMID:21595881

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration.

PubMed

Chepelev, Leonid L; Dumontier, Michel

2011-05-19

Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information. Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase. By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research.

Using RDF to Model the Structure and Process of Systems

NASA Astrophysics Data System (ADS)

Rodriguez, Marko A.; Watkins, Jennifer H.; Bollen, Johan; Gershenson, Carlos

Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of 109 edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.

Mining semantic networks of bioinformatics e-resources from the literature

PubMed Central

2011-01-01

Background There have been a number of recent efforts (e.g. BioCatalogue, BioMoby) to systematically catalogue bioinformatics tools, services and datasets. These efforts rely on manual curation, making it difficult to cope with the huge influx of various electronic resources that have been provided by the bioinformatics community. We present a text mining approach that utilises the literature to automatically extract descriptions and semantically profile bioinformatics resources to make them available for resource discovery and exploration through semantic networks that contain related resources. Results The method identifies the mentions of resources in the literature and assigns a set of co-occurring terminological entities (descriptors) to represent them. We have processed 2,691 full-text bioinformatics articles and extracted profiles of 12,452 resources containing associated descriptors with binary and tf*idf weights. Since such representations are typically sparse (on average 13.77 features per resource), we used lexical kernel metrics to identify semantically related resources via descriptor smoothing. Resources are then clustered or linked into semantic networks, providing the users (bioinformaticians, curators and service/tool crawlers) with a possibility to explore algorithms, tools, services and datasets based on their relatedness. Manual exploration of links between a set of 18 well-known bioinformatics resources suggests that the method was able to identify and group semantically related entities. Conclusions The results have shown that the method can reconstruct interesting functional links between resources (e.g. linking data types and algorithms), in particular when tf*idf-like weights are used for profiling. This demonstrates the potential of combining literature mining and simple lexical kernel methods to model relatedness between resource descriptors in particular when there are few features, thus potentially improving the resource description, discovery and exploration process. The resource profiles are available at http://gnode1.mib.man.ac.uk/bioinf/semnets.html PMID:21388573

Semantic Differential Scale Method Can Reveal Multi-Dimensional Aspects of Mind Perception.

PubMed

Takahashi, Hideyuki; Ban, Midori; Asada, Minoru

2016-01-01

As humans, we tend to perceive minds in both living and non-living entities, such as robots. From a questionnaire developed in a previous mind perception study, authors found that perceived minds could be located on two dimensions "experience" and "agency." This questionnaire allowed the assessment of how we perceive minds of various entities from a multi-dimensional point of view. In this questionnaire, subjects had to evaluate explicit mental capacities of target characters (e.g., capacity to feel hunger). However, we sometimes perceive minds in non-living entities, even though we cannot attribute these evidently biological capacities to the entity. In this study, we performed a large-scale web survey to assess mind perception by using the semantic differential scale method. We revealed that two mind dimensions "emotion" and "intelligence," respectively, corresponded to the two mind dimensions (experience and agency) proposed in a previous mind perception study. We did this without having to ask about specific mental capacities. We believe that the semantic differential scale is a useful method to assess the dimensions of mind perception especially for non-living entities that are hard to be attributed to biological capacities.

Linking Disparate Datasets of the Earth Sciences with the SemantEco Annotator

NASA Astrophysics Data System (ADS)

Seyed, P.; Chastain, K.; McGuinness, D. L.

2013-12-01

Use of Semantic Web technologies for data management in the Earth sciences (and beyond) has great potential but is still in its early stages, since the challenges of translating data into a more explicit or semantic form for immediate use within applications has not been fully addressed. In this abstract we help address this challenge by introducing the SemantEco Annotator, which enables anyone, regardless of expertise, to semantically annotate tabular Earth Science data and translate it into linked data format, while applying the logic inherent in community-standard vocabularies to guide the process. The Annotator was conceived under a desire to unify dataset content from a variety of sources under common vocabularies, for use in semantically-enabled web applications. Our current use case employs linked data generated by the Annotator for use in the SemantEco environment, which utilizes semantics to help users explore, search, and visualize water or air quality measurement and species occurrence data through a map-based interface. The generated data can also be used immediately to facilitate discovery and search capabilities within 'big data' environments. The Annotator provides a method for taking information about a dataset, that may only be known to its maintainers, and making it explicit, in a uniform and machine-readable fashion, such that a person or information system can more easily interpret the underlying structure and meaning. Its primary mechanism is to enable a user to formally describe how columns of a tabular dataset relate and/or describe entities. For example, if a user identifies columns for latitude and longitude coordinates, we can infer the data refers to a point that can be plotted on a map. Further, it can be made explicit that measurements of 'nitrate' and 'NO3-' are of the same entity through vocabulary assignments, thus more easily utilizing data sets that use different nomenclatures. The Annotator provides an extensive and searchable library of vocabularies to assist the user in locating terms to describe observed entities, their properties, and relationships. The Annotator leverages vocabulary definitions of these concepts to guide the user in describing data in a logically consistent manner. The vocabularies made available through the Annotator are open, as is the Annotator itself. We have taken a step towards making semantic annotation/translation of data more accessible. Our vision for the Annotator is as a tool that can be integrated into a semantic data 'workbench' environment, which would allow semantic annotation of a variety of data formats, using standard vocabularies. These vocabularies involved enable search for similar datasets, and integration with any semantically-enabled applications for analysis and visualization.

Discovering gene annotations in biomedical text databases

PubMed Central

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-01-01

Background Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values. PMID:18325104

Discovering gene annotations in biomedical text databases.

PubMed

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-03-06

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

PubMed

Urbain, Jay

2015-12-01

We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results. Copyright © 2015 Elsevier Inc. All rights reserved.

The left temporal pole is a heteromodal hub for retrieving proper names

PubMed Central

Waldron, Eric J.; Manzel, Kenneth; Tranel, Daniel

2015-01-01

The left temporal pole (LTP) has been posited to be a heteromodal hub for retrieving proper names for semantically unique entities. Previous investigations have demonstrated that LTP is important for retrieving names for famous faces and unique landmarks. However, whether such a relationship would hold for unique entities apprehended through stimulus modalities other than vision has not been well established, and such evidence is critical to adjudicate claims about the “heteromodal” nature of the LTP. Here, we tested the hypothesis that the LTP would be important for naming famous voices. Individuals with LTP lesions were asked to recognize and name famous persons speaking in audio clips. Relative to neurologically normal and brain damaged comparison participants, patients with LTP lesions were able to recognize famous persons from their voices normally, but were selectively impaired in naming famous persons from their voices. The current results extend previous research and provide further support for the notion that the LTP is a convergence region serving as a heteromodal hub for retrieving the names of semantically unique entities. PMID:24389260

PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media

PubMed Central

Cameron, Delroy; Smith, Gary A.; Daniulaityte, Raminta; Sheth, Amit P.; Dave, Drashti; Chen, Lu; Anand, Gaurish; Carlson, Robert; Watkins, Kera Z.; Falck, Russel

2013-01-01

Objectives The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel Semantic Web platform called PREDOSE (PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO) (pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC). A combination of lexical, pattern-based and semantics-based techniques is used together with the domain knowledge to extract fine-grained semantic information from UGC. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks. Methods Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, routes of administration, etc. The DAO is also used to help recognize three types of data, namely: 1) entities, 2) relationships and 3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information from UGC, and querying, search, trend analysis and overall content analysis of social media related to prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques. Results A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University. Conclusion A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future. PMID:23892295

Neuroscientific Insights into the Development of Analogical Reasoning

ERIC Educational Resources Information Center

Whitaker, Kirstie J.; Vendetti, Michael S.; Wendelken, Carter; Bunge, Silvia A.

2018-01-01

Analogical reasoning, or the ability to find correspondences between entities based on shared relationships, supports knowledge acquisition. As such, the development of this ability during childhood is thought to promote learning. Here, we sought to better understand the mechanisms by which analogical reasoning about semantic relations improves…

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

PubMed

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A

2017-03-01

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems

PubMed Central

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A.

2017-01-01

Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. PMID:28328252

Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection

PubMed Central

Lossio-Ventura, Juan Antonio; Hogan, William; Modave, François; Hicks, Amanda; Hanna, Josh; Guo, Yi; He, Zhe; Bian, Jiang

2017-01-01

Obesity is associated with increased risks of various types of cancer, as well as a wide range of other chronic diseases. On the other hand, access to health information activates patient participation, and improve their health outcomes. However, existing online information on obesity and its relationship to cancer is heterogeneous ranging from pre-clinical models and case studies to mere hypothesis-based scientific arguments. A formal knowledge representation (i.e., a semantic knowledge base) would help better organizing and delivering quality health information related to obesity and cancer that consumers need. Nevertheless, current ontologies describing obesity, cancer and related entities are not designed to guide automatic knowledge base construction from heterogeneous information sources. Thus, in this paper, we present methods for named-entity recognition (NER) to extract biomedical entities from scholarly articles and for detecting if two biomedical entities are related, with the long term goal of building a obesity-cancer knowledge base. We leverage both linguistic and statistical approaches in the NER task, which supersedes the state-of-the-art results. Further, based on statistical features extracted from the sentences, our method for relation detection obtains an accuracy of 99.3% and a f-measure of 0.993. PMID:28503356

SATware: A Semantic Approach for Building Sentient Spaces

NASA Astrophysics Data System (ADS)

Massaguer, Daniel; Mehrotra, Sharad; Vaisenberg, Ronen; Venkatasubramanian, Nalini

This chapter describes the architecture of a semantic-based middleware environment for building sensor-driven sentient spaces. The proposed middleware explicitly models sentient space semantics (i.e., entities, spaces, activities) and supports mechanisms to map sensor observations to the state of the sentient space. We argue how such a semantic approach provides a powerful programming environment for building sensor spaces. In addition, the approach provides natural ways to exploit semantics for variety of purposes including scheduling under resource constraints and sensor recalibration.

Spatial Databases

DTIC Science & Technology

2007-09-19

extended object relations such as boundary, interior, open, closed , within, connected, and overlaps, which are invariant under elastic deformation...is required in a geo-spatial semantic web is challenging because the defining properties of geographic entities are very closely related to space. In...Objects under Primitive will be open (i.e., they will not contain their boundary points) and the objects under Complex will be closed . In addition to

SPECTRa-T: machine-based data extraction and semantic searching of chemistry e-theses.

PubMed

Downing, Jim; Harvey, Matt J; Morgan, Peter B; Murray-Rust, Peter; Rzepa, Henry S; Stewart, Diana C; Tonge, Alan P; Townsend, Joe A

2010-02-22

The SPECTRa-T project has developed text-mining tools to extract named chemical entities (NCEs), such as chemical names and terms, and chemical objects (COs), e.g., experimental spectral assignments and physical chemistry properties, from electronic theses (e-theses). Although NCEs were readily identified within the two major document formats studied, only the use of structured documents enabled identification of chemical objects and their association with the relevant chemical entity (e.g., systematic chemical name). A corpus of theses was analyzed and it is shown that a high degree of semantic information can be extracted from structured documents. This integrated information has been deposited in a persistent Resource Description Framework (RDF) triple-store that allows users to conduct semantic searches. The strength and weaknesses of several document formats are reviewed.

PREDOSE: a semantic web platform for drug abuse epidemiology using social media.

PubMed

Cameron, Delroy; Smith, Gary A; Daniulaityte, Raminta; Sheth, Amit P; Dave, Drashti; Chen, Lu; Anand, Gaurish; Carlson, Robert; Watkins, Kera Z; Falck, Russel

2013-12-01

The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel semantic web platform called PREDOSE (PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO--pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC), through combination of lexical, pattern-based and semantics-based techniques. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks. Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, and routes of administration. The DAO is also used to help recognize three types of data, namely: (1) entities, (2) relationships and (3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information, which facilitate search, trend analysis and overall content analysis using social media on prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques. A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University. A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future. Copyright © 2013 Elsevier Inc. All rights reserved.

Semantic annotation in biomedicine: the current landscape.

PubMed

Jovanović, Jelena; Bagheri, Ebrahim

2017-09-22

The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.

Semantic Entity-Component State Management Techniques to Enhance Software Quality for Multimodal VR-Systems.

PubMed

Fischbach, Martin; Wiebusch, Dennis; Latoschik, Marc Erich

2017-04-01

Modularity, modifiability, reusability, and API usability are important software qualities that determine the maintainability of software architectures. Virtual, Augmented, and Mixed Reality (VR, AR, MR) systems, modern computer games, as well as interactive human-robot systems often include various dedicated input-, output-, and processing subsystems. These subsystems collectively maintain a real-time simulation of a coherent application state. The resulting interdependencies between individual state representations, mutual state access, overall synchronization, and flow of control implies a conceptual close coupling whereas software quality asks for a decoupling to develop maintainable solutions. This article presents five semantics-based software techniques that address this contradiction: Semantic grounding, code from semantics, grounded actions, semantic queries, and decoupling by semantics. These techniques are applied to extend the well-established entity-component-system (ECS) pattern to overcome some of this pattern's deficits with respect to the implied state access. A walk-through of central implementation aspects of a multimodal (speech and gesture) VR-interface is used to highlight the techniques' benefits. This use-case is chosen as a prototypical example of complex architectures with multiple interacting subsystems found in many VR, AR and MR architectures. Finally, implementation hints are given, lessons learned regarding maintainability pointed-out, and performance implications discussed.

Cheminformatics and the Semantic Web: adding value with linked data and enhanced provenance

PubMed Central

Frey, Jeremy G; Bird, Colin L

2013-01-01

Cheminformatics is evolving from being a field of study associated primarily with drug discovery into a discipline that embraces the distribution, management, access, and sharing of chemical data. The relationship with the related subject of bioinformatics is becoming stronger and better defined, owing to the influence of Semantic Web technologies, which enable researchers to integrate heterogeneous sources of chemical, biochemical, biological, and medical information. These developments depend on a range of factors: the principles of chemical identifiers and their role in relationships between chemical and biological entities; the importance of preserving provenance and properly curated metadata; and an understanding of the contribution that the Semantic Web can make at all stages of the research lifecycle. The movements toward open access, open source, and open collaboration all contribute to progress toward the goals of integration. PMID:24432050

Semantic characteristics of NLP-extracted concepts in clinical notes vs. biomedical literature.

PubMed

Wu, Stephen; Liu, Hongfang

2011-01-01

Natural language processing (NLP) has become crucial in unlocking information stored in free text, from both clinical notes and biomedical literature. Clinical notes convey clinical information related to individual patient health care, while biomedical literature communicates scientific findings. This work focuses on semantic characterization of texts at an enterprise scale, comparing and contrasting the two domains and their NLP approaches. We analyzed the empirical distributional characteristics of NLP-discovered named entities in Mayo Clinic clinical notes from 2001-2010, and in the 2011 MetaMapped Medline Baseline. We give qualitative and quantitative measures of domain similarity and point to the feasibility of transferring resources and techniques. An important by-product for this study is the development of a weighted ontology for each domain, which gives distributional semantic information that may be used to improve NLP applications.

Personal semantics: at the crossroads of semantic and episodic memory.

PubMed

Renoult, Louis; Davidson, Patrick S R; Palombo, Daniela J; Moscovitch, Morris; Levine, Brian

2012-11-01

Declarative memory is usually described as consisting of two systems: semantic and episodic memory. Between these two poles, however, may lie a third entity: personal semantics (PS). PS concerns knowledge of one's past. Although typically assumed to be an aspect of semantic memory, it is essentially absent from existing models of knowledge. Furthermore, like episodic memory (EM), PS is idiosyncratically personal (i.e., not culturally-shared). We show that, depending on how it is operationalized, the neural correlates of PS can look more similar to semantic memory, more similar to EM, or dissimilar to both. We consider three different perspectives to better integrate PS into existing models of declarative memory and suggest experimental strategies for disentangling PS from semantic and episodic memory. Copyright © 2012 Elsevier Ltd. All rights reserved.

Abstracts versus Full Texts and Patents: A Quantitative Analysis of Biomedical Entities

NASA Astrophysics Data System (ADS)

Müller, Bernd; Klinger, Roman; Gurulingappa, Harsha; Mevissen, Heinz-Theodor; Hofmann-Apitius, Martin; Fluck, Juliane; Friedrich, Christoph M.

In information retrieval, named entity recognition gives the opportunity to apply semantic search in domain specific corpora. Recently, more full text patents and journal articles became freely available. As the information distribution amongst the different sections is unknown, an analysis of the diversity is of interest.

Information Management for Unmanned Systems: Combining DL-Reasoning with Publish/Subscribe

NASA Astrophysics Data System (ADS)

Moser, Herwig; Reichelt, Toni; Oswald, Norbert; Förster, Stefan

Sharing capabilities and information between collaborating entities by using modem information- and communication-technology is a core principle in complex distributed civil or military mission scenarios. Previous work proved the suitability of Service-oriented Architectures for modelling and sharing the participating entities' capabilities. Albeit providing a satisfactory model for capabilities sharing, pure service-orientation curtails expressiveness for information exchange as opposed to dedicated data-centric communication principles. In this paper we introduce an Information Management System which combines OWL-Ontologies and automated reasoning with Publish/Subscribe-Systems, providing for a shared but decoupled data model. While confirming existing related research results, we emphasise the novel application and lack of practical experience of using Semantic Web technologies in areas other than originally intended. That is, aiding decision support and software design in the context of a mission scenario for an unmanned system. Experiments within a complex simulation environment show the immediate benefits of a semantic information-management and -dissemination platform: Clear separation of concerns in code and data model, increased service re-usability and extensibility as well as regulation of data flow and respective system behaviour through declarative rules.

Distributed semantic networks and CLIPS

NASA Technical Reports Server (NTRS)

Snyder, James; Rodriguez, Tony

1991-01-01

Semantic networks of frames are commonly used as a method of reasoning in many problems. In most of these applications the semantic network exists as a single entity in a single process environment. Advances in workstation hardware provide support for more sophisticated applications involving multiple processes, interacting in a distributed environment. In these applications the semantic network may well be distributed over several concurrently executing tasks. This paper describes the design and implementation of a frame based, distributed semantic network in which frames are accessed both through C Language Integrated Production System (CLIPS) expert systems and procedural C++ language programs. The application area is a knowledge based, cooperative decision making model utilizing both rule based and procedural experts.

Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.

PubMed

He, Bin; Dong, Bin; Guan, Yi; Yang, Jinfeng; Jiang, Zhipeng; Yu, Qiubin; Cheng, Jianyi; Qu, Chunyan

2017-05-01

To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain. An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation quality assurance measures, a comprehensive corpus was built, containing annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions, and relations. Inter-annotator agreement (IAA) was calculated to evaluate the annotation quality and a Chinese clinical text processing and information extraction system (CCTPIES) was developed based on our annotated corpus. The syntactic corpus consists of 138 Chinese clinical documents with 47,426 tokens and 2612 full parsing trees, while the semantic corpus includes 992 documents that annotated 39,511 entities with their assertions and 7693 relations. IAA evaluation shows that this comprehensive corpus is of good quality, and the system modules are effective. The annotated corpus makes a considerable contribution to natural language processing (NLP) research into Chinese texts in the clinical domain. However, this corpus has a number of limitations. Some additional types of clinical text should be introduced to improve corpus coverage and active learning methods should be utilized to promote annotation efficiency. In this study, several annotation guidelines and an annotation method for Chinese clinical texts were proposed, and a comprehensive corpus with its NLP modules were constructed, providing a foundation for further study of applying NLP techniques to Chinese texts in the clinical domain. Copyright © 2017. Published by Elsevier Inc.

The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation

DOE PAGES

Buttigieg, Pier Luigi; Pafilis, Evangelos; Lewis, Suzanna E.; ...

2016-09-23

Background: The Environment Ontology (ENVO; http://www.environmentontology.org/), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications. Methods: We have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVOmore » in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO. Results: Relative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl. Conclusions: ENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, 'omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO's growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.« less

The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buttigieg, Pier Luigi; Pafilis, Evangelos; Lewis, Suzanna E.

Background: The Environment Ontology (ENVO; http://www.environmentontology.org/), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications. Methods: We have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVOmore » in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO. Results: Relative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl. Conclusions: ENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, 'omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO's growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.« less

The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation.

PubMed

Buttigieg, Pier Luigi; Pafilis, Evangelos; Lewis, Suzanna E; Schildhauer, Mark P; Walls, Ramona L; Mungall, Christopher J

2016-09-23

The Environment Ontology (ENVO; http://www.environmentontology.org/ ), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications. We have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVO in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO. Relative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl . ENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, 'omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO's growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.

Semantic Similarity in Biomedical Ontologies

PubMed Central

Pesquita, Catia; Faria, Daniel; Falcão, André O.; Lord, Phillip; Couto, Francisco M.

2009-01-01

In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies. Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research. PMID:19649320

Ontological Standardization for Historical Map Collections: Studying the Greek Borderlines of 1881

NASA Astrophysics Data System (ADS)

Gkadolou, E.; Tomai, E.; Stefanakis, E.; Kritikos, G.

2012-07-01

Historical maps deliver valuable historical information which is applicable in several domains while they document the spatiotemporal evolution of the geographical entities that are depicted therein. In order to use the historical cartographic information effectively, the maps' semantic documentation becomes a necessity for restoring any semantic ambiguities and structuring the relationship between historical and current geographical space. This paper examines cartographic ontologies as a proposed methodology and presents the first outcomes of the methodology applied for the historical map series «Carte de la nouvelle frontière Turco-Grecque» that sets the borderlines between Greece and Ottoman Empire in 1881. The map entities were modelled and compared to the current ones so as to record the changes in their spatial and thematic attributes and an ontology was developed in Protégé OWL Editor 3.4.4 for the attributes that thoroughly define a historical map and the digitised spatial entities. Special focus was given on the Greek borderline and the changes that it caused to other geographic entities.

Supporting open collaboration in science through explicit and linked semantic description of processes

USGS Publications Warehouse

Gil, Yolanda; Michel, Felix; Ratnakar, Varun; Read, Jordan S.; Hauder, Matheus; Duffy, Christopher; Hanson, Paul C.; Dugan, Hilary

2015-01-01

The Web was originally developed to support collaboration in science. Although scientists benefit from many forms of collaboration on the Web (e.g., blogs, wikis, forums, code sharing, etc.), most collaborative projects are coordinated over email, phone calls, and in-person meetings. Our goal is to develop a collaborative infrastructure for scientists to work on complex science questions that require multi-disciplinary contributions to gather and analyze data, that cannot occur without significant coordination to synthesize findings, and that grow organically to accommodate new contributors as needed as the work evolves over time. Our approach is to develop an organic data science framework based on a task-centered organization of the collaboration, includes principles from social sciences for successful on-line communities, and exposes an open science process. Our approach is implemented as an extension of a semantic wiki platform, and captures formal representations of task decomposition structures, relations between tasks and users, and other properties of tasks, data, and other relevant science objects. All these entities are captured through the semantic wiki user interface, represented as semantic web objects, and exported as linked data.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

Object-Oriented Approach to Integrating Database Semantics. Volume 4.

DTIC Science & Technology

1987-12-01

schemata for; 1. Object Classification Shema -- Entities 2. Object Structure and Relationship Schema -- Relations 3. Operation Classification and... relationships are represented in a database is non- intuitive for naive users. *It is difficult to access and combine information in multiple databases. In this...from the CURRENT-.CLASSES table. Choosing a selected item do-selects it. Choose 0 to exit. 1. STUDENTS 2. CUR~RENT-..CLASSES 3. MANAGMNT -.CLASS

Automatic information extraction from unstructured mammography reports using distributed semantics.

PubMed

Gupta, Anupama; Banerjee, Imon; Rubin, Daniel L

2018-02-01

To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F 1 -score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules. Copyright © 2018 Elsevier Inc. All rights reserved.

A Concept Hierarchy Based Ontology Mapping Approach

NASA Astrophysics Data System (ADS)

Wang, Ying; Liu, Weiru; Bell, David

Ontology mapping is one of the most important tasks for ontology interoperability and its main aim is to find semantic relationships between entities (i.e. concept, attribute, and relation) of two ontologies. However, most of the current methods only consider one to one (1:1) mappings. In this paper we propose a new approach (CHM: Concept Hierarchy based Mapping approach) which can find simple (1:1) mappings and complex (m:1 or 1:m) mappings simultaneously. First, we propose a new method to represent the concept names of entities. This method is based on the hierarchical structure of an ontology such that each concept name of entity in the ontology is included in a set. The parent-child relationship in the hierarchical structure of an ontology is then extended as a set-inclusion relationship between the sets for the parent and the child. Second, we compute the similarities between entities based on the new representation of entities in ontologies. Third, after generating the mapping candidates, we select the best mapping result for each source entity. We design a new algorithm based on the Apriori algorithm for selecting the mapping results. Finally, we obtain simple (1:1) and complex (m:1 or 1:m) mappings. Our experimental results and comparisons with related work indicate that utilizing this method in dealing with ontology mapping is a promising way to improve the overall mapping results.

Model-based semantic dictionaries for medical language understanding.

PubMed Central

Rassinoux, A. M.; Baud, R. H.; Ruch, P.; Trombert-Paviot, B.; Rodrigues, J. M.

1999-01-01

Semantic dictionaries are emerging as a major cornerstone towards achieving sound natural language understanding. Indeed, they constitute the main bridge between words and conceptual entities that reflect their meanings. Nowadays, more and more wide-coverage lexical dictionaries are electronically available in the public domain. However, associating a semantic content with lexical entries is not a straightforward task as it is subordinate to the existence of a fine-grained concept model of the treated domain. This paper presents the benefits and pitfalls in building and maintaining multilingual dictionaries, the semantics of which is directly established on an existing concept model. Concrete cases, handled through the GALEN-IN-USE project, illustrate the use of such semantic dictionaries for the analysis and generation of multilingual surgical procedures. PMID:10566333

The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies.

PubMed

Harispe, Sébastien; Ranwez, Sylvie; Janaqi, Stefan; Montmain, Jacky

2014-03-01

The semantic measures library and toolkit are robust open-source and easy to use software solutions dedicated to semantic measures. They can be used for large-scale computations and analyses of semantic similarities between terms/concepts defined in terminologies and ontologies. The comparison of entities (e.g. genes) annotated by concepts is also supported. A large collection of measures is available. Not limited to a specific application context, the library and the toolkit can be used with various controlled vocabularies and ontology specifications (e.g. Open Biomedical Ontology, Resource Description Framework). The project targets both designers and practitioners of semantic measures providing a JAVA library, as well as a command-line tool that can be used on personal computers or computer clusters. Downloads, documentation, tutorials, evaluation and support are available at http://www.semantic-measures-library.org.

Impact of Machine-Translated Text on Entity and Relationship Extraction

DTIC Science & Technology

2014-12-01

20 1 1. Introduction Using social network analysis tools is an important asset in...semantic modeling software to automatically build detailed network models from unstructured text. Contour imports unstructured text and then maps the text...onto an existing ontology of frames at the sentence level, using FrameNet, a structured language model, and through Semantic Role Labeling ( SRL

The shared neural basis of music and language.

PubMed

Yu, Mengxia; Xu, Miao; Li, Xueting; Chen, Zhencai; Song, Yiying; Liu, Jia

2017-08-15

Human musical ability is proposed to play a key phylogenetical role in the evolution of language, and the similarity of hierarchical structure in music and language has led to considerable speculation about their shared mechanisms. While behavioral and electrophysioglocial studies have revealed associations between music and linguistic abilities, results from functional magnetic resonance imaging (fMRI) studies on their relations are contradictory, possibly because these studies usually treat music or language as single entities without breaking down to their components. Here, we examined the relations between different components of music (i.e., melodic and rhythmic analysis) and language (i.e., semantic and phonological processing) using both behavioral tests and resting-state fMRI. Behaviorally, we found that individuals with music training experiences were better at semantic processing, but not at phonological processing, than those without training. Further correlation analyses showed that semantic processing of language was related to melodic, but not rhythmic, analysis of music. Neurally, we found that performances in both semantic processing and melodic analysis were correlated with spontaneous brain activities in the bilateral precentral gyrus (PCG) and superior temporal plane at the regional level, and with the resting-state functional connectivity of the left PCG with the left supramarginal gyrus and left superior temporal gyrus at the network level. Together, our study revealed the shared spontaneous neural basis of music and language based on the behavioral link between melodic analysis and semantic processing, which possibly relied on a common mechanism of automatic auditory-motor integration. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Segregation of anterior temporal regions critical for retrieving names of unique and nonunique entities reflects underlying long-range connectivity

PubMed Central

Mehta, Sonya; Inoue, Kayo; Rudrauf, David; Damasio, Hanna; Tranel, Daniel; Grabowski, Thomas

2015-01-01

Lesion-deficit studies support the hypothesis that the left anterior temporal lobe (ATL) plays a critical role in retrieving names of concrete entities. They further suggest that different regions of the left ATL process different conceptual categories. Here we test the specificity of these relationships and whether the anatomical segregation is related to the underlying organization of white matter connections. We reanalyzed data from a previous lesion study of naming and recognition across five categories of concrete entities. In voxelwise logistic regressions of lesion-deficit associations, we formally incorporated measures of disconnection of long-range association fiber tracts (FTs) and covaried for recognition and non-category specific naming deficits. We also performed fiber tractwise analyses to assess whether damage to specific FTs was preferentially associated with category-selective naming deficits. Damage to the basolateral ATL was associated with naming deficits for both unique (famous faces) and non-unique entities, whereas the damage to the temporal pole was associated with naming deficits for unique entities only. This segregation pattern remained after accounting for comorbid recognition deficits or naming deficits in other categories. The tractwise analyses showed that damage to the uncinate fasciculus was associated with naming impairments for unique entities, while damage to the inferior longitudinal fasciculus was associated with naming impairments for non-unique entities. Covarying for FT transection in voxelwise analyses rendered the cortical association for unique entities more focal. These results are consistent with the partial segregation of brain system support for name retrieval of unique and non-unique entities at both the level of cortical components and underlying white matter fiber bundles. Our study reconciles theoretic accounts of the functional organization of the left ATL by revealing both category-related processing and semantic hub sectors. PMID:26707082

Semantic Document Library: A Virtual Research Environment for Documents, Data and Workflows Sharing

NASA Astrophysics Data System (ADS)

Kotwani, K.; Liu, Y.; Myers, J.; Futrelle, J.

2008-12-01

The Semantic Document Library (SDL) was driven by use cases from the environmental observatory communities and is designed to provide conventional document repository features of uploading, downloading, editing and versioning of documents as well as value adding features of tagging, querying, sharing, annotating, ranking, provenance, social networking and geo-spatial mapping services. It allows users to organize a catalogue of watershed observation data, model output, workflows, as well publications and documents related to the same watershed study through the tagging capability. Users can tag all relevant materials using the same watershed name and find all of them easily later using this tag. The underpinning semantic content repository can store materials from other cyberenvironments such as workflow or simulation tools and SDL provides an effective interface to query and organize materials from various sources. Advanced features of the SDL allow users to visualize the provenance of the materials such as the source and how the output data is derived. Other novel features include visualizing all geo-referenced materials on a geospatial map. SDL as a component of a cyberenvironment portal (the NCSA Cybercollaboratory) has goal of efficient management of information and relationships between published artifacts (Validated models, vetted data, workflows, annotations, best practices, reviews and papers) produced from raw research artifacts (data, notes, plans etc.) through agents (people, sensors etc.). Tremendous scientific potential of artifacts is achieved through mechanisms of sharing, reuse and collaboration - empowering scientists to spread their knowledge and protocols and to benefit from the knowledge of others. SDL successfully implements web 2.0 technologies and design patterns along with semantic content management approach that enables use of multiple ontologies and dynamic evolution (e.g. folksonomies) of terminology. Scientific documents involved with many interconnected entities (artifacts or agents) are represented as RDF triples using semantic content repository middleware Tupelo in one or many data/metadata RDF stores. Queries to the RDF enables discovery of relations among data, process and people, digging out valuable aspects, making recommendations to users, such as what tools are typically used to answer certain kinds of questions or with certain types of dataset. This innovative concept brings out coherent information about entities from four different perspectives of the social context (Who-human relations and interactions), the casual context (Why - provenance and history), the geo-spatial context (Where - location or spatially referenced information) and the conceptual context (What - domain specific relations, ontologies etc.).

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System.

PubMed

Wu, Zhenyu; Xu, Yuan; Yang, Yunong; Zhang, Chunhong; Zhu, Xinning; Ji, Yang

2017-02-20

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.

Cross-modal representation of spoken and written word meaning in left pars triangularis.

PubMed

Liuzzi, Antonietta Gabriella; Bruffaerts, Rose; Peeters, Ronald; Adamczuk, Katarzyna; Keuleers, Emmanuel; De Deyne, Simon; Storms, Gerrit; Dupont, Patrick; Vandenberghe, Rik

2017-04-15

The correspondence in meaning extracted from written versus spoken input remains to be fully understood neurobiologically. Here, in a total of 38 subjects, the functional anatomy of cross-modal semantic similarity for concrete words was determined based on a dual criterion: First, a voxelwise univariate analysis had to show significant activation during a semantic task (property verification) performed with written and spoken concrete words compared to the perceptually matched control condition. Second, in an independent dataset, in these clusters, the similarity in fMRI response pattern to two distinct entities, one presented as a written and the other as a spoken word, had to correlate with the similarity in meaning between these entities. The left ventral occipitotemporal transition zone and ventromedial temporal cortex, retrosplenial cortex, pars orbitalis bilaterally, and the left pars triangularis were all activated in the univariate contrast. Only the left pars triangularis showed a cross-modal semantic similarity effect. There was no effect of phonological nor orthographic similarity in this region. The cross-modal semantic similarity effect was confirmed by a secondary analysis in the cytoarchitectonically defined BA45. A semantic similarity effect was also present in the ventral occipital regions but only within the visual modality, and in the anterior superior temporal cortex only within the auditory modality. This study provides direct evidence for the coding of word meaning in BA45 and positions its contribution to semantic processing at the confluence of input-modality specific pathways that code for meaning within the respective input modalities. Copyright © 2017 Elsevier Inc. All rights reserved.

Determining similarity of scientific entities in annotation datasets

PubMed Central

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug–drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called ‘AnnSim’ that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1–1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ PMID:25725057

Determining similarity of scientific entities in annotation datasets.

PubMed

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug-drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called 'AnnSim' that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1-1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ © The Author(s) 2015. Published by Oxford University Press.

Towards comprehensive syntactic and semantic annotations of the clinical narrative

PubMed Central

Albright, Daniel; Lanfranchi, Arrick; Fredriksen, Anwen; Styler, William F; Warner, Colin; Hwang, Jena D; Choi, Jinho D; Dligach, Dmitriy; Nielsen, Rodney D; Martin, James; Ward, Wayne; Palmer, Martha; Savova, Guergana K

2013-01-01

Objective To create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components. Methods Manual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for predicate-argument structures, and the Unified Medical Language System (UMLS) schema for semantic information. NLP components were developed. Results The final corpus consists of 13 091 sentences containing 1772 distinct predicate lemmas. Of the 766 newly created PropBank frames, 74 are verbs. There are 28 539 named entity (NE) annotations spread over 15 UMLS semantic groups, one UMLS semantic type, and the Person semantic category. The most frequent annotations belong to the UMLS semantic groups of Procedures (15.71%), Disorders (14.74%), Concepts and Ideas (15.10%), Anatomy (12.80%), Chemicals and Drugs (7.49%), and the UMLS semantic type of Sign or Symptom (12.46%). Inter-annotator agreement results: Treebank (0.926), PropBank (0.891–0.931), NE (0.697–0.750). The part-of-speech tagger, constituency parser, dependency parser, and semantic role labeler are built from the corpus and released open source. A significant limitation uncovered by this project is the need for the NLP community to develop a widely agreed-upon schema for the annotation of clinical concepts and their relations. Conclusions This project takes a foundational step towards bringing the field of clinical NLP up to par with NLP in the general domain. The corpus creation and NLP components provide a resource for research and application development that would have been previously impossible. PMID:23355458

Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature.

PubMed

García-Remesal, Miguel; García-Ruiz, Alejandro; Pérez-Rey, David; de la Iglesia, Diana; Maojo, Víctor

2013-01-01

Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a "proof of concept" that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine.

Design of an ontology for medical image manipulation: an example applied for DICOM extensions

NASA Astrophysics Data System (ADS)

Aubry, Florent; Chameroy, Virginie; Todd-Pokropek, Andrew; Di Paola, Robert

1999-07-01

Currently, various data formats are widely used for medical imags, e.g. DICOM for exchange through network and storage media, and INTERFILE for image exchange in nuclear medicine. These formats are only able partly to solve problems arising in accessing and handling imags. To solve such problems, an ontology dedicated to the description of data and knowledge involved in the handling and the management of medical images has been designed. The ontology offers a semantic frame of reference to which manipulation tools can refer. It considers various point of view on the data, related to the context of production, the content,and the data quality. It supports several levels of abstraction, going from a declarative level related to the examination type to the implementation level. Moreover, the ontology provides mechanisms allowing the creation and the description of new entities. It can, thus, act as an intermediate language ensuring accurate reuse of the entities. This paper, which presents work in progress, is focused on the description of the ontology and points out how to use it for the description of and the access to DICOM or INTERFILE entities, and for the extension of the DICOM or INTERFILE dictionaries, by adding new entities, in order to describe complex relationships between images.

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System

PubMed Central

Wu, Zhenyu; Xu, Yuan; Yang, Yunong; Zhang, Chunhong; Zhu, Xinning; Ji, Yang

2017-01-01

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed. PMID:28230725

Emerald: an object-based language for distributed programming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hutchinson, N.C.

1987-01-01

Distributed systems have become more common, however constructing distributed applications remains a very difficult task. Numerous operating systems and programming languages have been proposed that attempt to simplify the programming of distributed applications. Here a programing language called Emerald is presented that simplifies distributed programming by extending the concepts of object-based languages to the distributed environment. Emerald supports a single model of computation: the object. Emerald objects include private entities such as integers and Booleans, as well as shared, distributed entities such as compilers, directories, and entire file systems. Emerald objects may move between machines in the system, but objectmore » invocation is location independent. The uniform semantic model used for describing all Emerald objects makes the construction of distributed applications in Emerald much simpler than in systems where the differences in implementation between local and remote entities are visible in the language semantics. Emerald incorporates a type system that deals only with the specification of objects - ignoring differences in implementation. Thus, two different implementations of the same abstraction may be freely mixed.« less

How the brain learns how few are “many”: An fMRI study of the flexibility of quantifier semantics

PubMed Central

Heim, Stefan; McMillan, Corey T.; Clark, Robin; Baehr, Laura; Ternes, Kylie; Olm, Christopher; Min, Nam Eun; Grossman, Murray

2015-01-01

Previous work has shown that the meaning of a quantifier such as “many” or “few” depends in part on quantity. However, the meaning of a quantifier may vary depending on the context, e.g. in the case of common entities such as “many ants” (perhaps several thousands) compared to endangered species such as “many pandas” (perhaps a dozen). In a recent study (Heim et al. 2015 Front. Psychol.) we demonstrated that the relative meaning of “many” and “few” may be changed experimentally. In a truth value judgment task, displays with 40% of circles in a named color initially had a low probability of being labeled “many”. After a training phase, the likelihood of acceptance 40% as “many” increased. Moreover, the semantic learning effect also generalized to the related quantifier “few” which had not been mentioned in the training phase. Thus, fewer 40% arrays were considered “few.” In the present study, we tested the hypothesis that this semantic adaptation effect was supported by cytoarchitectonic Brodmann area (BA) 45 in Broca’s region which may contribute to semantic evaluation in the context of language and quantification. In an event-related fMRI study, 17 healthy volunteers performed the same paradigm as in the previous behavioral study. We found a relative signal increase when comparing the critical, trained proportion to untrained proportions. This specific effect was found in left BA 45 for the trained quantifier “many”, and in left BA 44 for both quantifiers, reflecting the semantic adjustment for the untrained but related quantifier “few.” These findings demonstrate the neural basis for processing the flexible meaning of a quantifier, and illustrate the neuroanatomical structures that contribute to variable meanings that can be associated with a word when used in different contexts. PMID:26481678

Semantic annotation of consumer health questions.

PubMed

Kilicoglu, Halil; Ben Abacha, Asma; Mrabet, Yassine; Shooshan, Sonya E; Rodriguez, Laritza; Masterton, Kate; Demner-Fushman, Dina

2018-02-06

Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding). To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus; however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations. The resulting corpus consists of 2614 questions (CHQA-email: 1740, CHQA-web: 874). Problems are the most frequent named entities, while treatment and general information questions are the most common question types. Inter-annotator agreement was generally modest: question types and topics yielded highest agreement, while the agreement for more complex frame annotations was lower. Agreement in CHQA-web was consistently higher than that in CHQA-email. Pairwise inter-annotator agreement proved most useful in estimating annotation confidence. To our knowledge, our corpus is the first focusing on annotation of uncurated consumer health questions. It is currently used to develop machine learning-based methods for question understanding. We make the corpus publicly available to stimulate further research on consumer health QA.

How semantic category modulates preschool children's visual memory.

PubMed

Giganti, Fiorenza; Viggiano, Maria Pia

2015-01-01

The dynamic interplay between perception and memory has been explored in preschool children by presenting filtered stimuli regarding animals and artifacts. The identification of filtered images was markedly influenced by both prior exposure and the semantic nature of the stimuli. The identification of animals required less physical information than artifacts did. Our results corroborate the notion that the human attention system evolves to reliably develop definite category-specific selection criteria by which living entities are monitored in different ways.

The influence of the immediate visual context on incremental thematic role-assignment: evidence from eye-movements in depicted events.

PubMed

Knoeferle, Pia; Crocker, Matthew W; Scheepers, Christoph; Pickering, Martin J

2005-02-01

Studies monitoring eye-movements in scenes containing entities have provided robust evidence for incremental reference resolution processes. This paper addresses the less studied question of whether depicted event scenes can affect processes of incremental thematic role-assignment. In Experiments 1 and 2, participants inspected agent-action-patient events while listening to German verb-second sentences with initial structural and role ambiguity. The experiments investigated the time course with which listeners could resolve this ambiguity by relating the verb to the depicted events. Such verb-mediated visual event information allowed early disambiguation on-line, as evidenced by anticipatory eye-movements to the appropriate agent/patient role filler. We replicated this finding while investigating the effects of intonation. Experiment 3 demonstrated that when the verb was sentence-final and thus did not establish early reference to the depicted events, linguistic cues alone enabled disambiguation before people encountered the verb. Our results reveal the on-line influence of depicted events on incremental thematic role-assignment and disambiguation of local structural and role ambiguity. In consequence, our findings require a notion of reference that includes actions and events in addition to entities (e.g. Semantics and Cognition, 1983), and argue for a theory of on-line sentence comprehension that exploits a rich inventory of semantic categories.

Age-related differences in audiovisual interactions of semantically different stimuli.

PubMed

Viggiano, Maria Pia; Giovannelli, Fabio; Giganti, Fiorenza; Rossi, Arianna; Metitieri, Tiziana; Rebai, Mohamed; Guerrini, Renzo; Cincotta, Massimo

2017-01-01

Converging results have shown that adults benefit from congruent multisensory stimulation in the identification of complex stimuli, whereas the developmental trajectory of the ability to integrate multisensory inputs in children is less well understood. In this study we explored the effects of audiovisual semantic congruency on identification of visually presented stimuli belonging to different categories, using a cross-modal approach. Four groups of children ranging in age from 6 to 13 years and adults were administered an object identification task of visually presented pictures belonging to living and nonliving entities. Stimuli were presented in visual, congruent audiovisual, incongruent audiovisual, and noise conditions. Results showed that children under 12 years of age did not benefit from multisensory presentation in speeding up the identification. In children the incoherent audiovisual condition had an interfering effect, especially for the identification of living things. These data suggest that the facilitating effect of the audiovisual interaction into semantic factors undergoes developmental changes and the consolidation of adult-like processing of multisensory stimuli begins in late childhood. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Segregation of anterior temporal regions critical for retrieving names of unique and non-unique entities reflects underlying long-range connectivity.

PubMed

Mehta, Sonya; Inoue, Kayo; Rudrauf, David; Damasio, Hanna; Tranel, Daniel; Grabowski, Thomas

2016-02-01

Lesion-deficit studies support the hypothesis that the left anterior temporal lobe (ATL) plays a critical role in retrieving names of concrete entities. They further suggest that different regions of the left ATL process different conceptual categories. Here we test the specificity of these relationships and whether the anatomical segregation is related to the underlying organization of white matter connections. We reanalyzed data from a previous lesion study of naming and recognition across five categories of concrete entities. In voxelwise logistic regressions of lesion-deficit associations, we formally incorporated measures of disconnection of long-range association fiber tracts (FTs) and covaried for recognition and non-category-specific naming deficits. We also performed fiber tractwise analyses to assess whether damage to specific FTs was preferentially associated with category-selective naming deficits. Damage to the basolateral ATL was associated with naming deficits for both unique (famous faces) and non-unique entities, whereas the damage to the temporal pole was associated with naming deficits for unique entities only. This segregation pattern remained after accounting for comorbid recognition deficits or naming deficits in other categories. The tractwise analyses showed that damage to the uncinate fasciculus (UNC) was associated with naming impairments for unique entities, while damage to the inferior longitudinal fasciculus (ILF) was associated with naming impairments for non-unique entities. Covarying for FT transection in voxelwise analyses rendered the cortical association for unique entities more focal. These results are consistent with the partial segregation of brain system support for name retrieval of unique and non-unique entities at both the level of cortical components and underlying white matter fiber bundles. Our study reconciles theoretic accounts of the functional organization of the left ATL by revealing both category-related processing and semantic hub sectors. Copyright © 2015 Elsevier Ltd. All rights reserved.

Collaborative human-machine analysis to disambiguate entities in unstructured text and structured datasets

NASA Astrophysics Data System (ADS)

Davenport, Jack H.

2016-05-01

Intelligence analysts demand rapid information fusion capabilities to develop and maintain accurate situational awareness and understanding of dynamic enemy threats in asymmetric military operations. The ability to extract relationships between people, groups, and locations from a variety of text datasets is critical to proactive decision making. The derived network of entities must be automatically created and presented to analysts to assist in decision making. DECISIVE ANALYTICS Corporation (DAC) provides capabilities to automatically extract entities, relationships between entities, semantic concepts about entities, and network models of entities from text and multi-source datasets. DAC's Natural Language Processing (NLP) Entity Analytics model entities as complex systems of attributes and interrelationships which are extracted from unstructured text via NLP algorithms. The extracted entities are automatically disambiguated via machine learning algorithms, and resolution recommendations are presented to the analyst for validation; the analyst's expertise is leveraged in this hybrid human/computer collaborative model. Military capability is enhanced by these NLP Entity Analytics because analysts can now create/update an entity profile with intelligence automatically extracted from unstructured text, thereby fusing entity knowledge from structured and unstructured data sources. Operational and sustainment costs are reduced since analysts do not have to manually tag and resolve entities.

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

PubMed Central

Rebholz-Schuhmann, Dietrich; Kim, Jee-Hyub; Yan, Ying; Dixit, Abhishek; Friteyre, Caroline; Hoehndorf, Robert; Backofen, Rolf; Lewin, Ian

2013-01-01

Motivation Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). Result This study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions. Conclusion LexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources. PMID:24124474

An approach for formalising the supply chain operations

NASA Astrophysics Data System (ADS)

Zdravković, Milan; Panetto, Hervé; Trajanović, Miroslav; Aubry, Alexis

2011-11-01

Reference models play an important role in the knowledge management of the various complex collaboration domains (such as supply chain networks). However, they often show a lack of semantic precision and, they are sometimes incomplete. In this article, we present an approach to overcome semantic inconsistencies and incompleteness of the Supply Chain Operations Reference (SCOR) model and hence improve its usefulness and expand the application domain. First, we describe a literal web ontology language (OWL) specification of SCOR concepts (and related tools) built with the intention to preserve the original approach in the classification of process reference model entities, and hence enable the effectiveness of usage in original contexts. Next, we demonstrate the system for its exploitation, in specific - tools for SCOR framework browsing and rapid supply chain process configuration. Then, we describe the SCOR-Full ontology, its relations with relevant domain ontology and show how it can be exploited for improvement of SCOR ontological framework competence. Finally, we elaborate the potential impact of the presented approach, to interoperability of systems in supply chain networks.

Semantically Enriched Data Access Policies in eHealth.

PubMed

Drozdowicz, Michał; Ganzha, Maria; Paprzycki, Marcin

2016-11-01

Internet of Things (IoT) requires novel solutions to facilitate autonomous, though controlled, resource access. Access policies have to facilitate interactions between heterogeneous entities (devices and humans). Here, we focus our attention on access control in eHealth. We propose an approach based on enriching policies, based on well-known and widely-used eXtensible Access Control Markup Language, with semantics. In the paper we describe an implementation of a Policy Information Point integrated with the HL7 Security and Privacy Ontology.

Data Type Registry - Cross Road Between Catalogs, Data And Semantics

NASA Astrophysics Data System (ADS)

Richard, S. M.; Zaslavsky, I.; Bristol, S.

2017-12-01

As more data become accessible online, the opportunity is increasing to improve search for information within datasets and for automating some levels of data integration. A prerequisite for these advances is indexing the kinds of information that are present in datasets and providing machine actionable descriptions of data structures. We are exploring approaches to enabling these capabilities in the EarthCube DigitalCrust and Data Discovery Hub Building Block projects, building on the Data type registry (DTR) workgroup activity in the Research Data Alliance. We are prototyping a registry implementation using the CNRI Cordra platform and API to enable 'deep registration' of datasets for building hydrogeologic models of the Earth's Crust, and executing complex science scenarios for river chemistry and coral bleaching data. These use cases require the ability to respond to queries such as: What are properties of Entity X; What entities include property Y (or L, M, N…), and What DataTypes are about Entity X and include property Y. Development of the registry to enable these capabilities requires more in-depth metadata than is commonly available, so we are also exploring approaches to analyzing simple tabular data to automate recognition of entities and properties, and assist users with establishing semantic mappings to data integration vocabularies. This poster will review the current capabilities and implementation of a data type registry.

Verbs in the lexicon: Why is hitting easier than breaking?

PubMed

McKoon, Gail; Love, Jessica

2011-11-01

Adult speakers use verbs in syntactically appropriate ways. For example, they know implicitly that the boy hit at the fence is acceptable but the boy broke at the fence is not. We suggest that this knowledge is lexically encoded in semantic decompositions. The decomposition for break verbs (e.g. crack, smash) is hypothesized to be more complex than that for hit verbs (e.g. kick, kiss). Specifically, the decomposition of a break verb denotes that "an entity changes state as the result of some external force" whereas the decomposition for a hit verb denotes only that "an entity potentially comes in contact with another entity." In this article, verbs of the two types were compared in a lexical decision experiment - Experiment 1 - and they were compared in sentence comprehension experiments with transitive sentences (e.g. the car hit the bicycle and the car broke the bicycle) - Experiments 2 and 3. In Experiment 1, processing times were shorter for the hit than the break verbs and in Experiments 2 and 3, processing times were shorter for the hit sentences than the break sentences, results that are in accord with the complexities of the postulated semantic decompositions.

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

PubMed

Le, Hoang-Quynh; Tran, Mai-Vu; Dang, Thanh Hai; Ha, Quang-Thuy; Collier, Nigel

2016-07-01

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system's performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a 'silver' CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID-The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530). © The Author(s) 2016. Published by Oxford University Press.

Exploring information from the topology beneath the Gene Ontology terms to improve semantic similarity measures.

PubMed

Zhang, Shu-Bo; Lai, Jian-Huang

2016-07-15

Measuring the similarity between pairs of biological entities is important in molecular biology. The introduction of Gene Ontology (GO) provides us with a promising approach to quantifying the semantic similarity between two genes or gene products. This kind of similarity measure is closely associated with the GO terms annotated to biological entities under consideration and the structure of the GO graph. However, previous works in this field mainly focused on the upper part of the graph, and seldom concerned about the lower part. In this study, we aim to explore information from the lower part of the GO graph for better semantic similarity. We proposed a framework to quantify the similarity measure beneath a term pair, which takes into account both the information two ancestral terms share and the probability that they co-occur with their common descendants. The effectiveness of our approach was evaluated against seven typical measurements on public platform CESSM, protein-protein interaction and gene expression datasets. Experimental results consistently show that the similarity derived from the lower part contributes to better semantic similarity measure. The promising features of our approach are the following: (1) it provides a mirror model to characterize the information two ancestral terms share with respect to their common descendant; (2) it quantifies the probability that two terms co-occur with their common descendant in an efficient way; and (3) our framework can effectively capture the similarity measure beneath two terms, which can serve as an add-on to improve traditional semantic similarity measure between two GO terms. The algorithm was implemented in Matlab and is freely available from http://ejl.org.cn/bio/GOBeneath/. Copyright © 2016 Elsevier B.V. All rights reserved.

Bim-Gis Integrated Geospatial Information Model Using Semantic Web and Rdf Graphs

NASA Astrophysics Data System (ADS)

Hor, A.-H.; Jadidi, A.; Sohn, G.

2016-06-01

In recent years, 3D virtual indoor/outdoor urban modelling becomes a key spatial information framework for many civil and engineering applications such as evacuation planning, emergency and facility management. For accomplishing such sophisticate decision tasks, there is a large demands for building multi-scale and multi-sourced 3D urban models. Currently, Building Information Model (BIM) and Geographical Information Systems (GIS) are broadly used as the modelling sources. However, data sharing and exchanging information between two modelling domains is still a huge challenge; while the syntactic or semantic approaches do not fully provide exchanging of rich semantic and geometric information of BIM into GIS or vice-versa. This paper proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graphs. The novelty of the proposed solution comes from the benefits of integrating BIM and GIS technologies into one unified model, so-called Integrated Geospatial Information Model (IGIM). The proposed approach consists of three main modules: BIM-RDF and GIS-RDF graphs construction, integrating of two RDF graphs, and query of information through IGIM-RDF graph using SPARQL. The IGIM generates queries from both the BIM and GIS RDF graphs resulting a semantically integrated model with entities representing both BIM classes and GIS feature objects with respect to the target-client application. The linkage between BIM-RDF and GIS-RDF is achieved through SPARQL endpoints and defined by a query using set of datasets and entity classes with complementary properties, relationships and geometries. To validate the proposed approach and its performance, a case study was also tested using IGIM system design.

ER2OWL: Generating OWL Ontology from ER Diagram

NASA Astrophysics Data System (ADS)

Fahad, Muhammad

Ontology is the fundamental part of Semantic Web. The goal of W3C is to bring the web into (its full potential) a semantic web with reusing previous systems and artifacts. Most legacy systems have been documented in structural analysis and structured design (SASD), especially in simple or Extended ER Diagram (ERD). Such systems need up-gradation to become the part of semantic web. In this paper, we present ERD to OWL-DL ontology transformation rules at concrete level. These rules facilitate an easy and understandable transformation from ERD to OWL. The set of rules for transformation is tested on a structured analysis and design example. The framework provides OWL ontology for semantic web fundamental. This framework helps software engineers in upgrading the structured analysis and design artifact ERD, to components of semantic web. Moreover our transformation tool, ER2OWL, reduces the cost and time for building OWL ontologies with the reuse of existing entity relationship models.

Investigating Geosparql Requirements for Participatory Urban Planning

NASA Astrophysics Data System (ADS)

Mohammadi, E.; Hunter, A. J. S.

2015-06-01

We propose that participatory GIS (PGIS) activities including participatory urban planning can be made more efficient and effective if spatial reasoning rules are integrated with PGIS tools to simplify engagement for public contributors. Spatial reasoning is used to describe relationships between spatial entities. These relationships can be evaluated quantitatively or qualitatively using geometrical algorithms, ontological relations, and topological methods. Semantic web services utilize tools and methods that can facilitate spatial reasoning. GeoSPARQL, introduced by OGC, is a spatial reasoning standard used to make declarations about entities (graphical contributions) that take the form of a subject-predicate-object triple or statement. GeoSPARQL uses three basic methods to infer topological relationships between spatial entities, including: OGC's simple feature topology, RCC8, and the DE-9IM model. While these methods are comprehensive in their ability to define topological relationships between spatial entities, they are often inadequate for defining complex relationships that exist in the spatial realm. Particularly relationships between urban entities, such as those between a bus route, the collection of associated bus stops and their overall surroundings as an urban planning pattern. In this paper we investigate common qualitative spatial reasoning methods as a preliminary step to enhancing the capabilities of GeoSPARQL in an online participatory GIS framework in which reasoning is used to validate plans based on standard patterns that can be found in an efficient/effective urban environment.

Using the LOINC Semantic Structure to Integrate Community-based Survey Items into a Concept-based Enterprise Data Dictionary to Support Comparative Effectiveness Research.

PubMed

Co, Manuel C; Boden-Albala, Bernadette; Quarles, Leigh; Wilcox, Adam; Bakken, Suzanne

2012-01-01

In designing informatics infrastructure to support comparative effectiveness research (CER), it is necessary to implement approaches for integrating heterogeneous data sources such as clinical data typically stored in clinical data warehouses and those that are normally stored in separate research databases. One strategy to support this integration is the use of a concept-oriented data dictionary with a set of semantic terminology models. The aim of this paper is to illustrate the use of the semantic structure of Clinical LOINC (Logical Observation Identifiers, Names, and Codes) in integrating community-based survey items into the Medical Entities Dictionary (MED) to support the integration of survey data with clinical data for CER studies.

Spatiotemporal dynamics during processing of abstract and concrete verbs: an ERP study.

PubMed

Dalla Volta, Riccardo; Fabbri-Destro, Maddalena; Gentilucci, Maurizio; Avanzini, Pietro

2014-08-01

Different accounts have been proposed to explain the nature of concept representations. Embodied accounts claim a key involvement of sensory-motor systems during semantic processing while more traditional accounts posit that concepts are abstract mental entities independent of perceptual and motor brain systems. While the involvement of sensory-motor areas in concrete language processing is supported by a large number of studies, this involvement is far from being established when considering abstract language. The present study addressed abstract and concrete verb processing, by investigating the spatiotemporal dynamics of evoked responses by means of high density EEG while participants performed a semantic decision task. In addition, RTs to the same set of stimuli were collected. In both early and late time intervals, ERP scalp topography significantly differed according to word categories. Concrete verbs showed involvement of parieto-frontal networks for action, according to the implied body effector. In contrast, abstract verbs recruited mostly frontal regions outside the motor system, suggesting a non-motor semantic processing for this category. In addition, differently from what has been reported during action observation, the parietal recruitment related to concrete verbs presentation followed the frontal one. The present findings suggest that action word semantic is grounded in sensory-motor systems, provided a bodily effector is specified, while abstract concepts׳ representation cannot be easily explained by a motor embodiment. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Semantic Representation and Scale-Up of Integrated Air Traffic Management Data

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Ranjan, Shubha; Wei, Mei Y.; Eshow, Michelle M.

2016-01-01

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.

Unsupervised Biomedical Named Entity Recognition: Experiments with Clinical and Biological Texts

PubMed Central

Zhang, Shaodian; Elhadad, Nóemie

2013-01-01

Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work. PMID:23954592

Whence Structured Propositions?

ERIC Educational Resources Information Center

Keller, Lorraine Juliano

2012-01-01

This thesis is a critical examination of "Structured Propositionalism" (SP), the view that propositions are complex entities composed of the semantic values of the (meaningful) parts of the sentences that express them. According to SP, propositions have constituents and are individuated by the identity and arrangement of their…

Bayesian Modeling of Temporal Coherence in Videos for Entity Discovery and Summarization.

PubMed

Mitra, Adway; Biswas, Soma; Bhattacharyya, Chiranjib

2017-03-01

A video is understood by users in terms of entities present in it. Entity Discovery is the task of building appearance model for each entity (e.g., a person), and finding all its occurrences in the video. We represent a video as a sequence of tracklets, each spanning 10-20 frames, and associated with one entity. We pose Entity Discovery as tracklet clustering, and approach it by leveraging Temporal Coherence (TC): the property that temporally neighboring tracklets are likely to be associated with the same entity. Our major contributions are the first Bayesian nonparametric models for TC at tracklet-level. We extend Chinese Restaurant Process (CRP) to TC-CRP, and further to Temporally Coherent Chinese Restaurant Franchise (TC-CRF) to jointly model entities and temporal segments using mixture components and sparse distributions. For discovering persons in TV serial videos without meta-data like scripts, these methods show considerable improvement over state-of-the-art approaches to tracklet clustering in terms of clustering accuracy, cluster purity and entity coverage. The proposed methods can perform online tracklet clustering on streaming videos unlike existing approaches, and can automatically reject false tracklets. Finally we discuss entity-driven video summarization- where temporal segments of the video are selected based on the discovered entities, to create a semantically meaningful summary.

Collaborative mining and transfer learning for relational data

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Eslami, Mohammed

2015-06-01

Many of the real-world problems, - including human knowledge, communication, biological, and cyber network analysis, - deal with data entities for which the essential information is contained in the relations among those entities. Such data must be modeled and analyzed as graphs, with attributes on both objects and relations encode and differentiate their semantics. Traditional data mining algorithms were originally designed for analyzing discrete objects for which a set of features can be defined, and thus cannot be easily adapted to deal with graph data. This gave rise to the relational data mining field of research, of which graph pattern learning is a key sub-domain [11]. In this paper, we describe a model for learning graph patterns in collaborative distributed manner. Distributed pattern learning is challenging due to dependencies between the nodes and relations in the graph, and variability across graph instances. We present three algorithms that trade-off benefits of parallelization and data aggregation, compare their performance to centralized graph learning, and discuss individual benefits and weaknesses of each model. Presented algorithms are designed for linear speedup in distributed computing environments, and learn graph patterns that are both closer to ground truth and provide higher detection rates than centralized mining algorithm.

Network analysis of named entity co-occurrences in written texts

NASA Astrophysics Data System (ADS)

Amancio, Diego Raphael

2016-06-01

The use of methods borrowed from statistics and physics to analyze written texts has allowed the discovery of unprecedent patterns of human behavior and cognition by establishing links between models features and language structure. While current models have been useful to unveil patterns via analysis of syntactical and semantical networks, only a few works have probed the relevance of investigating the structure arising from the relationship between relevant entities such as characters, locations and organizations. In this study, we represent entities appearing in the same context as a co-occurrence network, where links are established according to a null model based on random, shuffled texts. Computational simulations performed in novels revealed that the proposed model displays interesting topological features, such as the small world feature, characterized by high values of clustering coefficient. The effectiveness of our model was verified in a practical pattern recognition task in real networks. When compared with traditional word adjacency networks, our model displayed optimized results in identifying unknown references in texts. Because the proposed representation plays a complementary role in characterizing unstructured documents via topological analysis of named entities, we believe that it could be useful to improve the characterization of written texts (and related systems), specially if combined with traditional approaches based on statistical and deeper paradigms.

Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

PubMed Central

Gan, Liang; Cheng, Mian; Wu, Quanyuan

2018-01-01

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking. PMID:29849994

External details revisited - A new taxonomy for coding 'non-episodic' content during autobiographical memory retrieval.

PubMed

Strikwerda-Brown, Cherie; Mothakunnel, Annu; Hodges, John R; Piguet, Olivier; Irish, Muireann

2018-04-24

Autobiographical memory (ABM) is typically held to comprise episodic and semantic elements, with the vast majority of studies to date focusing on profiles of episodic details in health and disease. In this context, 'non-episodic' elements are often considered to reflect semantic processing or are discounted from analyses entirely. Mounting evidence suggests that rather than reflecting one unitary entity, semantic autobiographical information may contain discrete subcomponents, which vary in their relative degree of semantic or episodic content. This study aimed to (1) review the existing literature to formally characterize the variability in analysis of 'non-episodic' content (i.e., external details) on the Autobiographical Interview and (2) use these findings to create a theoretically grounded framework for coding external details. Our review exposed discrepancies in the reporting and interpretation of external details across studies, reinforcing the need for a new, consistent approach. We validated our new external details scoring protocol (the 'NExt' taxonomy) in patients with Alzheimer's disease (n = 18) and semantic dementia (n = 13), and 20 healthy older Control participants and compared profiles of the NExt subcategories across groups and time periods. Our results revealed increased sensitivity of the NExt taxonomy in discriminating between ABM profiles of patient groups, when compared to traditionally used internal and external detail metrics. Further, remote and recent autobiographical memories displayed distinct compositions of the NExt detail types. This study is the first to provide a fine-grained and comprehensive taxonomy to parse external details into intuitive subcategories and to validate this protocol in neurodegenerative disorders. © 2018 The British Psychological Society.

Analysing the lack of Demand Organisation

NASA Astrophysics Data System (ADS)

Boxer, Philip; Cohen, Bernard

1998-07-01

We seek to develop means of intervention in Enterprises that will enable them to react in an effective, sustainable and timely fashion to changes in the ways that markets and demand are organized; that is, to act strategically. We take an enterprise to be some entity that seeks to provide its clients with services that they value while maintaining its ability to do so in the face of changes in the demands of its clients and in the resources at its disposal. The services that clients value form around what the organization of their demands lack. The concept of strategy therefore rests on critically evaluating the ontology and semantics of the Enterprise in relation to these holes in demand organization. We access ontology and semantics by constructing and manipulating hypothetical, first-order, mathematical models of the Enterprise's services and of its value-adding processes. Because an enterprise is an anticipatory system, its semantic domain must include representations of the enterprise's model of itself and of the market and demand organizations within which it competes. First-order (set) theory provides adequate expressive power here, but alternative, higher order, mathematical frameworks, such as Dubois' hyperincursion, provide inadequate power, particularly in relation to the analysis of the properties of emergence. Knowing exactly why and where this mathematical lack manifests in the analysis process enables effective collaboration between systems analysts and psychoanalysts, and suggest directions for mathematical research.

What's Unique about Unique Entities? An fMRI Investigation of the Semantics of Famous Faces and Landmarks

PubMed Central

Olson, Ingrid R.

2012-01-01

Famous people and artifacts are referred to as “unique entities” (UEs) due to the unique nature of the knowledge we have about them. Past imaging and lesion experiments have indicated that the anterior temporal lobes (ATLs) as having a special role in the processing of UEs. It has remained unclear which attributes of UEs were responsible for the observed effects in imaging experiments. In this study, we investigated what factors of UEs influence brain activity. In a training paradigm, we systematically varied the uniqueness of semantic associations, the presence/absence of a proper name, and the number of semantic associations to determine factors modulating activity in regions subserving the processing of UEs. We found that a conjunction of unique semantic information and proper names modulated activity within a section of the left ATL. Overall, the processing of UEs involved a wider left-hemispheric cortical network. Within these regions, brain activity was significantly affected by the unique semantic attributes especially in the presence of a proper name, but we could not find evidence for an effect of the number of semantic associations. Findings are discussed in regard to current models of ATL function, the neurophysiology of semantics, and social cognitive processing. PMID:22021913

GeneView: a comprehensive semantic search engine for PubMed.

PubMed

Thomas, Philippe; Starlinger, Johannes; Vowinkel, Alexander; Arzt, Sebastian; Leser, Ulf

2012-07-01

Research results are primarily published in scientific literature and curation efforts cannot keep up with the rapid growth of published literature. The plethora of knowledge remains hidden in large text repositories like MEDLINE. Consequently, life scientists have to spend a great amount of time searching for specific information. The enormous ambiguity among most names of biomedical objects such as genes, chemicals and diseases often produces too large and unspecific search results. We present GeneView, a semantic search engine for biomedical knowledge. GeneView is built upon a comprehensively annotated version of PubMed abstracts and openly available PubMed Central full texts. This semi-structured representation of biomedical texts enables a number of features extending classical search engines. For instance, users may search for entities using unique database identifiers or they may rank documents by the number of specific mentions they contain. Annotation is performed by a multitude of state-of-the-art text-mining tools for recognizing mentions from 10 entity classes and for identifying protein-protein interactions. GeneView currently contains annotations for >194 million entities from 10 classes for ∼21 million citations with 271,000 full text bodies. GeneView can be searched at http://bc3.informatik.hu-berlin.de/.

On the application of semantic technologies to the domain of forensic investigations in financial crimes

NASA Astrophysics Data System (ADS)

Scheidat, Tobias; Merkel, Ronny; Krummel, Volker; Gerlach, Andreas; Weisensee, Michala; Zeihe, Jana; Dittmann, Jana

2017-10-01

In daily police practice, forensic investigation of criminal cases is mainly based on manual work and the experience of individual forensic experts, using basic storage and data processing technologies. However, an individual criminal case does not only consist of the actual offence, but also of a variety of different aspects involved. For example, in order to solve a financial criminal case, an investigator has to find interrelations between different case entities as well as to other cases. The required information about these different entities is often stored in various databases and mostly requires to be manually requested and processed by forensic investigators. We propose the application of semantic technologies to the domain of forensic investigations at the example of financial crimes. Such combination allows for modelling specific case entities and their interrelations within and between cases. As a result, an explorative search of connections between case entities in the scope of an investigation as well as an automated derivation of conclusions from an established fact base is enabled. The proposed model is presented in the form of a crime field ontology, based on different types of knowledge obtained from three individual sources: open source intelligence, forensic investigators and captive interviews of detained criminals. The modelled crime field ontology is illustrated at two examples using the well known crime type of explosive attack on ATM and the potentially upcoming crime type data theft by NFC crowd skimming. Of these criminal modi operandi, anonymized fictional are modelled, visualized and exploratively searched. Modelled case entities include modi operandi, events, actors, resources, exploited weaknesses as well as flows of money, data and know how. The potential exploration of interrelations between the different case entities of such examples is illustrated in the scope of a fictitious investigation, highlighting the potential of the approach.

Effects of level of processing at encoding and types of retrieval task in mild cognitive impairment and normal aging.

PubMed

Froger, Charlotte; Taconnat, Laurence; Landré, Lionel; Beigneux, Katia; Isingrini, Michel

2009-04-01

A total of 16 young (M = 27.25 years), 13 healthy elderly (M = 75.38 years), and 10 older adults with probable mild cognitive impairment (MCI; M = 78.6 years) carried out a task under two different encoding conditions (shallow vs. semantic) and two retrieval conditions (free recall vs. recognition). For the shallow condition, participants had to decide whether the first or last letter of each word in a list was "E." For the semantic condition, they had to decide whether each word represented a concrete or abstract entity. The MCI group was only able to benefit from semantic encoding to the same extent as the healthy older adults in the recognition task, whereas the younger and healthy older adults benefited in both retrieval tasks. These results suggest that the MCI group required cognitive support at retrieval to make effective use of semantic processing carried out at encoding. In the discussion, we suggest that adults with MCI engage more in deep processing, using the semantic network, than hitherto thought.

Using the LOINC Semantic Structure to Integrate Community-based Survey Items into a Concept-based Enterprise Data Dictionary to Support Comparative Effectiveness Research

PubMed Central

Co, Manuel C.; Boden-Albala, Bernadette; Quarles, Leigh; Wilcox, Adam; Bakken, Suzanne

2012-01-01

In designing informatics infrastructure to support comparative effectiveness research (CER), it is necessary to implement approaches for integrating heterogeneous data sources such as clinical data typically stored in clinical data warehouses and those that are normally stored in separate research databases. One strategy to support this integration is the use of a concept-oriented data dictionary with a set of semantic terminology models. The aim of this paper is to illustrate the use of the semantic structure of Clinical LOINC (Logical Observation Identifiers, Names, and Codes) in integrating community-based survey items into the Medical Entities Dictionary (MED) to support the integration of survey data with clinical data for CER studies. PMID:24199059

Leveraging the UML Metamodel: Expressing ORM Semantics Using a UML Profile

DOE Office of Scientific and Technical Information (OSTI.GOV)

CUYLER,DAVID S.

2000-11-01

Object Role Modeling (ORM) techniques produce a detailed domain model from the perspective of the business owner/customer. The typical process begins with a set of simple sentences reflecting facts about the business. The output of the process is a single model representing primarily the persistent information needs of the business. This type of model contains little, if any reference to a targeted computerized implementation. It is a model of business entities not of software classes. Through well-defined procedures, an ORM model can be transformed into a high quality objector relational schema.

Frontotemporal Dementia

PubMed Central

Olney, Nicholas T.; Spina, Salvatore; Miller, Bruce L.

2017-01-01

Frontotemporal Dementia (FTD) is a heterogeneous disorder with distinct clinical phenotypes associated with multiple neuropathologic entities. Presently, the term FTD encompasses clinical disorders that include changes in behavior, language, executive control and often motor symptoms. The core FTD spectrum disorders include: behavioral variant FTD (bvFTD), nonfluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA). Related FTD disorders include frontotemporal dementia with motor neuron disease (FTD-MND), progressive supranuclear palsy syndrome (PSP-S) and corticobasal syndrome (CBS). In this chapter we will discuss the clinic presentation, diagnostic criteria, neuropathology, genetics and treatments of these disorders. PMID:28410663

Morphological Decomposition in Reading Hebrew Homographs

ERIC Educational Resources Information Center

Miller, Paul; Liran-Hazan, Batel; Vaknin, Vered

2016-01-01

The present work investigates whether and how morphological decomposition processes bias the reading of Hebrew heterophonic homographs, i.e., unique orthographic patterns that are associated with two separate phonological, semantic entities depicted by means of two morphological structures (linear and nonlinear). In order to reveal the nature of…

Understanding semantic mapping evolution by observing changes in biomedical ontologies.

PubMed

dos Reis, Julio Cesar; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

2014-02-01

Knowledge Organization Systems (KOSs) are extensively used in the biomedical domain to support information sharing between software applications. KOSs are proposed covering different, but overlapping subjects, and mappings indicate the semantic relation between concepts from two KOSs. Over time, KOSs change as do the mappings between them. This can result from a new discovery or a revision of existing knowledge which includes corrections of concepts or mappings. Indeed, changes affecting KOS entities may force the underline mappings to be updated in order to ensure their reliability over time. To tackle this open research problem, we study how mappings are affected by KOS evolution. This article presents a detailed descriptive analysis of the impact that changes in KOS have on mappings. As a case study, we use the official mappings established between SNOMED CT and ICD-9-CM from 2009 to 2011. Results highlight factors according to which KOS changes in varying degrees influence the evolution of mappings. Copyright © 2013 Elsevier Inc. All rights reserved.

Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2.

PubMed

Sorokin, Anatoly; Le Novère, Nicolas; Luna, Augustin; Czauderna, Tobias; Demir, Emek; Haw, Robin; Mi, Huaiyu; Moodie, Stuart; Schreiber, Falk; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Semantic Enhancement for Enterprise Data Management

NASA Astrophysics Data System (ADS)

Ma, Li; Sun, Xingzhi; Cao, Feng; Wang, Chen; Wang, Xiaoyuan; Kanellos, Nick; Wolfson, Dan; Pan, Yue

Taking customer data as an example, the paper presents an approach to enhance the management of enterprise data by using Semantic Web technologies. Customer data is the most important kind of core business entity a company uses repeatedly across many business processes and systems, and customer data management (CDM) is becoming critical for enterprises because it keeps a single, complete and accurate record of customers across the enterprise. Existing CDM systems focus on integrating customer data from all customer-facing channels and front and back office systems through multiple interfaces, as well as publishing customer data to different applications. To make the effective use of the CDM system, this paper investigates semantic query and analysis over the integrated and centralized customer data, enabling automatic classification and relationship discovery. We have implemented these features over IBM Websphere Customer Center, and shown the prototype to our clients. We believe that our study and experiences are valuable for both Semantic Web community and data management community.

Distant supervision for neural relation extraction integrated with word attention and property features.

PubMed

Qu, Jianfeng; Ouyang, Dantong; Hua, Wen; Ye, Yuxin; Li, Ximing

2018-04-01

Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each individual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines. Copyright © 2018 Elsevier Ltd. All rights reserved.

Incremental Ontology-Based Extraction and Alignment in Semi-structured Documents

NASA Astrophysics Data System (ADS)

Thiam, Mouhamadou; Bennacer, Nacéra; Pernelle, Nathalie; Lô, Moussa

SHIRIis an ontology-based system for integration of semi-structured documents related to a specific domain. The system’s purpose is to allow users to access to relevant parts of documents as answers to their queries. SHIRI uses RDF/OWL for representation of resources and SPARQL for their querying. It relies on an automatic, unsupervised and ontology-driven approach for extraction, alignment and semantic annotation of tagged elements of documents. In this paper, we focus on the Extract-Align algorithm which exploits a set of named entity and term patterns to extract term candidates to be aligned with the ontology. It proceeds in an incremental manner in order to populate the ontology with terms describing instances of the domain and to reduce the access to extern resources such as Web. We experiment it on a HTML corpus related to call for papers in computer science and the results that we obtain are very promising. These results show how the incremental behaviour of Extract-Align algorithm enriches the ontology and the number of terms (or named entities) aligned directly with the ontology increases.

Agent-patient similarity affects sentence structure in language production: evidence from subject omissions in Mandarin

PubMed Central

Hsiao, Yaling; Gao, Yannan; MacDonald, Maryellen C.

2014-01-01

Interference effects from semantically similar items are well-known in studies of single word production, where the presence of semantically similar distractor words slows picture naming. This article examines the consequences of this interference in sentence production and tests the hypothesis that in situations of high similarity-based interference, producers are more likely to omit one of the interfering elements than when there is low semantic similarity and thus low interference. This work investigated language production in Mandarin, which allows subject noun phrases to be omitted in discourse contexts in which the subject entity has been previously mentioned in the discourse. We hypothesize that Mandarin speakers omit the subject more often when the subject and the object entities are conceptually similar. A corpus analysis of simple transitive sentences found higher rates of subject omission when both the subject and object were animate (potentially yielding similarity-based interference) than when the subject was animate and object was inanimate. A second study manipulated subject-object animacy in a picture description task and replicated this result: participants omitted the animate subject more often when the object was also animate than when it was inanimate. These results suggest that similarity-based interference affects sentence forms, particularly when the agent of the action is mentioned in the sentence. Alternatives and mechanisms for this effect are discussed. PMID:25278915

An ontologically founded architecture for information systems in clinical and epidemiological research.

PubMed

Uciteli, Alexandr; Groß, Silvia; Kireyev, Sergej; Herre, Heinrich

2011-08-09

This paper presents an ontologically founded basic architecture for information systems, which are intended to capture, represent, and maintain metadata for various domains of clinical and epidemiological research. Clinical trials exhibit an important basis for clinical research, and the accurate specification of metadata and their documentation and application in clinical and epidemiological study projects represents a significant expense in the project preparation and has a relevant impact on the value and quality of these studies.An ontological foundation of an information system provides a semantic framework for the precise specification of those entities which are presented in this system. This semantic framework should be grounded, according to our approach, on a suitable top-level ontology. Such an ontological foundation leads to a deeper understanding of the entities of the domain under consideration, and provides a common unifying semantic basis, which supports the integration of data and the interoperability between different information systems.The intended information systems will be applied to the field of clinical and epidemiological research and will provide, depending on the application context, a variety of functionalities. In the present paper, we focus on a basic architecture which might be common to all such information systems. The research, set forth in this paper, is included in a broader framework of clinical research and continues the work of the IMISE on these topics.

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

PubMed Central

Miyao, Yusuke; Collier, Nigel

2017-01-01

Background Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. Objective This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed. Methods We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines. Results The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes. Conclusions We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP. PMID:28468748

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

PubMed

Alvaro, Nestor; Miyao, Yusuke; Collier, Nigel

2017-05-03

Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed. We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines. The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes. We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP. ©Nestor Alvaro, Yusuke Miyao, Nigel Collier. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 03.05.2017.

OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents.

PubMed

Naderi, Nona; Kappler, Thomas; Baker, Christopher J O; Witte, René

2011-10-01

Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation. We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%. The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger. witte@semanticsoftware.info.

Web-based Visualization and Query of semantically segmented multiresolution 3D Models in the Field of Cultural Heritage

NASA Astrophysics Data System (ADS)

Auer, M.; Agugiaro, G.; Billen, N.; Loos, L.; Zipf, A.

2014-05-01

Many important Cultural Heritage sites have been studied over long periods of time by different means of technical equipment, methods and intentions by different researchers. This has led to huge amounts of heterogeneous "traditional" datasets and formats. The rising popularity of 3D models in the field of Cultural Heritage in recent years has brought additional data formats and makes it even more necessary to find solutions to manage, publish and study these data in an integrated way. The MayaArch3D project aims to realize such an integrative approach by establishing a web-based research platform bringing spatial and non-spatial databases together and providing visualization and analysis tools. Especially the 3D components of the platform use hierarchical segmentation concepts to structure the data and to perform queries on semantic entities. This paper presents a database schema to organize not only segmented models but also different Levels-of-Details and other representations of the same entity. It is further implemented in a spatial database which allows the storing of georeferenced 3D data. This enables organization and queries by semantic, geometric and spatial properties. As service for the delivery of the segmented models a standardization candidate of the OpenGeospatialConsortium (OGC), the Web3DService (W3DS) has been extended to cope with the new database schema and deliver a web friendly format for WebGL rendering. Finally a generic user interface is presented which uses the segments as navigation metaphor to browse and query the semantic segmentation levels and retrieve information from an external database of the German Archaeological Institute (DAI).

Semantic Approaches Applied to Scientific Ocean Drilling Data

NASA Astrophysics Data System (ADS)

Fils, D.; Jenkins, C. J.; Arko, R. A.

2012-12-01

The application of Linked Open Data methods to 40 years of data from scientific ocean drilling is providing users with several new methods for rich-content data search and discovery. Data from the Deep Sea Drilling Project (DSDP), Ocean Drilling Program (ODP) and Integrated Ocean Drilling Program (IODP) have been translated and placed in RDF triple stores to provide access via SPARQL, linked open data patterns, and by embedded structured data through schema.org / RDFa. Existing search services have been re-encoded in this environment which allows the new and established architectures to be contrasted. Vocabularies including computed semantic relations between concepts, allow separate but related data sets to be connected on their concepts and resources even when they are expressed somewhat differently. Scientific ocean drilling produces a wide range of data types and data sets: borehole logging file-based data, images, measurements, visual observations and the physical sample data. The steps involved in connecting these data to concepts using vocabularies will be presented, including the connection of data sets through Vocabulary of Interlinked Datasets (VoID) and open entity collections such as Freebase and dbPedia. Demonstrated examples will include: (i) using RDF Schema for inferencing and in federated searches across NGDC and IODP data, (ii) using structured data in the data.oceandrilling.org web site, (iii) association through semantic methods of age models and depth recorded data to facilitate age based searches for data recorded by depth only.

Application of Alignment Methodologies to Spatial Ontologies in the Hydro Domain

NASA Astrophysics Data System (ADS)

Lieberman, J. E.; Cheatham, M.; Varanka, D.

2015-12-01

Ontologies are playing an increasing role in facilitating mediation and translation between datasets representing diverse schemas, vocabularies, or knowledge communities. This role is relatively straightforward when there is one ontology comprising all relevant common concepts that can be mapped to entities in each dataset. Frequently, one common ontology has not been agreed to. Either each dataset is represented by a distinct ontology, or there are multiple candidates for commonality. Either the one most appropriate (expressive, relevant, correct) ontology must be chosen, or else concepts and relationships matched across multiple ontologies through an alignment process so that they may be used in concert to carry out mediation or other semantic operations. A resulting alignment can be effective to the extent that entities in in the ontologies represent differing terminology for comparable conceptual knowledge. In cases such as spatial ontologies, though, ontological entities may also represent disparate conceptualizations of space according to the discernment methods and application domains on which they are based. One ontology's wetland concept may overlap in space with another ontology's recharge zone or wildlife range or water feature. In order to evaluate alignment with respect to spatial ontologies, alignment has been applied to a series of ontologies pertaining to surface water that are used variously in hydrography (characterization of water features), hydrology (study of water cycling), and water quality (nutrient and contaminant transport) application domains. There is frequently a need to mediate between datasets in each domain in order to develop broader understanding of surface water systems, so there is a practical as well theoretical value in the alignment. From a domain expertise standpoint, the ontologies under consideration clearly contain some concepts that are spatially as well as conceptually identical and then others with less clear similarities in either sense. Our study serves both to determine the limits of standard methods for aligning spatial ontologies and to suggest new methods of calculating similarity axioms that take into account semantic, spatial, and cognitive criteria relevant to fitness for relevant usage scenarios.

Contextual Processing of Abstract Concepts Reveals Neural Representations of Non-Linguistic Semantic Content

PubMed Central

Wilson-Mendenhall, Christine D.; Simmons, W. Kyle; Martin, Alex; Barsalou, Lawrence W.

2014-01-01

Concepts develop for many aspects of experience, including abstract internal states and abstract social activities that do not refer to concrete entities in the world. The current study assessed the hypothesis that, like concrete concepts, distributed neural patterns of relevant, non-linguistic semantic content represent the meanings of abstract concepts. In a novel neuroimaging paradigm, participants processed two abstract concepts (convince, arithmetic) and two concrete concepts (rolling, red) deeply and repeatedly during a concept-scene matching task that grounded each concept in typical contexts. Using a catch trial design, neural activity associated with each concept word was separated from neural activity associated with subsequent visual scenes to assess activations underlying the detailed semantics of each concept. We predicted that brain regions underlying mentalizing and social cognition (e.g., medial prefrontal cortex, superior temporal sulcus) would become active to represent semantic content central to convince, whereas brain regions underlying numerical cognition (e.g., bilateral intraparietal sulcus) would become active to represent semantic content central to arithmetic. The results supported these predictions, suggesting that the meanings of abstract concepts arise from distributed neural systems that represent concept-specific content. PMID:23363408

Complement Coercion: The Joint Effects of Type and Typicality.

PubMed

Zarcone, Alessandra; McRae, Ken; Lenci, Alessandro; Padó, Sebastian

2017-01-01

Complement coercion ( begin a book → reading ) involves a type clash between an event-selecting verb and an entity-denoting object, triggering a covert event ( reading ). Two main factors involved in complement coercion have been investigated: the semantic type of the object (event vs. entity), and the typicality of the covert event ( the author began a book → writing ). In previous research, reading times have been measured at the object. However, the influence of the typicality of the subject-object combination on processing an aspectual verb such as begin has not been studied. Using a self-paced reading study, we manipulated semantic type and subject-object typicality, exploiting German word order to measure reading times at the aspectual verb. These variables interacted at the target verb. We conclude that both type and typicality probabilistically guide expectations about upcoming input. These results are compatible with an expectation-based view of complement coercion and language comprehension more generally in which there is rapid interaction between what is typically viewed as linguistic knowledge, and what is typically viewed as domain general knowledge about how the world works.

Complement Coercion: The Joint Effects of Type and Typicality

PubMed Central

Zarcone, Alessandra; McRae, Ken; Lenci, Alessandro; Padó, Sebastian

2017-01-01

Complement coercion (begin a book →reading) involves a type clash between an event-selecting verb and an entity-denoting object, triggering a covert event (reading). Two main factors involved in complement coercion have been investigated: the semantic type of the object (event vs. entity), and the typicality of the covert event (the author began a book →writing). In previous research, reading times have been measured at the object. However, the influence of the typicality of the subject–object combination on processing an aspectual verb such as begin has not been studied. Using a self-paced reading study, we manipulated semantic type and subject–object typicality, exploiting German word order to measure reading times at the aspectual verb. These variables interacted at the target verb. We conclude that both type and typicality probabilistically guide expectations about upcoming input. These results are compatible with an expectation-based view of complement coercion and language comprehension more generally in which there is rapid interaction between what is typically viewed as linguistic knowledge, and what is typically viewed as domain general knowledge about how the world works. PMID:29225585

Recognition of a person named entity from the text written in a natural language

NASA Astrophysics Data System (ADS)

Dolbin, A. V.; Rozaliev, V. L.; Orlova, Y. A.

2017-01-01

This work is devoted to the semantic analysis of texts, which were written in a natural language. The main goal of the research was to compare latent Dirichlet allocation and latent semantic analysis to identify elements of the human appearance in the text. The completeness of information retrieval was chosen as the efficiency criteria for methods comparison. However, it was insufficient to choose only one method for achieving high recognition rates. Thus, additional methods were used for finding references to the personality in the text. All these methods are based on the created information model, which represents person’s appearance.

A method of constructing geo-object ontology in disaster system for prevention and decrease

NASA Astrophysics Data System (ADS)

Li, Bin; Liu, Jiping; Shi, Lihong; Wang, Zhenfeng

2009-10-01

A kind of formal system, which can express clearly a certain entity or information, is needed to express geographical concept. Besides, some rules explaining the interrelationship and action between different components are also required. Therefore, the conception of geo-object ontology is introduced. It is a shared formalization and display specification of conceptual knowledge system in the field of concrete application of spatial information science. It can constitute hierarchy structure, which derives from the concept classification system in the geographical area. Its concepts can be described by the property. Property sets can form a vector space with multi-dimensional characteristics. Geographic space is composed of different types of geographic entities. And its concept is formed by a series of geographic entities with the same properties and actions. Moreover, each of the geographic entities can be mapped to an object, and each object has its spatial property, time information and topology, semantic relationships associated with other objects. The biggest difference between ecumenical information ontology and geo-ontology is that the latter has the spatial characteristics. During the construction process of geo-object ontology, some important components, such as geographic type, spatial relation, spatial entity type and coordinates, time, should be included to make further research. Here, taking disaster as an example, by using Protégé and OWL, combined methods used by constructing the geo-object ontology in the form of being manual made by domanial experts and semi-automatic are investigated oriented to disaster to serve ultimately geographic information retrieval service driven by ontology.

Alignment of the UMLS semantic network with BioTop: methodology and assessment.

PubMed

Schulz, Stefan; Beisswanger, Elena; van den Hoek, László; Bodenreider, Olivier; van Mulligen, Erik M

2009-06-15

For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop. The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability. The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up several design errors that could be fixed. A comparison between a human curator and the ontology yielded only a low agreement. Ontology reasoning was also used to successfully identify 133 inconsistent semantic-type combinations. BioTop, the OWL DL representation of the UMLS SN, and the mapping ontology are available at http://www.purl.org/biotop/.

Algorithms and semantic infrastructure for mutation impact extraction and grounding.

PubMed

Laurila, Jonas B; Naderi, Nona; Witte, René; Riazanov, Alexandre; Kouznetsov, Alexandre; Baker, Christopher J O

2010-12-02

Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases. We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework. We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

Entrez Neuron RDFa: a pragmatic semantic web application for data integration in neuroscience research.

PubMed

Samwald, Matthias; Lim, Ernest; Masiar, Peter; Marenco, Luis; Chen, Huajun; Morse, Thomas; Mutalik, Pradeep; Shepherd, Gordon; Miller, Perry; Cheung, Kei-Hoi

2009-01-01

The amount of biomedical data available in Semantic Web formats has been rapidly growing in recent years. While these formats are machine-friendly, user-friendly web interfaces allowing easy querying of these data are typically lacking. We present "Entrez Neuron", a pilot neuron-centric interface that allows for keyword-based queries against a coherent repository of OWL ontologies. These ontologies describe neuronal structures, physiology, mathematical models and microscopy images. The returned query results are organized hierarchically according to brain architecture. Where possible, the application makes use of entities from the Open Biomedical Ontologies (OBO) and the 'HCLS knowledgebase' developed by the W3C Interest Group for Health Care and Life Science. It makes use of the emerging RDFa standard to embed ontology fragments and semantic annotations within its HTML-based user interface. The application and underlying ontologies demonstrate how Semantic Web technologies can be used for information integration within a curated information repository and between curated information repositories. It also demonstrates how information integration can be accomplished on the client side, through simple copying and pasting of portions of documents that contain RDFa markup.

Standard biological parts knowledgebase.

PubMed

Galdzicki, Michal; Rodriguez, Cesar; Chandran, Deepak; Sauro, Herbert M; Gennari, John H

2011-02-24

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate "promoter" parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible.

Continuous Flash Suppression: Stimulus Fractionation rather than Integration.

PubMed

Moors, Pieter; Hesselmann, Guido; Wagemans, Johan; van Ee, Raymond

2017-10-01

Recent studies using continuous flash suppression suggest that invisible stimuli are processed as integrated, semantic entities. We challenge the viability of this account, given recent findings on the neural basis of interocular suppression and replication failures of high-profile CFS studies. We conclude that CFS reveals stimulus fractionation in visual cortex. Copyright © 2017 Elsevier Ltd. All rights reserved.

BANNER: an executable survey of advances in biomedical named entity recognition.

PubMed

Leaman, Robert; Gonzalez, Graciela

2008-01-01

There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text.

Initial implementation of a comparative data analysis ontology.

PubMed

Prosdocimi, Francisco; Chisham, Brandon; Pontelli, Enrico; Thompson, Julie D; Stoltzfus, Arlin

2009-07-03

Comparative analysis is used throughout biology. When entities under comparison (e.g. proteins, genomes, species) are related by descent, evolutionary theory provides a framework that, in principle, allows N-ary comparisons of entities, while controlling for non-independence due to relatedness. Powerful software tools exist for specialized applications of this approach, yet it remains under-utilized in the absence of a unifying informatics infrastructure. A key step in developing such an infrastructure is the definition of a formal ontology. The analysis of use cases and existing formalisms suggests that a significant component of evolutionary analysis involves a core problem of inferring a character history, relying on key concepts: "Operational Taxonomic Units" (OTUs), representing the entities to be compared; "character-state data" representing the observations compared among OTUs; "phylogenetic tree", representing the historical path of evolution among the entities; and "transitions", the inferred evolutionary changes in states of characters that account for observations. Using the Web Ontology Language (OWL), we have defined these and other fundamental concepts in a Comparative Data Analysis Ontology (CDAO). CDAO has been evaluated for its ability to represent token data sets and to support simple forms of reasoning. With further development, CDAO will provide a basis for tools (for semantic transformation, data retrieval, validation, integration, etc.) that make it easier for software developers and biomedical researchers to apply evolutionary methods of inference to diverse types of data, so as to integrate this powerful framework for reasoning into their research.

Animacy and competition in relative clause production: A cross-linguistic investigation

PubMed Central

Gennari, Silvia P.; Mirkovi, Jelena; MacDonald, Maryellen C.

2014-01-01

This work investigates production preferences in different languages. Specifically, it examines how animacy, competition processes, and language-specific constraints shape speakers’ choices of structure. English, Spanish and Serbian speakers were presented with depicted events in which either an animate or inanimate entity was acted upon by an agent. Questions about the affected participant in these events prompted the production of relative clauses identifying these entities (e.g., the bag the woman is punching). Results indicated that in English, animacy plays a strong role in determining the choice of passive structures. In contrast, it plays a less prominent role in Spanish and Serbian structure choices, where more active structures were produced to varying degrees. Critically, the semantic similarity between the agent and the patient of the event correlated with the omission of the agent in all languages, indicating that competition resulted in the agent’s inhibition. Similarity also correlated with different functional choices in Spanish. The results suggest that similarity-based competition may influence various stages of production planning but its manifestations are constrained by language-specific grammatical options. Implications for models of sentence production and the relationship between production and comprehension are discussed. PMID:22537914

Handling knowledge via Concept Maps: a space weather use case

NASA Astrophysics Data System (ADS)

Messerotti, Mauro; Fox, Peter

Concept Maps (Cmaps) are powerful means for knowledge coding in graphical form. As flexible software tools exist to manipulate the knowledge embedded in Cmaps in machine-readable form, such complex entities are suitable candidates not only for the representation of ontologies and semantics in Virtual Observatory (VO) architectures, but also for knowledge handling and knowledge discovery. In this work, we present a use case relevant to space weather applications and we elaborate on its possible implementation and adavanced use in Semantic Virtual Observatories dedicated to Sun-Earth Connections. This analysis was carried out in the framework of the Electronic Geophysical Year (eGY) and represents an achievement synergized by the eGY Virtual Observatories Working Group.

Semi-automated ontology generation and evolution

NASA Astrophysics Data System (ADS)

Stirtzinger, Anthony P.; Anken, Craig S.

2009-05-01

Extending the notion of data models or object models, ontology can provide rich semantic definition not only to the meta-data but also to the instance data of domain knowledge, making these semantic definitions available in machine readable form. However, the generation of an effective ontology is a difficult task involving considerable labor and skill. This paper discusses an Ontology Generation and Evolution Processor (OGEP) aimed at automating this process, only requesting user input when un-resolvable ambiguous situations occur. OGEP directly attacks the main barrier which prevents automated (or self learning) ontology generation: the ability to understand the meaning of artifacts and the relationships the artifacts have to the domain space. OGEP leverages existing lexical to ontological mappings in the form of WordNet, and Suggested Upper Merged Ontology (SUMO) integrated with a semantic pattern-based structure referred to as the Semantic Grounding Mechanism (SGM) and implemented as a Corpus Reasoner. The OGEP processing is initiated by a Corpus Parser performing a lexical analysis of the corpus, reading in a document (or corpus) and preparing it for processing by annotating words and phrases. After the Corpus Parser is done, the Corpus Reasoner uses the parts of speech output to determine the semantic meaning of a word or phrase. The Corpus Reasoner is the crux of the OGEP system, analyzing, extrapolating, and evolving data from free text into cohesive semantic relationships. The Semantic Grounding Mechanism provides a basis for identifying and mapping semantic relationships. By blending together the WordNet lexicon and SUMO ontological layout, the SGM is given breadth and depth in its ability to extrapolate semantic relationships between domain entities. The combination of all these components results in an innovative approach to user assisted semantic-based ontology generation. This paper will describe the OGEP technology in the context of the architectural components referenced above and identify a potential technology transition path to Scott AFB's Tanker Airlift Control Center (TACC) which serves as the Air Operations Center (AOC) for the Air Mobility Command (AMC).

The Cost of Switching Language in a Semantic Categorization Task.

ERIC Educational Resources Information Center

von Studnitz, Roswitha E.; Green, David W.

2002-01-01

Presents a study in which German-English bilinguals decided whether a visually presented word, either German or English, referred to an animate or to an inanimate entity. Bilinguals were slower to respond on a language switch trial than on language non-switch trials but only if they had to make the same response as on the prior trial. (Author/VWL)

[Developmental amnesia as a focal cognitive sequela of a neonatal pathology].

PubMed

Sans, Anna; Colomé, Roser; López-Sala, Anna; Boix, Cristina; Muchart, Jordi; Rebollo, Mónica; Guitet, Montse; Callejón-Póo, Laura; Campistol, Jaume

2011-03-01

The developmental amnesia is a recently known entity that occurs as a consequence of hypoxic-ischemic events in the perinatal period. This is a specific deficit of episodic memory with greater preservation of semantic memory and other memory components such as the immediate and working memory. It occurs in patients without apparent neurological sequelae, with normal psychomotor development and general intelligence. The developmental amnesia has been associated with bilateral involvement of the hippocampus, which is evident in some cases on magnetic resonance imaging (MRI) as signal disturbance and signs of atrophy, or reduced size of the hippocampus in brain volumetric studies. We present six observations of developmental amnesia, their clinical, neuropsychological and neuroimaging findings. All of them show impaired episodic memory with preservation of semantic memory, have a normal general intelligence and follow a regular school with special educational needs. It is necessary to keep in mind this entity in monitoring risk newborns by their perinatal history and include the exploration of memory in neuropsychological study of these subjects. On the other hand, we highlight the specificity of the clinical and neuropsychological profile for the diagnosis of developmental amnesia even in the absence of hippocampal lesions on conventional MRI.

A bibliometric and visual analysis of global geo-ontology research

NASA Astrophysics Data System (ADS)

Li, Lin; Liu, Yu; Zhu, Haihong; Ying, Shen; Luo, Qinyao; Luo, Heng; Kuai, Xi; Xia, Hui; Shen, Hang

2017-02-01

In this paper, the results of a bibliometric and visual analysis of geo-ontology research articles collected from the Web of Science (WOS) database between 1999 and 2014 are presented. The numbers of national institutions and published papers are visualized and a global research heat map is drawn, illustrating an overview of global geo-ontology research. In addition, we present a chord diagram of countries and perform a visual cluster analysis of a knowledge co-citation network of references, disclosing potential academic communities and identifying key points, main research areas, and future research trends. The International Journal of Geographical Information Science, Progress in Human Geography, and Computers & Geosciences are the most active journals. The USA makes the largest contributions to geo-ontology research by virtue of its highest numbers of independent and collaborative papers, and its dominance was also confirmed in the country chord diagram. The majority of institutions are in the USA, Western Europe, and Eastern Asia. Wuhan University, University of Munster, and the Chinese Academy of Sciences are notable geo-ontology institutions. Keywords such as "Semantic Web," "GIS," and "space" have attracted a great deal of attention. "Semantic granularity in ontology-driven geographic information systems, "Ontologies in support of activities in geographical space" and "A translation approach to portable ontology specifications" have the highest cited centrality. Geographical space, computer-human interaction, and ontology cognition are the three main research areas of geo-ontology. The semantic mismatch between the producers and users of ontology data as well as error propagation in interdisciplinary and cross-linguistic data reuse needs to be solved. In addition, the development of geo-ontology modeling primitives based on OWL (Web Ontology Language）and finding methods to automatically rework data in Semantic Web are needed. Furthermore, the topological relations between geographical entities still require further study.

Discovering semantic features in the literature: a foundation for building functional associations

PubMed Central

Chagoyen, Monica; Carmona-Saez, Pedro; Shatkay, Hagit; Carazo, Jose M; Pascual-Montano, Alberto

2006-01-01

Background Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. Results We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. Conclusion The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data. PMID:16438716

A Joint Investigation of Semantic Facilitation and Semantic Interference in Continuous Naming

ERIC Educational Resources Information Center

Scaltritti, Michele; Peressotti, Francesca; Navarrete, Eduardo

2017-01-01

When speakers name multiple semantically related items, opposing effects can be found. Semantic facilitation is found when naming 2 semantically related items in a row. In contrast, semantic interference is found when speakers name semantically related items separated by 1 or more intervening unrelated items. This latter form of interference is…

Terminology for Neuroscience Data Discovery: Multi-tree Syntax and Investigator-Derived Semantics

PubMed Central

Goldberg, David H.; Grafstein, Bernice; Robert, Adrian; Gardner, Esther P.

2009-01-01

The Neuroscience Information Framework (NIF), developed for the NIH Blueprint for Neuroscience Research and available at http://nif.nih.gov and http://neurogateway.org, is built upon a set of coordinated terminology components enabling data and web-resource description and selection. Core NIF terminologies use a straightforward syntax designed for ease of use and for navigation by familiar web interfaces, and readily exportable to aid development of relational-model databases for neuroscience data sharing. Datasets, data analysis tools, web resources, and other entities are characterized by multiple descriptors, each addressing core concepts, including data type, acquisition technique, neuroanatomy, and cell class. Terms for each concept are organized in a tree structure, providing is-a and has-a relations. Broad general terms near each root span the category or concept and spawn more detailed entries for specificity. Related but distinct concepts (e.g., brain area and depth) are specified by separate trees, for easier navigation than would be required by graph representation. Semantics enabling NIF data discovery were selected at one or more workshops by investigators expert in particular systems (vision, olfaction, behavioral neuroscience, neurodevelopment), brain areas (cerebellum, thalamus, hippocampus), preparations (molluscs, fly), diseases (neurodegenerative disease), or techniques (microscopy, computation and modeling, neurogenetics). Workshop-derived integrated term lists are available Open Source at http://brainml.org; a complete list of participants is at http://brainml.org/workshops. PMID:18958630

BioTextQuest: a web-based biomedical text mining suite for concept discovery.

PubMed

Papanikolaou, Nikolas; Pafilis, Evangelos; Nikolaou, Stavros; Ouzounis, Christos A; Iliopoulos, Ioannis; Promponas, Vasilis J

2011-12-01

BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. http://biotextquest.biol.ucy.ac.cy vprobon@ucy.ac.cy; iliopj@med.uoc.gr Supplementary data are available at Bioinformatics online.

Entrez Neuron RDFa: a pragmatic Semantic Web application for data integration in neuroscience research

PubMed Central

Samwald, Matthias; Lim, Ernest; Masiar, Peter; Marenco, Luis; Chen, Huajun; Morse, Thomas; Mutalik, Pradeep; Shepherd, Gordon; Miller, Perry; Cheung, Kei-Hoi

2013-01-01

The amount of biomedical data available in Semantic Web formats has been rapidly growing in recent years. While these formats are machine-friendly, user-friendly web interfaces allowing easy querying of these data are typically lacking. We present “Entrez Neuron”, a pilot neuron-centric interface that allows for keyword-based queries against a coherent repository of OWL ontologies. These ontologies describe neuronal structures, physiology, mathematical models and microscopy images. The returned query results are organized hierarchically according to brain architecture. Where possible, the application makes use of entities from the Open Biomedical Ontologies (OBO) and the ‘HCLS knowledgebase’ developed by the W3C Interest Group for Health Care and Life Science. It makes use of the emerging RDFa standard to embed ontology fragments and semantic annotations within its HTML-based user interface. The application and underlying ontologies demonstrates how Semantic Web technologies can be used for information integration within a curated information repository and between curated information repositories. It also demonstrates how information integration can be accomplished on the client side, through simple copying and pasting of portions of documents that contain RDFa markup. PMID:19745321

Standard Biological Parts Knowledgebase

PubMed Central

Galdzicki, Michal; Rodriguez, Cesar; Chandran, Deepak; Sauro, Herbert M.; Gennari, John H.

2011-01-01

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate “promoter” parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible. PMID:21390321

Characterizing semantic mappings adaptation via biomedical KOS evolution: a case study investigating SNOMED CT and ICD.

PubMed

Dos Reis, Julio Cesar; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

2013-01-01

Mappings established between Knowledge Organization Systems (KOS) increase semantic interoperability between biomedical information systems. However, biomedical knowledge is highly dynamic and changes affecting KOS entities can potentially invalidate part or the totality of existing mappings. Understanding how mappings evolve and what the impacts of KOS evolution on mappings are is therefore crucial for the definition of an automatic approach to maintain mappings valid and up-to-date over time. In this article, we study variations of a specific KOS complex change (split) for two biomedical KOS (SNOMED CT and ICD-9-CM) through a rigorous method of investigation for identifying and refining complex changes, and for selecting representative cases. We empirically analyze and explain their influence on the evolution of associated mappings. Results point out the importance of considering various dimensions of the information described in KOS, like the semantic structure of concepts, the set of relevant information used to define the mappings and the change operations interfering with this set of information.

Characterizing Semantic Mappings Adaptation via Biomedical KOS Evolution: A Case Study Investigating SNOMED CT and ICD

PubMed Central

Reis, Julio Cesar Dos; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

2013-01-01

Mappings established between Knowledge Organization Systems (KOS) increase semantic interoperability between biomedical information systems. However, biomedical knowledge is highly dynamic and changes affecting KOS entities can potentially invalidate part or the totality of existing mappings. Understanding how mappings evolve and what the impacts of KOS evolution on mappings are is therefore crucial for the definition of an automatic approach to maintain mappings valid and up-to-date over time. In this article, we study variations of a specific KOS complex change (split) for two biomedical KOS (SNOMED CT and ICD-9-CM) through a rigorous method of investigation for identifying and refining complex changes, and for selecting representative cases. We empirically analyze and explain their influence on the evolution of associated mappings. Results point out the importance of considering various dimensions of the information described in KOS, like the semantic structure of concepts, the set of relevant information used to define the mappings and the change operations interfering with this set of information. PMID:24551341

Spatial coding of object typical size: evidence for a SNARC-like effect.

PubMed

Sellaro, Roberta; Treccani, Barbara; Job, Remo; Cubelli, Roberto

2015-11-01

The present study aimed to assess whether the representation of the typical size of objects can interact with response position codes in two-choice bimanual tasks, and give rise to a SNARC-like effect (faster responses when the representation of the typical size of the object to which the target stimulus refers corresponds to response side). Participants performed either a magnitude comparison task (in which they were required to judge whether the target was smaller or larger than a reference stimulus; Experiment 1) or a semantic decision task (in which they had to classify the target as belonging to either the category of living or non-living entities; Experiment 2). Target stimuli were pictures or written words referring to either typically large and small animals or inanimate objects. In both tasks, participants responded by pressing a left- or right-side button. Results showed that, regardless of the to-be-performed task (magnitude comparison or semantic decision) and stimulus format (picture or word), left responses were faster when the target represented typically small-sized entities, whereas right responses were faster for typically large-sized entities. These results provide evidence that the information about the typical size of objects is activated even if it is not requested by the task, and are consistent with the idea that objects' typical size is automatically spatially coded, as has been proposed to occur for number magnitudes. In this representation, small objects would be on the left and large objects would be on the right. Alternative interpretations of these results are also discussed.

The Semantic Web: From Representation to Realization

NASA Astrophysics Data System (ADS)

Thórisson, Kristinn R.; Spivack, Nova; Wissner, James M.

A semantically-linked web of electronic information - the Semantic Web - promises numerous benefits including increased precision in automated information sorting, searching, organizing and summarizing. Realizing this requires significantly more reliable meta-information than is readily available today. It also requires a better way to represent information that supports unified management of diverse data and diverse Manipulation methods: from basic keywords to various types of artificial intelligence, to the highest level of intelligent manipulation - the human mind. How this is best done is far from obvious. Relying solely on hand-crafted annotation and ontologies, or solely on artificial intelligence techniques, seems less likely for success than a combination of the two. In this paper describe an integrated, complete solution to these challenges that has already been implemented and tested with hundreds of thousands of users. It is based on an ontological representational level we call SemCards that combines ontological rigour with flexible user interface constructs. SemCards are machine- and human-readable digital entities that allow non-experts to create and use semantic content, while empowering machines to better assist and participate in the process. SemCards enable users to easily create semantically-grounded data that in turn acts as examples for automation processes, creating a positive iterative feedback loop of metadata creation and refinement between user and machine. They provide a holistic solution to the Semantic Web, supporting powerful management of the full lifecycle of data, including its creation, retrieval, classification, sorting and sharing. We have implemented the SemCard technology on the semantic Web site Twine.com, showing that the technology is indeed versatile and scalable. Here we present the key ideas behind SemCards and describe the initial implementation of the technology.

Eye-Tracking and Corpus-Based Analyses of Syntax-Semantics Interactions in Complement Coercion

PubMed Central

Lowder, Matthew W.; Gordon, Peter C.

2016-01-01

Previous work has shown that the difficulty associated with processing complex semantic expressions is reduced when the critical constituents appear in separate clauses as opposed to when they appear together in the same clause. We investigated this effect further, focusing in particular on complement coercion, in which an event-selecting verb (e.g., began) combines with a complement that represents an entity (e.g., began the memo). Experiment 1 compared reading times for coercion versus control expressions when the critical verb and complement appeared together in a subject-extracted relative clause (SRC) (e.g., The secretary that began/wrote the memo) compared to when they appeared together in a simple sentence. Readers spent more time processing coercion expressions than control expressions, replicating the typical coercion cost. In addition, readers spent less time processing the verb and complement in SRCs than in simple sentences; however, the magnitude of the coercion cost did not depend on sentence structure. In contrast, Experiment 2 showed that the coercion cost was reduced when the complement appeared as the head of an object-extracted relative clause (ORC) (e.g., The memo that the secretary began/wrote) compared to when the constituents appeared together in an SRC. Consistent with the eye-tracking results of Experiment 2, a corpus analysis showed that expressions requiring complement coercion are more frequent when the constituents are separated by the clause boundary of an ORC compared to when they are embedded together within an SRC. The results provide important information about the types of structural configurations that contribute to reduced difficulty with complex semantic expressions, as well as how these processing patterns are reflected in naturally occurring language. PMID:28529960

Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2.

PubMed

Mi, Huaiyu; Schreiber, Falk; Moodie, Stuart; Czauderna, Tobias; Demir, Emek; Haw, Robin; Luna, Augustin; Le Novère, Nicolas; Sorokin, Anatoly; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Desiderata for ontologies to be used in semantic annotation of biomedical documents.

PubMed

Bada, Michael; Hunter, Lawrence

2011-02-01

A wealth of knowledge valuable to the translational research scientist is contained within the vast biomedical literature, but this knowledge is typically in the form of natural language. Sophisticated natural-language-processing systems are needed to translate text into unambiguous formal representations grounded in high-quality consensus ontologies, and these systems in turn rely on gold-standard corpora of annotated documents for training and testing. To this end, we are constructing the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-text biomedical journal articles that are being manually annotated with the entire sets of terms from select vocabularies, predominantly from the Open Biomedical Ontologies (OBO) library. Our efforts in building this corpus has illuminated infelicities of these ontologies with respect to the semantic annotation of biomedical documents, and we propose desiderata whose implementation could substantially improve their utility in this task; these include the integration of overlapping terms across OBOs, the resolution of OBO-specific ambiguities, the integration of the BFO with the OBOs and the use of mid-level ontologies, the inclusion of noncanonical instances, and the expansion of relations and realizable entities. Copyright © 2010 Elsevier Inc. All rights reserved.

[Schizophrenia and semantic priming effects].

PubMed

Lecardeur, L; Giffard, B; Eustache, F; Dollfus, S

2006-01-01

This article is a review of studies using the semantic priming paradigm to assess the functioning of semantic memory in schizophrenic patients. Semantic priming describes the phenomenon of increasing the speed with which a string of letters (the target) is recognized as a word (lexical decision task) by presenting to the subject a semantically related word (the prime) prior to the appearance of the target word. This semantic priming is linked to both automatic and controlled processes depending on experimental conditions (stimulus onset asynchrony (SOA), percentage of related words and explicit memory instructions). Automatic process observed with short SOA, low related word percentage and instructions asking only to process the target, could be linked to the "automatic spreading activation" through the semantic network. Controlled processes involve "semantic matching" (the number of related and unrelated pairs influences the subjects decision) and "expectancy" (the prime leads the subject to generate an expectancy set of potential target to the prime). These processes can be observed whatever the SOA for the former and with long SOA for the later, but both with only high related word percentage and explicit memory instructions. Studies evaluating semantic priming effects in schizophrenia show conflicting results: schizophrenic patients can present hyperpriming (semantic priming effect is larger in patients than in controls), hypopriming (semantic priming effect is lower in patients than in controls) or equal semantic priming effects compared to control subjects. These results could be associated to a global impairment of controlled processes in schizophrenia, essentially to a dysfunction of semantic matching process. On the other hand, efficiency of semantic automatic spreading activation process is controversial. These discrepancies could be linked to the different experimental conditions used (duration of SOA, proportion of related pairs and instructions), which influence on the degree of involvement of controlled processes and therefore prevent to really assess its functioning. In addition, manipulations of the relation between prime and target (semantic distance, type of semantic relation and strength of semantic relation) seem to influence reaction times. However, the relation between prime and target (mediated priming) frequently used could not be the most relevant relation to understand the way of spreading of activation in semantic network in patients with schizophrenia. Finally, patients with formal thought disorders present particularly high priming effects relative to controls. These abnormal semantic priming effects could reflect a dysfunction of automatic spreading activation process and consequently an exaggerated diffusion of activation in the semantic network. In the future, the inclusion of different groups schizophrenic subjects could allow us to determine whether semantic memory disorders are pathognomonic or specific of a particular group of patients with schizophrenia.

Motivation and Organizational Principles for Anatomical Knowledge Representation

PubMed Central

Rosse, Cornelius; Mejino, José L.; Modayur, Bharath R.; Jakobovits, Rex; Hinshaw, Kevin P.; Brinkley, James F.

1998-01-01

Abstract Objective: Conceptualization of the physical objects and spaces that constitute the human body at the macroscopic level of organization, specified as a machine-parseable ontology that, in its human-readable form, is comprehensible to both expert and novice users of anatomical information. Design: Conceived as an anatomical enhancement of the UMLS Semantic Network and Metathesaurus, the anatomical ontology was formulated by specifying defining attributes and differentia for classes and subclasses of physical anatomical entities based on their partitive and spatial relationships. The validity of the classification was assessed by instantiating the ontology for the thorax. Several transitive relationships were used for symbolically modeling aspects of the physical organization of the thorax. Results: By declaring Organ as the macroscopic organizational unit of the body, and defining the entities that constitute organs and higher level entities constituted by organs, all anatomical entities could be assigned to one of three top level classes (Anatomical structure, Anatomical spatial entity and Body substance). The ontology accommodates both the systemic and regional (topographical) views of anatomy, as well as diverse clinical naming conventions of anatomical entities. Conclusions: The ontology formulated for the thorax is extendible to microscopic and cellular levels, as well as to other body parts, in that its classes subsume essentially all anatomical entities that constitute the body. Explicit definitions of these entities and their relationships provide the first requirement for standards in anatomical concept representation. Conceived from an anatomical viewpoint, the ontology can be generalized and mapped to other biomedical domains and problem solving tasks that require anatomical knowledge. PMID:9452983

Handling or being the concept: An fMRI study on metonymy representations in coverbal gestures.

PubMed

Joue, Gina; Boven, Linda; Willmes, Klaus; Evola, Vito; Demenescu, Liliana R; Hassemer, Julius; Mittelberg, Irene; Mathiak, Klaus; Schneider, Frank; Habel, Ute

2018-01-31

In "Two heads are better than one," "head" stands for people and focuses the message on the intelligence of people. This is an example of figurative language through metonymy, where substituting a whole entity by one of its parts focuses attention on a specific aspect of the entity. Whereas metaphors, another figurative language device, are substitutions based on similarity, metonymy involves substitutions based on associations. Both are figures of speech but are also expressed in coverbal gestures during multimodal communication. The closest neuropsychological studies of metonymy in gestures have been nonlinguistic tool-use, illustrated by the classic apraxic problem of body-part-as-object (BPO, equivalent to an internal metonymy representation of the tool) vs. pantomimed action (external metonymy representation of the absent object/tool). Combining these research domains with concepts in cognitive linguistic research on gestures, we conducted an fMRI study to investigate metonymy resolution in coverbal gestures. Given the greater difficulty in developmental and apraxia studies, perhaps explained by the more complex semantic inferencing involved for external metonymy than for internal metonymy representations, we hypothesized that external metonymy resolution requires greater processing demands and that the neural resources supporting metonymy resolution would modulate regions involved in semantic processing. We found that there are indeed greater activations for external than for internal metonymy resolution in the temporoparietal junction (TPJ). This area is posterior to the lateral temporal regions recruited by metaphor processing. Effective connectivity analysis confirmed our hypothesis that metonymy resolution modulates areas implicated in semantic processing. We interpret our results in an interdisciplinary view of what metonymy in action can reveal about abstract cognition. Copyright © 2017 Elsevier Ltd. All rights reserved.

Input that Contradicts Young Children's Strategy for Mapping Novel Words Affects Their Phonological and Semantic Interpretation of Other Novel Words.

ERIC Educational Resources Information Center

Jarvis, Lorna Hernandez; Merriman, William E.; Barnett, Michelle; Hanba, Jessica; Van Haitsma, Kylee S.

2004-01-01

Children tend to choose an entity they cannot already label, rather than one they can, as the likely referent of a novel noun. The effect of input that contradicts this strategy on the interpretation of other novel nouns was investigated. In pre- and posttests, 4-year-olds were asked to judge whether novel nouns referred to "name-similar" familiar…

Connecting geoscience systems and data using Linked Open Data in the Web of Data

NASA Astrophysics Data System (ADS)

Ritschel, Bernd; Neher, Günther; Iyemori, Toshihiko; Koyama, Yukinobu; Yatagai, Akiyo; Murayama, Yasuhiro; Galkin, Ivan; King, Todd; Fung, Shing F.; Hughes, Steve; Habermann, Ted; Hapgood, Mike; Belehaki, Anna

2014-05-01

Linked Data or Linked Open Data (LOD) in the realm of free and publically accessible data is one of the most promising and most used semantic Web frameworks connecting various types of data and vocabularies including geoscience and related domains. The semantic Web extension to the commonly existing and used World Wide Web is based on the meaning of entities and relationships or in different words classes and properties used for data in a global data and information space, the Web of Data. LOD data is referenced and mash-uped by URIs and is retrievable using simple parameter controlled HTTP-requests leading to a result which is human-understandable or machine-readable. Furthermore the publishing and mash-up of data in the semantic Web realm is realized by specific Web standards, such as RDF, RDFS, OWL and SPARQL defined for the Web of Data. Semantic Web based mash-up is the Web method to aggregate and reuse various contents from different sources, such as e.g. using FOAF as a model and vocabulary for the description of persons and organizations -in our case- related to geoscience projects, instruments, observations, data and so on. On the example of three different geoscience data and information management systems, such as ESPAS, IUGONET and GFZ ISDC and the associated science data and related metadata or better called context data, the concept of the mash-up of systems and data using the semantic Web approach and the Linked Open Data framework is described in this publication. Because the three systems are based on different data models, data storage structures and technical implementations an extra semantic Web layer upon the existing interfaces is used for mash-up solutions. In order to satisfy the semantic Web standards, data transition processes, such as the transfer of content stored in relational databases or mapped in XML documents into SPARQL capable databases or endpoints using D2R or XSLT is necessary. In addition, the use of mapped and/or merged domain specific and cross-domain vocabularies in the sense of terminological ontologies are the foundation for a virtually unified data retrieval and access in IUGONET, ESPAS and GFZ ISDC data management systems. SPARQL endpoints realized either by originally RDF databases, e.g. Virtuoso or by virtual SPARQL endpoints, e.g. D2R services enable an only upon Web standard-based mash-up of domain-specific systems and data, such as in this case the space weather and geomagnetic domain but also cross-domain connection to data and vocabularies, e.g. related to NASA's VxOs, particularly VWO or NASA's PDS data system within LOD. LOD - Linked Open Data RDF - Resource Description Framework RDFS - RDF Schema OWL - Ontology Web Language SPARQL - SPARQL Protocol and RDF Query Language FOAF - Friends of a Friend ontology ESPAS - Near Earth Space Data Infrastructure for e-Science (Project) IUGONET - Inter-university Upper Atmosphere Global Observation Network (Project) GFZ ISDC - German Research Centre for Geosciences Information System and Data Center XML - Extensible Mark-up Language D2R - (Relational) Database to RDF (Transformation) XSLT - Extensible Stylesheet Language Transformation Virtuoso - OpenLink Virtuoso Universal Server (including RDF data management) NASA - National Aeronautics and Space Administration VOx - Virtual Observatories VWO - Virtual Wave Observatory PDS - Planetary Data System

KARL: A Knowledge-Assisted Retrieval Language. M.S. Thesis Final Report, 1 Jul. 1985 - 31 Dec. 1987

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Triantafyllopoulos, Spiros

1985-01-01

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems.

Psych verbs, the Linking Problem, and the Acquisition of Language

PubMed Central

Hartshorne, Joshua K.; O'Donnell, Timothy J.; Sudo, Yasutada; Uruwashi, Miki; Lee, Miseon; Snedeker, Jesse

2016-01-01

In acquiring language, children must learn to appropriately place the different participants of an event (e.g., causal agent, affected entity) into the correct syntactic positions (e.g., subject, object) so that listeners will know who did what to whom. While many of these mappings can be characterized by broad generalizations, both within and across languages (e.g., semantic agents tend to be mapped onto syntactic subjects), not all verbs fit neatly into these generalizations. One particularly striking example is verbs of psychological state: The experiencer of the state can appear as either the subject (Agnes fears/hates/loves Bartholomew) or the direct object (Agnes frightens/angers/delights Bartholomew). The present studies explore whether this apparent variability in subject/object mapping may actually result from differences in these verbs’ underlying meanings. Specifically, we suggest that verbs like fear describe a habitual attitude towards some entity whereas verbs like frighten describe an externally caused emotional episode. We find that this distinction systematically characterizes verbs in English, Mandarin, and Korean. This pattern is generalized to novel verbs by adults in English, Japanese, and Russian, and even by English-speaking children who are just beginning to acquire psych verbs. This results support a broad role for systematic mappings between semantics and syntax in language acquisition. PMID:27693942

Semantic and syntactic interoperability in online processing of big Earth observation data.

PubMed

Sudmanns, Martin; Tiede, Dirk; Lang, Stefan; Baraldi, Andrea

2018-01-01

The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

Semantic and syntactic interoperability in online processing of big Earth observation data

PubMed Central

Sudmanns, Martin; Tiede, Dirk; Lang, Stefan; Baraldi, Andrea

2018-01-01

ABSTRACT The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover). PMID:29387171

Early sensitivity of left perisylvian cortex to relationality in nouns and verbs.

PubMed

Williams, Adina; Reddigari, Samir; Pylkkänen, Liina

2017-06-01

The ability to track the relationality of concepts, i.e., their capacity to encode a relationship between entities, is one of the core semantic abilities humans possess. In language processing, we systematically leverage this ability when computing verbal argument structure, in order to link participants to the events they participate in. Previous work has converged on a large region of left posterior perisylvian cortex as a locus for such processing, but the wide range of experimental stimuli and manipulations has yielded an unclear picture of the region's exact role(s). Importantly, there is a tendency for effects of relationality in single-word studies to localize to posterior temporo-parietal cortex, while argument structure effects in sentences appear in left superior temporal cortex. To characterize these sensitivities, we designed two MEG experiments that cross the factors relationality and eventivity. The first used minimal noun phrases and tested for an effect of semantic composition, while the second employed full sentences and a manipulation of grammatical category. The former identified a region of the left inferior parietal lobe sensitive to relationality, but not eventivity or combination, beginning at 170ms. The latter revealed a similarly-timed effect of relationality in left mid-superior temporal cortex, independent of eventivity and category. The results suggest that i) multiple sub-regions of perisylvian cortex are sensitive to the relationality carried by concepts even in the absence of arguments, ii) linguistic context modulates the locus of this sensitivity, consistent with prior studies, and iii) relationality information is accessed early - before 200ms - regardless of the concept's event status or syntactic category. Copyright © 2017 Elsevier Ltd. All rights reserved.

Physical properties of biological entities: an introduction to the ontology of physics for biology.

PubMed

Cook, Daniel L; Bookstein, Fred L; Gennari, John H

2011-01-01

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration. © 2011 Cook et al.

Relations between Short-term Memory Deficits, Semantic Processing, and Executive Function

PubMed Central

Allen, Corinne M.; Martin, Randi C.; Martin, Nadine

2012-01-01

Background Previous research has suggested separable short-term memory (STM) buffers for the maintenance of phonological and lexical-semantic information, as some patients with aphasia show better ability to retain semantic than phonological information and others show the reverse. Recently, researchers have proposed that deficits to the maintenance of semantic information in STM are related to executive control abilities. Aims The present study investigated the relationship of executive function abilities with semantic and phonological short-term memory (STM) and semantic processing in such patients, as some previous research has suggested that semantic STM deficits and semantic processing abilities are critically related to specific or general executive function deficits. Method and Procedures 20 patients with aphasia and STM deficits were tested on measures of short-term retention, semantic processing, and both complex and simple executive function tasks. Outcome and Results In correlational analyses, we found no relation between semantic STM and performance on simple or complex executive function tasks. In contrast, phonological STM was related to executive function performance in tasks that had a verbal component, suggesting that performance in some executive function tasks depends on maintaining or rehearsing phonological codes. Although semantic STM was not related to executive function ability, performance on semantic processing tasks was related to executive function, perhaps due to similar executive task requirements in both semantic processing and executive function tasks. Conclusions Implications for treatment and interpretations of executive deficits are discussed. PMID:22736889

The chemical information ontology: provenance and disambiguation for chemical data on the biological semantic web.

PubMed

Hastings, Janna; Chepelev, Leonid; Willighagen, Egon; Adams, Nico; Steinbeck, Christoph; Dumontier, Michel

2011-01-01

Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA).

The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web

PubMed Central

Hastings, Janna; Chepelev, Leonid; Willighagen, Egon; Adams, Nico; Steinbeck, Christoph; Dumontier, Michel

2011-01-01

Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA). PMID:21991315

MMKG: An approach to generate metallic materials knowledge graph based on DBpedia and Wikipedia

NASA Astrophysics Data System (ADS)

Zhang, Xiaoming; Liu, Xin; Li, Xin; Pan, Dongyu

2017-02-01

The research and development of metallic materials are playing an important role in today's society, and in the meanwhile lots of metallic materials knowledge is generated and available on the Web (e.g., Wikipedia) for materials experts. However, due to the diversity and complexity of metallic materials knowledge, the knowledge utilization may encounter much inconvenience. The idea of knowledge graph (e.g., DBpedia) provides a good way to organize the knowledge into a comprehensive entity network. Therefore, the motivation of our work is to generate a metallic materials knowledge graph (MMKG) using available knowledge on the Web. In this paper, an approach is proposed to build MMKG based on DBpedia and Wikipedia. First, we use an algorithm based on directly linked sub-graph semantic distance (DLSSD) to preliminarily extract metallic materials entities from DBpedia according to some predefined seed entities; then based on the results of the preliminary extraction, we use an algorithm, which considers both semantic distance and string similarity (SDSS), to achieve the further extraction. Second, due to the absence of materials properties in DBpedia, we use an ontology-based method to extract properties knowledge from the HTML tables of corresponding Wikipedia Web pages for enriching MMKG. Materials ontology is used to locate materials properties tables as well as to identify the structure of the tables. The proposed approach is evaluated by precision, recall, F1 and time performance, and meanwhile the appropriate thresholds for the algorithms in our approach are determined through experiments. The experimental results show that our approach returns expected performance. A tool prototype is also designed to facilitate the process of building the MMKG as well as to demonstrate the effectiveness of our approach.

Experiments using Semantic Web technologies to connect IUGONET, ESPAS and GFZ ISDC data portals

NASA Astrophysics Data System (ADS)

Ritschel, Bernd; Borchert, Friederike; Kneitschel, Gregor; Neher, Günther; Schildbach, Susanne; Iyemori, Toshihiko; Koyama, Yukinobu; Yatagai, Akiyo; Hori, Tomoaki; Hapgood, Mike; Belehaki, Anna; Galkin, Ivan; King, Todd

2016-11-01

E-science on the Web plays an important role and offers the most advanced technology for the integration of data systems. It also makes available data for the research of more and more complex aspects of the system earth and beyond. The great number of e-science projects founded by the European Union (EU), university-driven Japanese efforts in the field of data services and institutional anchored developments for the enhancement of a sustainable data management in Germany are proof of the relevance and acceptance of e-science or cyberspace-based applications as a significant tool for successful scientific work. The collaboration activities related to near-earth space science data systems and first results in the field of information science between the EU-funded project ESPAS, the Japanese IUGONET project and the GFZ ISDC-based research and development activities are the focus of this paper. The main objective of the collaboration is the use of a Semantic Web approach for the mashup of the project related and so far inoperable data systems. Both the development and use of mapped and/or merged geo and space science controlled vocabularies and the connection of entities in ontology-based domain data model are addressed. The developed controlled vocabularies for the description of geo and space science data and related context information as well as the domain ontologies itself with their domain and cross-domain relationships will be published in Linked Open Data.[Figure not available: see fulltext.

Associated impairment of the categories of conspecifics and biological entities: cognitive and neuroanatomical aspects of a new case.

PubMed

Capitani, Erminio; Chieppa, Francesca; Laiacona, Marcella

2010-05-01

Case A.C.A. presented an associated impairment of visual recognition and semantic knowledge for celebrities and biological objects. This case was relevant for (a) the neuroanatomical correlations, and (b) the relationship between visual recognition and semantics within the biological domain and the conspecifics domain. A.C.A. was not affected by anterior temporal damage. Her bilateral vascular lesions were localized on the medial and inferior temporal gyrus on the right and on the intermediate fusiform gyrus on the left, without concomitant lesions of the parahippocampal gyrus or posterior fusiform. Data analysis was based on a novel methodology developed to estimate the rate of stored items in the visual structural description system (SDS) or in the face recognition unit. For each biological object, no particular correlation was found between the visual information accessed through the semantic system and that tapped by the picture reality judgement. Findings are discussed with reference to whether a putative resource commonality is likely between biological objects and conspecifics, and whether or not either category may depend on an exclusive neural substrate.

Knowledge Reasoning with Semantic Data for Real-Time Data Processing in Smart Factory

PubMed Central

Wang, Shiyong; Li, Di; Liu, Chengliang

2018-01-01

The application of high-bandwidth networks and cloud computing in manufacturing systems will be followed by mass data. Industrial data analysis plays important roles in condition monitoring, performance optimization, flexibility, and transparency of the manufacturing system. However, the currently existing architectures are mainly for offline data analysis, not suitable for real-time data processing. In this paper, we first define the smart factory as a cloud-assisted and self-organized manufacturing system in which physical entities such as machines, conveyors, and products organize production through intelligent negotiation and the cloud supervises this self-organized process for fault detection and troubleshooting based on data analysis. Then, we propose a scheme to integrate knowledge reasoning and semantic data where the reasoning engine processes the ontology model with real time semantic data coming from the production process. Based on these ideas, we build a benchmarking system for smart candy packing application that supports direct consumer customization and flexible hybrid production, and the data are collected and processed in real time for fault diagnosis and statistical analysis. PMID:29415444

Systems Biology Graphical Notation: Process Description language Level 1 Version 1.3.

PubMed

Moodie, Stuart; Le Novère, Nicolas; Demir, Emek; Mi, Huaiyu; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Process Description language represents biological entities and processes between these entities within a network. SBGN PD focuses on the mechanistic description and temporal dependencies of biological interactions and transformations. The nodes (elements) are split into entity nodes describing, e.g., metabolites, proteins, genes and complexes, and process nodes describing, e.g., reactions and associations. The edges (connections) provide descriptions of relationships (or influences) between the nodes, such as consumption, production, stimulation and inhibition. Among all three languages of SBGN, PD is the closest to metabolic and regulatory pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Patent Retrieval in Chemistry based on Semantically Tagged Named Entities

DTIC Science & Technology

2009-11-01

their corresponding synonyms. An ex- ample query for TS-15 is: (" Betaine " OR "Glycine betaine " OR "Glycocol betaine " OR "Glycylbetaine" OR ...) AND...task in an automatic way based on noun- phrase detection incorporating the OpenNLP chun- 3 Informative Term Synonyms Source Betaine Glycine betaine ...Glycocol betaine , Glycylbetaine etc. ATC Peripheral Artery Disease Peripheral Artery Disorder, Peripheral Arterial Disease etc. MeSH Diels-Alder reaction

Recognition of human activity characteristics based on state transitions modeling technique

NASA Astrophysics Data System (ADS)

Elangovan, Vinayak; Shirkhodaie, Amir

2012-06-01

Human Activity Discovery & Recognition (HADR) is a complex, diverse and challenging task but yet an active area of ongoing research in the Department of Defense. By detecting, tracking, and characterizing cohesive Human interactional activity patterns, potential threats can be identified which can significantly improve situation awareness, particularly, in Persistent Surveillance Systems (PSS). Understanding the nature of such dynamic activities, inevitably involves interpretation of a collection of spatiotemporally correlated activities with respect to a known context. In this paper, we present a State Transition model for recognizing the characteristics of human activities with a link to a prior contextbased ontology. Modeling the state transitions between successive evidential events determines the activities' temperament. The proposed state transition model poses six categories of state transitions including: Human state transitions of Object handling, Visibility, Entity-entity relation, Human Postures, Human Kinematics and Distance to Target. The proposed state transition model generates semantic annotations describing the human interactional activities via a technique called Casual Event State Inference (CESI). The proposed approach uses a low cost kinect depth camera for indoor and normal optical camera for outdoor monitoring activities. Experimental results are presented here to demonstrate the effectiveness and efficiency of the proposed technique.

Toward Semantic Interoperability in Home Health Care: Formally Representing OASIS Items for Integration into a Concept-oriented Terminology

PubMed Central

Choi, Jeungok; Jenkins, Melinda L.; Cimino, James J.; White, Thomas M.; Bakken, Suzanne

2005-01-01

Objective: The authors aimed to (1) formally represent OASIS-B1 concepts using the Logical Observation Identifiers, Names, and Codes (LOINC) semantic structure; (2) demonstrate integration of OASIS-B1 concepts into a concept-oriented terminology, the Medical Entities Dictionary (MED); (3) examine potential hierarchical structures within LOINC among OASIS-B1 and other nursing terms; and (4) illustrate a Web-based implementation for OASIS-B1 data entry using Dialogix, a software tool with a set of functions that supports complex data entry. Design and Measurements: Two hundred nine OASIS-B1 items were dissected into the six elements of the LOINC semantic structure and then integrated into the MED hierarchy. Each OASIS-B1 term was matched to LOINC-coded nursing terms, Home Health Care Classification, the Omaha System, and the Sign and Symptom Check-List for Persons with HIV, and the extent of the match was judged based on a scale of 0 (no match) to 4 (exact match). OASIS-B1 terms were implemented as a Web-based survey using Dialogix. Results: Of 209 terms, 204 were successfully dissected into the elements of the LOINC semantics structure and integrated into the MED with minor revisions of MED semantics. One hundred fifty-one OASIS-B1 terms were mapped to one or more of the LOINC-coded nursing terms. Conclusion: The LOINC semantic structure offers a standard way to add home health care data to a comprehensive patient record to facilitate data sharing for monitoring outcomes across sites and to further terminology management, decision support, and accurate information retrieval for evidence-based practice. The cross-mapping results support the possibility of a hierarchical structure of the OASIS-B1 concepts within nursing terminologies in the LOINC database. PMID:15802480

Toward semantic interoperability in home health care: formally representing OASIS items for integration into a concept-oriented terminology.

PubMed

Choi, Jeungok; Jenkins, Melinda L; Cimino, James J; White, Thomas M; Bakken, Suzanne

2005-01-01

The authors aimed to (1) formally represent OASIS-B1 concepts using the Logical Observation Identifiers, Names, and Codes (LOINC) semantic structure; (2) demonstrate integration of OASIS-B1 concepts into a concept-oriented terminology, the Medical Entities Dictionary (MED); (3) examine potential hierarchical structures within LOINC among OASIS-B1 and other nursing terms; and (4) illustrate a Web-based implementation for OASIS-B1 data entry using Dialogix, a software tool with a set of functions that supports complex data entry. Two hundred nine OASIS-B1 items were dissected into the six elements of the LOINC semantic structure and then integrated into the MED hierarchy. Each OASIS-B1 term was matched to LOINC-coded nursing terms, Home Health Care Classification, the Omaha System, and the Sign and Symptom Check-List for Persons with HIV, and the extent of the match was judged based on a scale of 0 (no match) to 4 (exact match). OASIS-B1 terms were implemented as a Web-based survey using Dialogix. Of 209 terms, 204 were successfully dissected into the elements of the LOINC semantics structure and integrated into the MED with minor revisions of MED semantics. One hundred fifty-one OASIS-B1 terms were mapped to one or more of the LOINC-coded nursing terms. The LOINC semantic structure offers a standard way to add home health care data to a comprehensive patient record to facilitate data sharing for monitoring outcomes across sites and to further terminology management, decision support, and accurate information retrieval for evidence-based practice. The cross-mapping results support the possibility of a hierarchical structure of the OASIS-B1 concepts within nursing terminologies in the LOINC database.

Priming effects under correct change detection and change blindness.

PubMed

Caudek, Corrado; Domini, Fulvio

2013-03-01

In three experiments, we investigated the priming effects induced by an image change on a successive animate/inanimate decision task. We studied both perceptual (Experiments 1 and 2) and conceptual (Experiment 3) priming effects, under correct change detection and change blindness (CB). Under correct change detection, we found larger positive priming effects on congruent trials for probes representing animate entities than for probes representing artifactual objects. Under CB, we found performance impairment relative to a "no-change" baseline condition. This inhibition effect induced by CB was modulated by the semantic congruency between the changed item and the probe in the case of probe images, but not for probe words. We discuss our results in the context of the literature on the negative priming effect. Copyright © 2012 Elsevier Inc. All rights reserved.

Searching for the elusive neural substrates of body part terms: a neuropsychological study.

PubMed

Kemmerer, David; Tranel, Daniel

2008-06-01

Previous neuropsychological studies suggest that, compared to other categories of concrete entities, lexical and conceptual aspects of body part knowledge are frequently spared in brain-damaged patients. To further investigate this issue, we administered a battery of 12 tests assessing lexical and conceptual aspects of body part knowledge to 104 brain-damaged patients with lesions distributed throughout the telencephalon. There were two main outcomes. First, impaired oral naming of body parts, attributable to a disturbance of the mapping between lexical-semantic and lexical-phonological structures, was most reliably and specifically associated with lesions in the left frontal opercular and anterior/inferior parietal opercular cortices and in the white matter underlying these regions (8 patients). Also, 1 patient with body part anomia had a left occipital lesion that included the "extrastriate body area" (EBA). Second, knowledge of the meanings of body part terms was remarkably resistant to impairment, regardless of lesion site; in fact, we did not uncover a single patient who exhibited significantly impaired understanding of the meanings of these terms. In the 9 patients with body part anomia, oral naming of concrete entities was evaluated, and this revealed that 4 patients had disproportionately worse naming of body parts relative to other types of concrete entities. Taken together, these findings extend previous neuropsychological and functional neuroimaging studies of body part knowledge and add to our growing understanding of the nuances of how different linguistic and conceptual categories are operated by left frontal and parietal structures.

Streams as Entanglement of Nature and Culture: European Upper Paleolithic River Systems and Their Role as Features of Spatial Organization.

PubMed

Hussain, Shumon T; Floss, Harald

2016-01-01

Large river valleys have long been seen as important factors to shape the mobility, communication, and exchange of Pleistocene hunter-gatherers. However, rivers have been debated as either natural entities people adapt and react to or as cultural and meaningful entities people experience and interpret in different ways. Here, we attempt to integrate both perspectives. Building on theoretical work from various disciplines, we discuss the relationship between biophysical river properties and sociocultural river semantics and suggest that understanding a river's persona is central to evaluating its role in spatial organization. By reviewing the literature and analyzing European Upper Paleolithic site distribution and raw material transfer patterns in relation to river catchments, we show that the role of prominent rivers varies considerably over time. Both ecological and cultural factors are crucial to explaining these patterns. Whereas the Earlier Upper Paleolithic record displays a general tendency toward conceiving rivers as mobility guidelines, the spatial consolidation process after the colonization of the European mainland is paralleled by a trend of conceptualizing river regimes as frontiers, separating archaeological entities, regional groups, or local networks. The Late Upper Paleolithic Magdalenian, however, is characterized again by a role of rivers as mobility and communication vectors. Tracing changing patterns in the role of certain river regimes through time thus contributes to our growing knowledge of human spatial behavior and helps to improve our understanding of dynamic and mutually informed human-environment interactions in the Paleolithic.

Searching for the Elusive Neural Substrates of Body Part Terms: A Neuropsychological Study

PubMed Central

Kemmerer, David; Tranel, Daniel

2010-01-01

Previous neuropsychological studies suggest that, compared to other categories of concrete entities, lexical and conceptual aspects of body part knowledge are frequently spared in brain-damaged patients. To further investigate this issue, we administered a battery of 12 tests assessing lexical and conceptual aspects of body part knowledge to 104 brain-damaged patients with lesions distributed throughout the telencephalon. There were two main outcomes. First, impaired oral naming of body parts, attributable to a disturbance of the mapping between lexical-semantic and lexical-phonological structures, was most reliably and specifically associated with lesions in the left frontal opercular and anterior/inferior parietal opercular cortices, and in the white matter underlying these regions (8 patients). Also, one patient with body part anomia had a left occipital lesion that included the “extrastriate body area” (EBA). Second, knowledge of the meanings of body part terms was remarkably resistant to impairment, regardless of lesion site; in fact, we did not uncover a single patient who exhibited significantly impaired understanding of the meanings of these terms. In the 9 patients with body part anomia, oral naming of concrete entities was evaluated, and this revealed that 4 patients had disproportionately worse naming of body parts relative to other types of concrete entities. Taken together, these findings extend previous neuropsychological and functional neuroimaging studies of body part knowledge, and add to our growing understanding of the nuances of how different linguistic and conceptual categories are operated by left frontal and parietal structures. PMID:18608319

Context Aware Programmable Trackers for the Next Generation Internet

NASA Astrophysics Data System (ADS)

Sousa, Pedro

This work introduces and proposes the concept of context aware programmable trackers for the next generation Internet. The proposed solution gives ground for the development of advanced applications based on the P2P paradigm and will foster collaborative efforts among several network entities (e.g. P2P applications and ISPs). The proposed concept of context aware programmable trackers allows that several peer selection strategies might be supported by a P2P tracker entity able to improve the peer selection decisions according with pre-defined objectives and external inputs provided by specific services. The flexible, adaptive and enhanced peer selection semantics that might be achieved by the proposed solution will contribute for devising novel P2P based services and business models for the future Internet.

A pool of pairs of related objects (POPORO) for investigating visual semantic integration: behavioral and electrophysiological validation.

PubMed

Kovalenko, Lyudmyla Y; Chaumon, Maximilien; Busch, Niko A

2012-07-01

Semantic processing of verbal and visual stimuli has been investigated in semantic violation or semantic priming paradigms in which a stimulus is either related or unrelated to a previously established semantic context. A hallmark of semantic priming is the N400 event-related potential (ERP)--a deflection of the ERP that is more negative for semantically unrelated target stimuli. The majority of studies investigating the N400 and semantic integration have used verbal material (words or sentences), and standardized stimulus sets with norms for semantic relatedness have been published for verbal but not for visual material. However, semantic processing of visual objects (as opposed to words) is an important issue in research on visual cognition. In this study, we present a set of 800 pairs of semantically related and unrelated visual objects. The images were rated for semantic relatedness by a sample of 132 participants. Furthermore, we analyzed low-level image properties and matched the two semantic categories according to these features. An ERP study confirmed the suitability of this image set for evoking a robust N400 effect of semantic integration. Additionally, using a general linear modeling approach of single-trial data, we also demonstrate that low-level visual image properties and semantic relatedness are in fact only minimally overlapping. The image set is available for download from the authors' website. We expect that the image set will facilitate studies investigating mechanisms of semantic and contextual processing of visual stimuli.

Automatic generation of nursing narratives from entity-attribute-value triplet for electronic nursing records system.

PubMed

Min, Yul Ha; Park, Hyeoun-Ae; Lee, Joo Yun; Jo, Soo Jung; Jeon, Eunjoo; Byeon, Namsoo; Choi, Seung Yong; Chung, Eunja

2014-01-01

The aim of this study is to develop and evaluate a natural language generation system to populate nursing narratives using detailed clinical models. Semantic, contextual, and syntactical knowledges were extracted. A natural language generation system linking these knowledges was developed. The quality of generated nursing narratives was evaluated by the three nurse experts using a five-point rating scale. With 82 detailed clinical models, in total 66,888 nursing narratives in four different types of statement were generated. The mean scores for overall quality was 4.66, for content 4.60, for grammaticality 4.40, for writing style 4.13, and for correctness 4.60. The system developed in this study generated nursing narratives with different levels of granularity. The generated nursing narratives can improve semantic interoperability of nursing data documented in nursing records.

On the interdependence of cognition and emotion

PubMed Central

Storbeck, Justin; Clore, Gerald L.

2008-01-01

Affect and cognition have long been treated as independent entities, but in the current review we suggest that affect and cognition are in fact highly interdependent. We open the article by discussing three classic views for the independence of affect. These are (i) the affective independence hypothesis, that emotion is processed independently from cognition, (ii) the affective primacy hypothesis, that evaluative processing precedes semantic processing, and (iii) the affective automaticity hypothesis, that affectively potent stimuli commandeer attention and evaluation is automatic. We argue that affect is not independent from cognition, that affect is not primary to cognition, nor is affect automatically elicited. The second half of the paper discusses several instances of how affect influences cognition. We review experiments showing affective involvement in perception, semantic activation, and attitude activation. We conclude that one function of affect is to regulate cognitive processing. PMID:18458789

Semantic Categories and Context in L2 Vocabulary Learning

ERIC Educational Resources Information Center

Bolger, Patrick; Zapata, Gabriela

2011-01-01

This article extends recent findings that presenting semantically related vocabulary simultaneously inhibits learning. It does so by adding story contexts. Participants learned 32 new labels for known concepts from four different semantic categories in stories that were either semantically related (one category per story) or semantically unrelated…

Semantic encoding and retrieval in the left inferior prefrontal cortex: a functional MRI study of task difficulty and process specificity.

PubMed

Demb, J B; Desmond, J E; Wagner, A D; Vaidya, C J; Glover, G H; Gabrieli, J D

1995-09-01

Prefrontal cortical function was examined during semantic encoding and repetition priming using functional magnetic resonance imaging (fMRI), a noninvasive technique for localizing regional changes in blood oxygenation, a correlate of neural activity. Words studied in a semantic (deep) encoding condition were better remembered than words studied in both easier and more difficult nonsemantic (shallow) encoding conditions, with difficulty indexed by response time. The left inferior prefrontal cortex (LIPC) (Brodmann's areas 45, 46, 47) showed increased activation during semantic encoding relative to nonsemantic encoding regardless of the relative difficulty of the nonsemantic encoding task. Therefore, LIPC activation appears to be related to semantic encoding and not task difficulty. Semantic encoding decisions are performed faster the second time words are presented. This represents semantic repetition priming, a facilitation in semantic processing for previously encoded words that is not dependent on intentional recollection. The same LIPC area activated during semantic encoding showed decreased activation during repeated semantic encoding relative to initial semantic encoding of the same words. This decrease in activation during repeated encoding was process specific; it occurred when words were semantically reprocessed but not when words were nonsemantically reprocessed. The results were apparent in both individual and averaged functional maps. These findings suggest that the LIPC is part of a semantic executive system that contributes to the on-line retrieval of semantic information.

Construction of an annotated corpus to support biomedical information extraction

PubMed Central

Thompson, Paul; Iqbal, Syed A; McNaught, John; Ananiadou, Sophia

2009-01-01

Background Information Extraction (IE) is a component of text mining that facilitates knowledge discovery by automatically locating instances of interesting biomedical events from huge document collections. As events are usually centred on verbs and nominalised verbs, understanding the syntactic and semantic behaviour of these words is highly important. Corpora annotated with information concerning this behaviour can constitute a valuable resource in the training of IE components and resources. Results We have defined a new scheme for annotating sentence-bound gene regulation events, centred on both verbs and nominalised verbs. For each event instance, all participants (arguments) in the same sentence are identified and assigned a semantic role from a rich set of 13 roles tailored to biomedical research articles, together with a biological concept type linked to the Gene Regulation Ontology. To our knowledge, our scheme is unique within the biomedical field in terms of the range of event arguments identified. Using the scheme, we have created the Gene Regulation Event Corpus (GREC), consisting of 240 MEDLINE abstracts, in which events relating to gene regulation and expression have been annotated by biologists. A novel method of evaluating various different facets of the annotation task showed that average inter-annotator agreement rates fall within the range of 66% - 90%. Conclusion The GREC is a unique resource within the biomedical field, in that it annotates not only core relationships between entities, but also a range of other important details about these relationships, e.g., location, temporal, manner and environmental conditions. As such, it is specifically designed to support bio-specific tool and resource development. It has already been used to acquire semantic frames for inclusion within the BioLexicon (a lexical, terminological resource to aid biomedical text mining). Initial experiments have also shown that the corpus may viably be used to train IE components, such as semantic role labellers. The corpus and annotation guidelines are freely available for academic purposes. PMID:19852798

The structure of semantic person memory: evidence from semantic priming in person recognition.

PubMed

Wiese, Holger

2011-11-01

This paper reviews research on the structure of semantic person memory as examined with semantic priming. In this experimental paradigm, a familiarity decision on a target face or written name is usually faster when it is preceded by a related as compared to an unrelated prime. This effect has been shown to be relatively short lived and susceptible to interfering items. Moreover, semantic priming can cross stimulus domains, such that a written name can prime a target face and vice versa. However, it remains controversial whether representations of people are stored in associative networks based on co-occurrence, or in more abstract semantic categories. In line with prominent cognitive models of face recognition, which explain semantic priming by shared semantic information between prime and target, recent research demonstrated that priming could be obtained from purely categorically related, non-associated prime/target pairs. Although strategic processes, such as expectancy and retrospective matching likely contribute, there is also evidence for a non-strategic contribution to priming, presumably related to spreading activation. Finally, a semantic priming effect has been demonstrated in the N400 event-related potential (ERP) component, which may reflect facilitated access to semantic information. It is concluded that categorical relatedness is one organizing principle of semantic person memory. ©2011 The British Psychological Society.

Auditing Associative Relations across Two Knowledge Sources

PubMed Central

Vizenor, Lowell T.; Bodenreider, Olivier; McCray, Alexa T.

2009-01-01

Objectives This paper proposes a novel semantic method for auditing associative relations in biomedical terminologies. We tested our methodology on two Unified Medical Language System (UMLS) knowledge sources. Methods We use the UMLS semantic groups as high-level representations of the domain and range of relationships in the Metathesaurus and in the Semantic Network. A mapping created between Metathesaurus relationships and Semantic Network relationships forms the basis for comparing the signatures of a given Metathesaurus relationship to the signatures of the semantic relationship to which it is mapped. The consistency of Metathesaurus relations is studied for each relationship. Results Of the 177 associative relationships in the Metathesaurus, 84 (48%) exhibit a high degree of consistency with the corresponding Semantic Network relationships. Overall, 63% of the 1.8M associative relations in the Metathesaurus are consistent with relations in the Semantic Network. Conclusion The semantics of associative relationships in biomedical terminologies should be defined explicitly by their developers. The Semantic Network would benefit from being extended with new relationships and with new relations for some existing relationships. The UMLS editing environment could take advantage of the correspondence established between relationships in the Metathesaurus and the Semantic Network. Finally, the auditing method also yielded useful information for refining the mapping of associative relationships between the two sources. PMID:19475724

The semantic origin of unconscious priming: Behavioral and event-related potential evidence during category congruency priming from strongly and weakly related masked words.

PubMed

Ortells, Juan J; Kiefer, Markus; Castillo, Alejandro; Megías, Montserrat; Morillas, Alejandro

2016-01-01

The mechanisms underlying masked congruency priming, semantic mechanisms such as semantic activation or non-semantic mechanisms, for example response activation, remain a matter of debate. In order to decide between these alternatives, reaction times (RTs) and event-related potentials (ERPs) were recorded in the present study, while participants performed a semantic categorization task on visible word targets that were preceded either 167 ms (Experiment 1) or 34 ms before (Experiment 2) by briefly presented (33 ms) novel (unpracticed) masked prime words. The primes and targets belonged to different categories (unrelated), or they were either strongly or weakly semantically related category co-exemplars. Behavioral (RT) and electrophysiological masked congruency priming effects were significantly greater for strongly related pairs than for weakly related pairs, indicating a semantic origin of effects. Priming in the latter condition was not statistically reliable. Furthermore, priming effects modulated the N400 event-related potential (ERP) component, an electrophysiological index of semantic processing, but not ERPs in the time range of the N200 component, associated with response conflict and visuo-motor response priming. The present results demonstrate that masked congruency priming from novel prime words also depends on semantic processing of the primes and is not exclusively driven by non-semantic mechanisms such as response activation. Copyright © 2015 Elsevier B.V. All rights reserved.

Co-occurrence frequency evaluated with large language corpora boosts semantic priming effects.

PubMed

Brunellière, Angèle; Perre, Laetitia; Tran, ThiMai; Bonnotte, Isabelle

2017-09-01

In recent decades, many computational techniques have been developed to analyse the contextual usage of words in large language corpora. The present study examined whether the co-occurrence frequency obtained from large language corpora might boost purely semantic priming effects. Two experiments were conducted: one with conscious semantic priming, the other with subliminal semantic priming. Both experiments contrasted three semantic priming contexts: an unrelated priming context and two related priming contexts with word pairs that are semantically related and that co-occur either frequently or infrequently. In the conscious priming presentation (166-ms stimulus-onset asynchrony, SOA), a semantic priming effect was recorded in both related priming contexts, which was greater with higher co-occurrence frequency. In the subliminal priming presentation (66-ms SOA), no significant priming effect was shown, regardless of the related priming context. These results show that co-occurrence frequency boosts pure semantic priming effects and are discussed with reference to models of semantic network.

Syntactic and semantic processing of Chinese middle sentences: evidence from event-related potentials.

PubMed

Zeng, Tao; Mao, Wen; Lu, Qing

2016-05-25

Scalp-recorded event-related potentials are known to be sensitive to particular aspects of sentence processing. The N400 component is widely recognized as an effect closely related to lexical-semantic processing. The absence of an N400 effect in participants performing tasks in Indo-European languages has been considered evidence that failed syntactic category processing appears to block lexical-semantic integration and that syntactic structure building is a prerequisite of semantic analysis. An event-related potential experiment was designed to investigate whether such syntactic primacy can be considered to apply equally to Chinese sentence processing. Besides correct middles, sentences with either single semantic or single syntactic violation as well as double syntactic and semantic anomaly were used in the present research. Results showed that both purely semantic and combined violation induced a broad negativity in the time window 300-500 ms, indicating the independence of lexical-semantic integration. These findings provided solid evidence that lexical-semantic parsing plays a crucial role in Chinese sentence comprehension.

Semantically-enabled Knowledge Discovery in the Deep Carbon Observatory

NASA Astrophysics Data System (ADS)

Wang, H.; Chen, Y.; Ma, X.; Erickson, J. S.; West, P.; Fox, P. A.

2013-12-01

The Deep Carbon Observatory (DCO) is a decadal effort aimed at transforming scientific and public understanding of carbon in the complex deep earth system from the perspectives of Deep Energy, Deep Life, Extreme Physics and Chemistry, and Reservoirs and Fluxes. Over the course of the decade DCO scientific activities will generate a massive volume of data across a variety of disciplines, presenting significant challenges in terms of data integration, management, analysis and visualization, and ultimately limiting the ability of scientists across disciplines to make insights and unlock new knowledge. The DCO Data Science Team (DCO-DS) is applying Semantic Web methodologies to construct a knowledge representation focused on the DCO Earth science disciplines, and use it together with other technologies (e.g. natural language processing and data mining) to create a more expressive representation of the distributed corpus of DCO artifacts including datasets, metadata, instruments, sensors, platforms, deployments, researchers, organizations, funding agencies, grants and various awards. The embodiment of this knowledge representation is the DCO Data Science Infrastructure, in which unique entities within the DCO domain and the relations between them are recognized and explicitly identified. The DCO-DS Infrastructure will serve as a platform for more efficient and reliable searching, discovery, access, and publication of information and knowledge for the DCO scientific community and beyond.

Subliminal semantic priming in speech.

PubMed

Daltrozzo, Jérôme; Signoret, Carine; Tillmann, Barbara; Perrin, Fabien

2011-01-01

Numerous studies have reported subliminal repetition and semantic priming in the visual modality. We transferred this paradigm to the auditory modality. Prime awareness was manipulated by a reduction of sound intensity level. Uncategorized prime words (according to a post-test) were followed by semantically related, unrelated, or repeated target words (presented without intensity reduction) and participants performed a lexical decision task (LDT). Participants with slower reaction times in the LDT showed semantic priming (faster reaction times for semantically related compared to unrelated targets) and negative repetition priming (slower reaction times for repeated compared to semantically related targets). This is the first report of semantic priming in the auditory modality without conscious categorization of the prime.

Subliminal Semantic Priming in Speech

PubMed Central

Tillmann, Barbara; Perrin, Fabien

2011-01-01

Numerous studies have reported subliminal repetition and semantic priming in the visual modality. We transferred this paradigm to the auditory modality. Prime awareness was manipulated by a reduction of sound intensity level. Uncategorized prime words (according to a post-test) were followed by semantically related, unrelated, or repeated target words (presented without intensity reduction) and participants performed a lexical decision task (LDT). Participants with slower reaction times in the LDT showed semantic priming (faster reaction times for semantically related compared to unrelated targets) and negative repetition priming (slower reaction times for repeated compared to semantically related targets). This is the first report of semantic priming in the auditory modality without conscious categorization of the prime. PMID:21655277

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

PubMed

Jiang, Min; Chen, Yukun; Liu, Mei; Rosenbloom, S Trent; Mani, Subramani; Denny, Joshua C; Xu, Hua

2011-01-01

The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge. The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes. Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge. Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test data set in the challenge.

Informatics Support for Basic Research in Biomedicine

PubMed Central

Rindflesch, Thomas C.; Blake, Catherine L.; Fiszman, Marcelo; Kilicoglu, Halil; Rosemblat, Graciela; Schneider, Jodi; Zeiss, Caroline J.

2017-01-01

Abstract Informatics methodologies exploit computer-assisted techniques to help biomedical researchers manage large amounts of information. In this paper, we focus on the biomedical research literature (MEDLINE). We first provide an overview of some text mining techniques that offer assistance in research by identifying biomedical entities (e.g., genes, substances, and diseases) and relations between them in text. We then discuss Semantic MEDLINE, an application that integrates PubMed document retrieval, concept and relation identification, and visualization, thus enabling a user to explore concepts and relations from within a set of retrieved citations. Semantic MEDLINE provides a roadmap through content and helps users discern patterns in large numbers of retrieved citations. We illustrate its use with an informatics method we call “discovery browsing,” which provides a principled way of navigating through selected aspects of some biomedical research area. The method supports an iterative process that accommodates learning and hypothesis formation in which a user is provided with high level connections before delving into details. As a use case, we examine current developments in basic research on mechanisms of Alzheimer’s disease. Out of the nearly 90 000 citations returned by the PubMed query “Alzheimer’s disease,” discovery browsing led us to 73 citations on sortilin and that disorder. We provide a synopsis of the basic research reported in 15 of these. There is wide-spread consensus among researchers working with a range of animal models and human cells that increased sortilin expression and decreased receptor expression are associated with amyloid beta and/or amyloid precursor protein. PMID:28838071

Combining the Generic Entity-Attribute-Value Model and Terminological Models into a Common Ontology to Enable Data Integration and Decision Support.

PubMed

Bouaud, Jacques; Guézennec, Gilles; Séroussi, Brigitte

2018-01-01

The integration of clinical information models and termino-ontological models into a unique ontological framework is highly desirable for it facilitates data integration and management using the same formal mechanisms for both data concepts and information model components. This is particularly true for knowledge-based decision support tools that aim to take advantage of all facets of semantic web technologies in merging ontological reasoning, concept classification, and rule-based inferences. We present an ontology template that combines generic data model components with (parts of) existing termino-ontological resources. The approach is developed for the guideline-based decision support module on breast cancer management within the DESIREE European project. The approach is based on the entity attribute value model and could be extended to other domains.

Grammatical gender effects on cognition: implications for language learning and language use.

PubMed

Vigliocco, Gabriella; Vinson, David P; Paganelli, Federica; Dworzynski, Katharina

2005-11-01

In 4 experiments, the authors addressed the mechanisms by which grammatical gender (in Italian and German) may come to affect meaning. In Experiments 1 (similarity judgments) and 2 (semantic substitution errors), the authors found Italian gender effects for animals but not for artifacts; Experiment 3 revealed no comparable effects in German. These results suggest that gender effects arise as a generalization from an established association between gender of nouns and sex of human referents, extending to nouns referring to sexuated entities. Across languages, such effects are found when the language allows for easy mapping between gender of nouns and sex of human referents (Italian) but not when the mapping is less transparent (German). A final experiment provided further constraints: These effects during processing arise at a lexical-semantic level rather than at a conceptual level. Copyright (c) 2005 APA, all rights reserved.

A Model for Semantic Equivalence Discovery for Harmonizing Master Data

NASA Astrophysics Data System (ADS)

Piprani, Baba

IT projects often face the challenge of harmonizing metadata and data so as to have a "single" version of the truth. Determining equivalency of multiple data instances against the given type, or set of types, is mandatory in establishing master data legitimacy in a data set that contains multiple incarnations of instances belonging to the same semantic data record . The results of a real-life application define how measuring criteria and equivalence path determination were established via a set of "probes" in conjunction with a score-card approach. There is a need for a suite of supporting models to help determine master data equivalency towards entity resolution—including mapping models, transform models, selection models, match models, an audit and control model, a scorecard model, a rating model. An ORM schema defines the set of supporting models along with their incarnation into an attribute based model as implemented in an RDBMS.

Building Scalable Knowledge Graphs for Earth Science

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Maskey, M.; Gatlin, P. N.; Zhang, J.; Duan, X.; Bugbee, K.; Christopher, S. A.; Miller, J. J.

2017-12-01

Estimates indicate that the world's information will grow by 800% in the next five years. In any given field, a single researcher or a team of researchers cannot keep up with this rate of knowledge expansion without the help of cognitive systems. Cognitive computing, defined as the use of information technology to augment human cognition, can help tackle large systemic problems. Knowledge graphs, one of the foundational components of cognitive systems, link key entities in a specific domain with other entities via relationships. Researchers could mine these graphs to make probabilistic recommendations and to infer new knowledge. At this point, however, there is a dearth of tools to generate scalable Knowledge graphs using existing corpus of scientific literature for Earth science research. Our project is currently developing an end-to-end automated methodology for incrementally constructing Knowledge graphs for Earth Science. Semantic Entity Recognition (SER) is one of the key steps in this methodology. SER for Earth Science uses external resources (including metadata catalogs and controlled vocabulary) as references to guide entity extraction and recognition (i.e., labeling) from unstructured text, in order to build a large training set to seed the subsequent auto-learning component in our algorithm. Results from several SER experiments will be presented as well as lessons learned.

GeoSciGraph: An Ontological Framework for EarthCube Semantic Infrastructure

NASA Astrophysics Data System (ADS)

Gupta, A.; Schachne, A.; Condit, C.; Valentine, D.; Richard, S.; Zaslavsky, I.

2015-12-01

The CINERGI (Community Inventory of EarthCube Resources for Geosciences Interoperability) project compiles an inventory of a wide variety of earth science resources including documents, catalogs, vocabularies, data models, data services, process models, information repositories, domain-specific ontologies etc. developed by research groups and data practitioners. We have developed a multidisciplinary semantic framework called GeoSciGraph semantic ingration of earth science resources. An integrated ontology is constructed with Basic Formal Ontology (BFO) as its upper ontology and currently ingests multiple component ontologies including the SWEET ontology, GeoSciML's lithology ontology, Tematres controlled vocabulary server, GeoNames, GCMD vocabularies on equipment, platforms and institutions, software ontology, CUAHSI hydrology vocabulary, the environmental ontology (ENVO) and several more. These ontologies are connected through bridging axioms; GeoSciGraph identifies lexically close terms and creates equivalence class or subclass relationships between them after human verification. GeoSciGraph allows a community to create community-specific customizations of the integrated ontology. GeoSciGraph uses the Neo4J,a graph database that can hold several billion concepts and relationships. GeoSciGraph provides a number of REST services that can be called by other software modules like the CINERGI information augmentation pipeline. 1) Vocabulary services are used to find exact and approximate terms, term categories (community-provided clusters of terms e.g., measurement-related terms or environmental material related terms), synonyms, term definitions and annotations. 2) Lexical services are used for text parsing to find entities, which can then be included into the ontology by a domain expert. 3) Graph services provide the ability to perform traversal centric operations e.g., finding paths and neighborhoods which can be used to perform ontological operations like computing transitive closure (e.g., finding all subclasses of rocks). 4) Annotation services are used to adorn an arbitrary block of text (e.g., from a NOAA catalog record) with ontology terms. The system has been used to ontologically integrate diverse sources like Science-base, NOAA records, PETDB.

A method and software framework for enriching private biomedical sources with data from public online repositories.

PubMed

Anguita, Alberto; García-Remesal, Miguel; Graf, Norbert; Maojo, Victor

2016-04-01

Modern biomedical research relies on the semantic integration of heterogeneous data sources to find data correlations. Researchers access multiple datasets of disparate origin, and identify elements-e.g. genes, compounds, pathways-that lead to interesting correlations. Normally, they must refer to additional public databases in order to enrich the information about the identified entities-e.g. scientific literature, published clinical trial results, etc. While semantic integration techniques have traditionally focused on providing homogeneous access to private datasets-thus helping automate the first part of the research, and there exist different solutions for browsing public data, there is still a need for tools that facilitate merging public repositories with private datasets. This paper presents a framework that automatically locates public data of interest to the researcher and semantically integrates it with existing private datasets. The framework has been designed as an extension of traditional data integration systems, and has been validated with an existing data integration platform from a European research project by integrating a private biological dataset with data from the National Center for Biotechnology Information (NCBI). Copyright © 2016 Elsevier Inc. All rights reserved.

A commentary on the associations among 'food addiction', binge eating disorder, and obesity: Overlapping conditions with idiosyncratic clinical features.

PubMed

Davis, Caroline

2017-08-01

This commentary discusses the evidence linking patterns of compulsive overeating, such as binge eating and grazing, with a putative psychopathological condition known commonly as 'food addiction'. It also addresses their distinctiveness as independent - albeit overlapping - clinical entities. Discussions focus largely on their respective clinical features and neuropsychobiological associations. Despite semantic issues about the appropriateness of the food-addiction label, there is accumulating evidence that some vulnerable individuals display addictive symptoms in relation to their consumption of certain highly rewarding foods. It is also argued in this paper that despite a positive relationship between obesity and addictive tendencies towards food, it is over-inclusive to model obesity as an addiction disorder, especially given the multi-faceted etiology and current pervasiveness of weight gain worldwide. Copyright © 2016 Elsevier Ltd. All rights reserved.

UMass at TREC WEB 2014: Entity Query Feature Expansion using Knowledge Base Links

DTIC Science & Technology

2014-11-01

bears 270 sun tzu 274 golf instruction 291 sangre de cristo mountains 263 evidence for evolution 300 how to find the mean 262 balding cure 280 view my...internet history 294 flowering plants (b) Worst Query Title 264 tribe formerly living in alabama 295 how to tie a windsor knot 283 hayrides in pa 252...work we leverage the rich semantic knowledge available through these links to understand relevance of documents for a query. We fo- cus on the ad hoc

The Role of Metaphysical Naturalism in Science

NASA Astrophysics Data System (ADS)

Mahner, Martin

2012-10-01

This paper defends the view that metaphysical naturalism is a constitutive ontological principle of science in that the general empirical methods of science, such as observation, measurement and experiment, and thus the very production of empirical evidence, presuppose a no-supernature principle. It examines the consequences of metaphysical naturalism for the testability of supernatural claims, and it argues that explanations involving supernatural entities are pseudo-explanatory due to the many semantic and ontological problems of supernatural concepts. The paper also addresses the controversy about metaphysical versus methodological naturalism.

Semantic associative relations and conceptual processing.

PubMed

Di Giacomo, Dina; De Federicis, Lucia Serenella; Pistelli, Manuela; Fiorenzi, Daniela; Passafiume, Domenico

2012-02-01

We analysed the organisation of semantic network using associative mechanisms between different types of information and studied the progression of the use of these associative relations during development. We aimed to verify the linkage of concepts with the use of semantic associative relations. The goal of this study was to analyse the cognitive ability to use associative relations between various items when describing old and/or new concepts. We examined the performance of 100 subjects between the ages of 4 and 7 years on an experimental task using five associative relations based on verbal encoding. The results showed that children are able to use the five semantic associative relations at age 4, but performance with each of the different associative relations improves at different times during development. Functional and part/whole relations develop at an early age, whereas the superordinate relations develop later. Our study clarified the characteristics of the progression of semantic associations during development as well as the roles that associative relations play in the structure and improvement of the semantic store.

Coreference annotation and resolution in the Colorado Richly Annotated Full Text (CRAFT) corpus of biomedical journal articles.

PubMed

Cohen, K Bretonnel; Lanfranchi, Arrick; Choi, Miji Joo-Young; Bada, Michael; Baumgartner, William A; Panteleyeva, Natalya; Verspoor, Karin; Palmer, Martha; Hunter, Lawrence E

2017-08-17

Coreference resolution is the task of finding strings in text that have the same referent as other strings. Failures of coreference resolution are a common cause of false negatives in information extraction from the scientific literature. In order to better understand the nature of the phenomenon of coreference in biomedical publications and to increase performance on the task, we annotated the Colorado Richly Annotated Full Text (CRAFT) corpus with coreference relations. The corpus was manually annotated with coreference relations, including identity and appositives for all coreferring base noun phrases. The OntoNotes annotation guidelines, with minor adaptations, were used. Interannotator agreement ranges from 0.480 (entity-based CEAF) to 0.858 (Class-B3), depending on the metric that is used to assess it. The resulting corpus adds nearly 30,000 annotations to the previous release of the CRAFT corpus. Differences from related projects include a much broader definition of markables, connection to extensive annotation of several domain-relevant semantic classes, and connection to complete syntactic annotation. Tool performance was benchmarked on the data. A publicly available out-of-the-box, general-domain coreference resolution system achieved an F-measure of 0.14 (B3), while a simple domain-adapted rule-based system achieved an F-measure of 0.42. An ensemble of the two reached F of 0.46. Following the IDENTITY chains in the data would add 106,263 additional named entities in the full 97-paper corpus, for an increase of 76% percent in the semantic classes of the eight ontologies that have been annotated in earlier versions of the CRAFT corpus. The project produced a large data set for further investigation of coreference and coreference resolution in the scientific literature. The work raised issues in the phenomenon of reference in this domain and genre, and the paper proposes that many mentions that would be considered generic in the general domain are not generic in the biomedical domain due to their referents to specific classes in domain-specific ontologies. The comparison of the performance of a publicly available and well-understood coreference resolution system with a domain-adapted system produced results that are consistent with the notion that the requirements for successful coreference resolution in this genre are quite different from those of the general domain, and also suggest that the baseline performance difference is quite large.

Won't Get Fooled Again: An Event-Related Potential Study of Task and Repetition Effects on the Semantic Processing of Items without Semantics

ERIC Educational Resources Information Center

Laszlo, Sarah; Stites, Mallory; Federmeier, Kara D.

2012-01-01

A growing body of evidence suggests that semantic access is obligatory. Several studies have demonstrated that brain activity associated with semantic processing, measured in the N400 component of the event-related brain potential (ERP), is elicited even by meaningless, orthographically illegal strings, suggesting that semantic access is not gated…

Semantic priming in the motor cortex: evidence from combined repetitive transcranial magnetic stimulation and event-related potential.

PubMed

Kuipers, Jan-Rouke; van Koningsbruggen, Martijn; Thierry, Guillaume

2013-08-21

Reading action verbs is associated with activity in the motor cortices involved in performing the corresponding actions. Here, we present new evidence that the motor cortex is involved in semantic processing of bodily action verbs. In contrast to previous studies, we used a direct, nonbehavioural index of semantic processing after repetitive transcranial magnetic stimulation (rTMS). Participants saw pairs of hand-related (e.g. to grab-to point) or mouth-related (e.g. to speak-to sing) verbs, whereas semantic priming was assessed using event-related potentials. Presentation of the first verb coincided with rTMS over the participant's cortical-left hand area and event-related brain potentials were analysed time-locked to the presentation onset of the second verb. Semantic integration - indexed by the N400 brain potential - was impaired for hand-related but not for mouth-related verb pairs after rTMS. This finding provides strong evidence that the motor cortex is involved in semantic encoding of action verbs, and supports the 'embodied semantics' hypothesis.

S3QL: A distributed domain specific language for controlled semantic integration of life sciences data

PubMed Central

2011-01-01

Background The value and usefulness of data increases when it is explicitly interlinked with related data. This is the core principle of Linked Data. For life sciences researchers, harnessing the power of Linked Data to improve biological discovery is still challenged by a need to keep pace with rapidly evolving domains and requirements for collaboration and control as well as with the reference semantic web ontologies and standards. Knowledge organization systems (KOSs) can provide an abstraction for publishing biological discoveries as Linked Data without complicating transactions with contextual minutia such as provenance and access control. We have previously described the Simple Sloppy Semantic Database (S3DB) as an efficient model for creating knowledge organization systems using Linked Data best practices with explicit distinction between domain and instantiation and support for a permission control mechanism that automatically migrates between the two. In this report we present a domain specific language, the S3DB query language (S3QL), to operate on its underlying core model and facilitate management of Linked Data. Results Reflecting the data driven nature of our approach, S3QL has been implemented as an application programming interface for S3DB systems hosting biomedical data, and its syntax was subsequently generalized beyond the S3DB core model. This achievement is illustrated with the assembly of an S3QL query to manage entities from the Simple Knowledge Organization System. The illustrative use cases include gastrointestinal clinical trials, genomic characterization of cancer by The Cancer Genome Atlas (TCGA) and molecular epidemiology of infectious diseases. Conclusions S3QL was found to provide a convenient mechanism to represent context for interoperation between public and private datasets hosted at biomedical research institutions and linked data formalisms. PMID:21756325

An Iterative Inference Procedure Applying Conditional Random Fields for Simultaneous Classification of Land Cover and Land Use

NASA Astrophysics Data System (ADS)

Albert, L.; Rottensteiner, F.; Heipke, C.

2015-08-01

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result.

S3QL: a distributed domain specific language for controlled semantic integration of life sciences data.

PubMed

Deus, Helena F; Correa, Miriã C; Stanislaus, Romesh; Miragaia, Maria; Maass, Wolfgang; de Lencastre, Hermínia; Fox, Ronan; Almeida, Jonas S

2011-07-14

The value and usefulness of data increases when it is explicitly interlinked with related data. This is the core principle of Linked Data. For life sciences researchers, harnessing the power of Linked Data to improve biological discovery is still challenged by a need to keep pace with rapidly evolving domains and requirements for collaboration and control as well as with the reference semantic web ontologies and standards. Knowledge organization systems (KOSs) can provide an abstraction for publishing biological discoveries as Linked Data without complicating transactions with contextual minutia such as provenance and access control.We have previously described the Simple Sloppy Semantic Database (S3DB) as an efficient model for creating knowledge organization systems using Linked Data best practices with explicit distinction between domain and instantiation and support for a permission control mechanism that automatically migrates between the two. In this report we present a domain specific language, the S3DB query language (S3QL), to operate on its underlying core model and facilitate management of Linked Data. Reflecting the data driven nature of our approach, S3QL has been implemented as an application programming interface for S3DB systems hosting biomedical data, and its syntax was subsequently generalized beyond the S3DB core model. This achievement is illustrated with the assembly of an S3QL query to manage entities from the Simple Knowledge Organization System. The illustrative use cases include gastrointestinal clinical trials, genomic characterization of cancer by The Cancer Genome Atlas (TCGA) and molecular epidemiology of infectious diseases. S3QL was found to provide a convenient mechanism to represent context for interoperation between public and private datasets hosted at biomedical research institutions and linked data formalisms.

Semantic similarity measurement between gene ontology terms based on exclusively inherited shared information.

PubMed

Zhang, Shu-Bo; Lai, Jian-Huang

2015-03-01

Quantifying the semantic similarities between pairs of terms in the Gene Ontology (GO) structure can help to explore the functional relationships between biological entities. A common approach to this problem is to measure the information they have in common based on the information content of their common ancestors. However, many studies have their limitations in measuring the information two GO terms share. This study presented a new measurement, exclusively inherited shared information (EISI) that captured the information shared by two terms based on an intuitive observation on the multiple inheritance relationships among the terms in the GO graph. EISI was derived from the information content of the exclusively inherited common ancestors (EICAs), which were screened from the common ancestors according to the attribute of their direct children. The effectiveness of EISI was evaluated against some state-of-the-art measurements on both artificial and real datasets, it produced more relevant results with experts' scores on the artificial dataset, and supported the prior knowledge of gene function in pathways on the Saccharomyces genome database (SGD). The promising features of EISI are the following: (1) it provides a more effective way to characterize the semantic relationship between two GO terms by taking into account multiple common ancestors related, and (2) can quickly detect all EICAs with time complexity of O(n), which is much more efficient than other methods based on disjunctive common ancestors. It is a promising alternative to multiple inheritance based methods for practical applications on large-scale dataset. The algorithm EISI was implemented in Matlab and is freely available from http://treaton.evai.pl/EISI/. Copyright © 2014 Elsevier B.V. All rights reserved.

Modulation of the N400 component in relation to hypomanic personality traits in a word meaning ambiguity resolution task.

PubMed

Raucher-Chéné, Delphine; Terrien, Sarah; Gobin, Pamela; Gierski, Fabien; Kaladjian, Arthur; Besche-Richard, Chrystel

2017-09-01

High levels of hypomanic personality traits have been associated with an increased risk of developing bipolar disorder (BD). Changes in semantic content, impaired verbal associations, abnormal prosody, and abnormal speed of language are core features of BD, and are thought to be related to semantic processing abnormalities. In the present study, we used event-related potentials to investigate the relation between semantic processing (N400 component) and hypomanic personality traits. We assessed 65 healthy young adults on the Hypomanic Personality Scale (HPS). Event-related potentials were recorded during a semantic ambiguity resolution task exploring semantic ambiguity (polysemous word ending a sentence) and congruency (target word semantically related to the sentence). As expected, semantic ambiguity and congruency both elicited an N400 effect across our sample. Correlation analyses showed a significant positive relationship between the Social Vitality subscore of the HPS and N400 modulation in the frontal region of interest in the incongruent unambiguous condition, and in the frontocentral region of interest in the incongruent ambiguous condition. We found differences in semantic processing (i.e., detection of incongruence and semantic inhibition) in individuals with higher Social Vitality subscores. In the light of the literature, we discuss the notion that a semantic processing impairment could be a potential marker of vulnerability to BD, and one that needs to be explored further in this clinical population. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.

Does N200 reflect semantic processing?--An ERP study on Chinese visual word recognition.

PubMed

Du, Yingchun; Zhang, Qin; Zhang, John X

2014-01-01

Recent event-related potential research has reported a N200 response or a negative deflection peaking around 200 ms following the visual presentation of two-character Chinese words. This N200 shows amplitude enhancement upon immediate repetition and there has been preliminary evidence that it reflects orthographic processing but not semantic processing. The present study tested whether this N200 is indeed unrelated to semantic processing with more sensitive measures, including the use of two tasks engaging semantic processing either implicitly or explicitly and the adoption of a within-trial priming paradigm. In Exp. 1, participants viewed repeated, semantically related and unrelated prime-target word pairs as they performed a lexical decision task judging whether or not each target was a real word. In Exp. 2, participants viewed high-related, low-related and unrelated word pairs as they performed a semantic task judging whether each word pair was related in meaning. In both tasks, semantic priming was found from both the behavioral data and the N400 ERP responses. Critically, while repetition priming elicited a clear and large enhancement on the N200 response, semantic priming did not show any modulation effect on the same response. The results indicate that the N200 repetition enhancement effect cannot be explained with semantic priming and that this specific N200 response is unlikely to reflect semantic processing.

Dissociating the semantic function of two neighbouring subregions in the left lateral anterior temporal lobe

PubMed Central

Sanjuán, Ana; Hope, Thomas M.H.; Parker Jones, 'Ōiwi; Prejawa, Susan; Oberhuber, Marion; Guerin, Julie; Seghier, Mohamed L.; Green, David W.; Price, Cathy J.

2015-01-01

We used fMRI in 35 healthy participants to investigate how two neighbouring subregions in the lateral anterior temporal lobe (LATL) contribute to semantic matching and object naming. Four different levels of processing were considered: (A) recognition of the object concepts; (B) search for semantic associations related to object stimuli; (C) retrieval of semantic concepts of interest; and (D) retrieval of stimulus specific concepts as required for naming. During semantic association matching on picture stimuli or heard object names, we found that activation in both subregions was higher when the objects were semantically related (mug–kettle) than unrelated (car–teapot). This is consistent with both LATL subregions playing a role in (C), the successful retrieval of amodal semantic concepts. In addition, one subregion was more activated for object naming than matching semantically related objects, consistent with (D), the retrieval of a specific concept for naming. We discuss the implications of these novel findings for cognitive models of semantic processing and left anterior temporal lobe function. PMID:25496810

The role of semantically related distractors during encoding and retrieval of words in long-term memory.

PubMed

Meade, Melissa E; Fernandes, Myra A

2016-07-01

We examined the influence of divided attention (DA) on recognition of words when the concurrent task was semantically related or unrelated to the to-be-recognised target words. Participants were asked to either study or retrieve a target list of semantically related words while simultaneously making semantic decisions (i.e., size judgements) to another set of related or unrelated words heard concurrently. We manipulated semantic relatedness of distractor to target words, and whether DA occurred during the encoding or retrieval phase of memory. Recognition accuracy was significantly diminished relative to full attention, following DA conditions at encoding, regardless of relatedness of distractors to study words. However, response times (RTs) were slower with related compared to unrelated distractors. Similarly, under DA at retrieval, recognition RTs were slower when distractors were semantically related than unrelated to target words. Unlike the effect from DA at encoding, recognition accuracy was worse under DA at retrieval when the distractors were related compared to unrelated to the target words. Results suggest that availability of general attentional resources is critical for successful encoding, whereas successful retrieval is particularly reliant on access to a semantic code, making it sensitive to related distractors under DA conditions.

Interpreting semantic clustering effects in free recall.

PubMed

Manning, Jeremy R; Kahana, Michael J

2012-07-01

The order in which participants choose to recall words from a studied list of randomly selected words provides insights into how memories of the words are represented, organised, and retrieved. One pervasive finding is that when a pair of semantically related words (e.g., "cat" and "dog") is embedded in the studied list, the related words are often recalled successively. This tendency to successively recall semantically related words is termed semantic clustering (Bousfield, 1953; Bousfield & Sedgewick, 1944; Cofer, Bruce, & Reicher, 1966). Measuring semantic clustering effects requires making assumptions about which words participants consider to be similar in meaning. However, it is often difficult to gain insights into individual participants' internal semantic models, and for this reason researchers typically rely on standardised semantic similarity metrics. Here we use simulations to gain insights into the expected magnitudes of semantic clustering effects given systematic differences between participants' internal similarity models and the similarity metric used to quantify the degree of semantic clustering. Our results provide a number of useful insights into the interpretation of semantic clustering effects in free recall.

Getting connected: Both associative and semantic links structure semantic memory for newly learned persons.

PubMed

Wiese, Holger; Schweinberger, Stefan R

2015-01-01

The present study examined whether semantic memory for newly learned people is structured by visual co-occurrence, shared semantics, or both. Participants were trained with pairs of simultaneously presented (i.e., co-occurring) preexperimentally unfamiliar faces, which either did or did not share additionally provided semantic information (occupation, place of living, etc.). Semantic information could also be shared between faces that did not co-occur. A subsequent priming experiment revealed faster responses for both co-occurrence/no shared semantics and no co-occurrence/shared semantics conditions, than for an unrelated condition. Strikingly, priming was strongest in the co-occurrence/shared semantics condition, suggesting additive effects of these factors. Additional analysis of event-related brain potentials yielded priming in the N400 component only for combined effects of visual co-occurrence and shared semantics, with more positive amplitudes in this than in the unrelated condition. Overall, these findings suggest that both semantic relatedness and visual co-occurrence are important when novel information is integrated into person-related semantic memory.

Semantic and phonological information in sentence recall: converging psycholinguistic and neuropsychological evidence.

PubMed

Schweppe, Judith; Rummer, Ralf; Bormann, Tobias; Martin, Randi C

2011-12-01

We present one experiment and a neuropsychological case study to investigate to what extent phonological and semantic representations contribute to short-term sentence recall. We modified Potter and Lombardi's (1990) intrusion paradigm, in which retention of a list interferes with sentence recall such that on the list a semantically related lure is presented, which is expected to intrude into sentence recall. In our version, lure words are either semantically related to target words in the sentence or semantically plus phonologically related. With healthy participants, intrusions are more frequent when lure and target overlap phonologically in addition to semantically than when they solely overlap semantically. When this paradigm is applied to a patient with a phonological short-term memory impairment, both lure types induce the same amount of intrusions. These findings indicate that usually phonological information is retained in sentence recall in addition to semantic information.

Assessment of disease named entity recognition on a corpus of annotated sentences.

PubMed

Jimeno, Antonio; Jimenez-Ruiz, Ernesto; Lee, Vivian; Gaudan, Sylvain; Berlanga, Rafael; Rebholz-Schuhmann, Dietrich

2008-04-11

In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions. As part of our research work, we have taken a corpus that has been delivered in the past for the identification of associations of genes to diseases based on the UMLS Metathesaurus and we have reprocessed and re-annotated the corpus. We have gathered annotations for disease entities from two curators, analyzed their disagreement (0.51 in the kappa-statistic) and composed a single annotated corpus for public use. Thereafter, three solutions for disease named entity recognition including MetaMap have been applied to the corpus to automatically annotate it with UMLS Metathesaurus concepts. The resulting annotations have been benchmarked to compare their performance. The annotated corpus is publicly available at ftp://ftp.ebi.ac.uk/pub/software/textmining/corpora/diseases and can serve as a benchmark to other systems. In addition, we found that dictionary look-up already provides competitive results indicating that the use of disease terminology is highly standardized throughout the terminologies and the literature. MetaMap generates precise results at the expense of insufficient recall while our statistical method obtains better recall at a lower precision rate. Even better results in terms of precision are achieved by combining at least two of the three methods leading, but this approach again lowers recall. Altogether, our analysis gives a better understanding of the complexity of disease annotations in the literature. MetaMap and the dictionary based approach are available through the Whatizit web service infrastructure (Rebholz-Schuhmann D, Arregui M, Gaudan S, Kirsch H, Jimeno A: Text processing through Web services: Calling Whatizit. Bioinformatics 2008, 24:296-298).

Semantic and Visual Memory After Alcohol Abuse.

ERIC Educational Resources Information Center

Donat, Dennis C.

1986-01-01

Compared the relative performance of 40 patients with a history of alcohol abuse on tasks of short-term semantic and visual memory. Performance on the visual memory tasks was impaired significantly relative to the semantic memory task in a within-subjects analysis of variance. Semantic memory was unimpaired. (Author/ABB)

Somatotopic Semantic Priming and Prediction in the Motor System

PubMed Central

Grisoni, Luigi; Dreyer, Felix R.; Pulvermüller, Friedemann

2016-01-01

The recognition of action-related sounds and words activates motor regions, reflecting the semantic grounding of these symbols in action information; in addition, motor cortex exerts causal influences on sound perception and language comprehension. However, proponents of classic symbolic theories still dispute the role of modality-preferential systems such as the motor cortex in the semantic processing of meaningful stimuli. To clarify whether the motor system carries semantic processes, we investigated neurophysiological indexes of semantic relationships between action-related sounds and words. Event-related potentials revealed that action-related words produced significantly larger stimulus-evoked (Mismatch Negativity-like) and predictive brain responses (Readiness Potentials) when presented in body-part-incongruent sound contexts (e.g., “kiss” in footstep sound context; “kick” in whistle context) than in body-part-congruent contexts, a pattern reminiscent of neurophysiological correlates of semantic priming. Cortical generators of the semantic relatedness effect were localized in areas traditionally associated with semantic memory, including left inferior frontal cortex and temporal pole, and, crucially, in motor areas, where body-part congruency of action sound–word relationships was indexed by a somatotopic pattern of activation. As our results show neurophysiological manifestations of action-semantic priming in the motor cortex, they prove semantic processing in the motor system and thus in a modality-preferential system of the human brain. PMID:26908635

Generation of open biomedical datasets through ontology-driven transformation and integration processes.

PubMed

Carmen Legaz-García, María Del; Miñarro-Giménez, José Antonio; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás

2016-06-03

Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources, which makes difficult the integrated exploitation of such data. The Semantic Web paradigm offers a natural technological space for data integration and exploitation by generating content readable by machines. Linked Open Data is a Semantic Web initiative that promotes the publication and sharing of data in machine readable semantic formats. We present an approach for the transformation and integration of heterogeneous biomedical data with the objective of generating open biomedical datasets in Semantic Web formats. The transformation of the data is based on the mappings between the entities of the data schema and the ontological infrastructure that provides the meaning to the content. Our approach permits different types of mappings and includes the possibility of defining complex transformation patterns. Once the mappings are defined, they can be automatically applied to datasets to generate logically consistent content and the mappings can be reused in further transformation processes. The results of our research are (1) a common transformation and integration process for heterogeneous biomedical data; (2) the application of Linked Open Data principles to generate interoperable, open, biomedical datasets; (3) a software tool, called SWIT, that implements the approach. In this paper we also describe how we have applied SWIT in different biomedical scenarios and some lessons learned. We have presented an approach that is able to generate open biomedical repositories in Semantic Web formats. SWIT is able to apply the Linked Open Data principles in the generation of the datasets, so allowing for linking their content to external repositories and creating linked open datasets. SWIT datasets may contain data from multiple sources and schemas, thus becoming integrated datasets.

Centralizing content and distributing labor: a community model for curating the very long tail of microbial genomes.

PubMed

Putman, Tim E; Burgstaller-Muehlbacher, Sebastian; Waagmeester, Andra; Wu, Chunlei; Su, Andrew I; Good, Benjamin M

2016-01-01

The last 20 years of advancement in sequencing technologies have led to sequencing thousands of microbial genomes, creating mountains of genetic data. While efficiency in generating the data improves almost daily, applying meaningful relationships between taxonomic and genetic entities on this scale requires a structured and integrative approach. Currently, knowledge is distributed across a fragmented landscape of resources from government-funded institutions such as National Center for Biotechnology Information (NCBI) and UniProt to topic-focused databases like the ODB3 database of prokaryotic operons, to the supplemental table of a primary publication. A major drawback to large scale, expert-curated databases is the expense of maintaining and extending them over time. No entity apart from a major institution with stable long-term funding can consider this, and their scope is limited considering the magnitude of microbial data being generated daily. Wikidata is an openly editable, semantic web compatible framework for knowledge representation. It is a project of the Wikimedia Foundation and offers knowledge integration capabilities ideally suited to the challenge of representing the exploding body of information about microbial genomics. We are developing a microbial specific data model, based on Wikidata's semantic web compatibility, which represents bacterial species, strains and the gene and gene products that define them. Currently, we have loaded 43,694 gene and 37,966 protein items for 21 species of bacteria, including the human pathogenic bacteriaChlamydia trachomatis.Using this pathogen as an example, we explore complex interactions between the pathogen, its host, associated genes, other microbes, disease and drugs using the Wikidata SPARQL endpoint. In our next phase of development, we will add another 99 bacterial genomes and their gene and gene products, totaling ∼900,000 additional entities. This aggregation of knowledge will be a platform for community-driven collaboration, allowing the networking of microbial genetic data through the sharing of knowledge by both the data and domain expert. © The Author(s) 2016. Published by Oxford University Press.

Semantic Facilitation in Category and Action Naming: Testing the Message-Congruency Account

ERIC Educational Resources Information Center

Kuipers, Jan-Rouke; La Heij, Wido

2008-01-01

Basic-level picture naming is hampered by the presence of a semantically related context word (compared to an unrelated word), whereas picture categorization is facilitated by a semantically related context word. This reversal of the semantic context effect has been explained by assuming that in categorization tasks, basic-level distractor words…

Semantically Enhanced Online Configuration of Feedback Control Schemes.

PubMed

Milis, Georgios M; Panayiotou, Christos G; Polycarpou, Marios M

2018-03-01

Recent progress toward the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability, and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This paper addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.

Semantic processes leading to true and false memory formation in schizophrenia.

PubMed

Paz-Alonso, Pedro M; Ghetti, Simona; Ramsay, Ian; Solomon, Marjorie; Yoon, Jong; Carter, Cameron S; Ragland, J Daniel

2013-07-01

Encoding semantic relationships between items on word lists (semantic processing) enhances true memories, but also increases memory distortions. Episodic memory impairments in schizophrenia (SZ) are strongly driven by failures to process semantic relations, but the exact nature of these relational semantic processing deficits is not well understood. Here, we used a false memory paradigm to investigate the impact of implicit and explicit semantic processing manipulations on episodic memory in SZ. Thirty SZ and 30 demographically matched healthy controls (HC) studied Deese/Roediger-McDermott (DRM) lists of semantically associated words. Half of the lists had strong implicit semantic associations and the remainder had low strength associations. Similarly, half of the lists were presented under "standard" instructions and the other half under explicit "relational processing" instructions. After study, participants performed recall and old/new recognition tests composed of targets, critical lures, and unrelated lures. HC exhibited higher true memories and better discriminability between true and false memory compared to SZ. High, versus low, associative strength increased false memory rates in both groups. However, explicit "relational processing" instructions positively improved true memory rates only in HC. Finally, true and false memory rates were associated with severity of disorganized and negative symptoms in SZ. These results suggest that reduced processing of semantic relationships during encoding in SZ may stem from an inability to implement explicit relational processing strategies rather than a fundamental deficit in the implicit activation and retrieval of word meanings from patients' semantic lexicon. Copyright © 2013 Elsevier B.V. All rights reserved.

Semantic technologies improving the recall and precision of the Mercury metadata search engine

NASA Astrophysics Data System (ADS)

Pouchard, L. C.; Cook, R. B.; Green, J.; Palanisamy, G.; Noy, N.

2011-12-01

The Mercury federated metadata system [1] was developed at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), a NASA-sponsored effort holding datasets about biogeochemical dynamics, ecological data, and environmental processes. Mercury currently indexes over 100,000 records from several data providers conforming to community standards, e.g. EML, FGDC, FGDC Biological Profile, ISO 19115 and DIF. With the breadth of sciences represented in Mercury, the potential exists to address some key interdisciplinary scientific challenges related to climate change, its environmental and ecological impacts, and mitigation of these impacts. However, this wealth of metadata also hinders pinpointing datasets relevant to a particular inquiry. We implemented a semantic solution after concluding that traditional search approaches cannot improve the accuracy of the search results in this domain because: a) unlike everyday queries, scientific queries seek to return specific datasets with numerous parameters that may or may not be exposed to search (Deep Web queries); b) the relevance of a dataset cannot be judged by its popularity, as each scientific inquiry tends to be unique; and c)each domain science has its own terminology, more or less curated, consensual, and standardized depending on the domain. The same terms may refer to different concepts across domains (homonyms), but different terms mean the same thing (synonyms). Interdisciplinary research is arduous because an expert in a domain must become fluent in the language of another, just to find relevant datasets. Thus, we decided to use scientific ontologies because they can provide a context for a free-text search, in a way that string-based keywords never will. With added context, relevant datasets are more easily discoverable. To enable search and programmatic access to ontology entities in Mercury, we are using an instance of the BioPortal ontology repository. Mercury accesses ontology entities using the BioPortal REST API by passing a search parameter to BioPortal that may return domain context, parameter attribute, or entity annotations depending on the entity's associated ontological relationships. As Mercury's facetted search is popular with users, the results are displayed as facets. Unlike a facetted search however, the ontology-based solution implements both restrictions (improving precision) and expansions (improving recall) on the results of the initial search. For instance, "carbon" acquires a scientific context and additional key terms or phrases for discovering domain-specific datasets. A limitation of our solution is that the user must perform an additional step. Another limitation is that the quality of the newly discovered metadata is contingent upon the quality of the ontologies we use. Our solution leverages Mercury's federated capabilities to collect records from heterogeneous domains, and BioPortal's storage, curation and access capabilities for ontology entities. With minimal additional development, our approach builds on two mature systems for finding relevant datasets for interdisciplinary inquiries. We thus indicate a path forward for linking environmental, ecological and biological sciences. References: [1] Devarakonda, R., Palanisamy, G., Wilson, B. E., & Green, J. M. (2010). Mercury: reusable metadata management, data discovery and access system. Earth Science Informatics, 3(1-2), 87-94.

Community Digital Library Requirements for the Southern California Earthquake Center Community Modeling Environment (SCEC/CME)

NASA Astrophysics Data System (ADS)

Moore, R.; Faerman, M.; Minster, J.; Day, S. M.; Ely, G.

2003-12-01

A community digital library provides support for ingestion, organization, description, preservation, and access of digital entities. The technologies that traditionally provide these capabilities are digital libraries (ingestion, organization, description), persistent archives (preservation) and data grids (access). We present a design for the SCEC community digital library that incorporates aspects of all three systems. Multiple groups have created integrated environments that sustain large-scale scientific data collections. By examining these projects, the following stages of implementation can be identified: \\begin{itemize} Definition of semantic terms to associate with relevant information. This includes definition of uniform content descriptors to describe physical quantities relevant to the scientific discipline, and creation of concept spaces to define how the uniform content descriptors are logically related. Organization of digital entities into logical collections that make it simple to browse and manage related material. Definition of services that are used to access and manipulate material in the collection. Creation of a preservation environment for the long-term management of the collection. Each community is faced with heterogeneity that is introduced when data is distributed across multiple sites, or when multiple sets of collection semantics are used, and or when multiple scientific sub-disciplines are federated. We will present the relevant standards that simplify the implementation of the SCEC community library, the resource requirements for different types of data sets that drive the implementation, and the digital library processes that the SCEC community library will support. The SCEC community library can be viewed as the set of processing steps that are required to build the appropriate SCEC reference data sets (SCEC approved encoding format, SCEC approved descriptive metadata, SCEC approved collection organization, and SCEC managed storage location). Each digital entity that is ingested into the SCEC community library is processed and validated for conformance to SCEC standards. These steps generate provenance, descriptive, administrative, structural, and behavioral metadata. Using data grid technology, the descriptive metadata can be registered onto a logical name space that is controlled and managed by the SCEC digital library. A version of the SCEC community digital library is being implemented in the Storage Resource Broker. The SRB system provides almost all the features enumerated above. The peer-to-peer federation of metadata catalogs is planned for release in September, 2003. The SRB system is in production use in multiple projects, from high-energy physics, to astronomy, to earth systems science, to bio-informatics. The SCEC community library will be based on the definition of standard metadata attributes, the creation of logical collections within the SRB, the creation of access services, and the demonstration of a preservation environment. The use of the SRB for the SCEC digital library will sustain the expected collection size and collection capabilities.

Varieties of semantic cognition revealed through simultaneous decomposition of intrinsic brain connectivity and behaviour.

PubMed

Vatansever, Deniz; Bzdok, Danilo; Wang, Hao-Ting; Mollo, Giovanna; Sormaz, Mladen; Murphy, Charlotte; Karapanagiotidis, Theodoros; Smallwood, Jonathan; Jefferies, Elizabeth

2017-09-01

Contemporary theories assume that semantic cognition emerges from a neural architecture in which different component processes are combined to produce aspects of conceptual thought and behaviour. In addition to the state-level, momentary variation in brain connectivity, individuals may also differ in their propensity to generate particular configurations of such components, and these trait-level differences may relate to individual differences in semantic cognition. We tested this view by exploring how variation in intrinsic brain functional connectivity between semantic nodes in fMRI was related to performance on a battery of semantic tasks in 154 healthy participants. Through simultaneous decomposition of brain functional connectivity and semantic task performance, we identified distinct components of semantic cognition at rest. In a subsequent validation step, these data-driven components demonstrated explanatory power for neural responses in an fMRI-based semantic localiser task and variation in self-generated thoughts during the resting-state scan. Our findings showed that good performance on harder semantic tasks was associated with relative segregation at rest between frontal brain regions implicated in controlled semantic retrieval and the default mode network. Poor performance on easier tasks was linked to greater coupling between the same frontal regions and the anterior temporal lobe; a pattern associated with deliberate, verbal thematic thoughts at rest. We also identified components that related to qualities of semantic cognition: relatively good performance on pictorial semantic tasks was associated with greater separation of angular gyrus from frontal control sites and greater integration with posterior cingulate and anterior temporal cortex. In contrast, good speech production was linked to the separation of angular gyrus, posterior cingulate and temporal lobe regions. Together these data show that quantitative and qualitative variation in semantic cognition across individuals emerges from variations in the interaction of nodes within distinct functional brain networks. Copyright © 2017 Elsevier Inc. All rights reserved.

Semantic and episodic memory in children with temporal lobe epilepsy: do they relate to literacy skills?

PubMed

Lah, Suncica; Smith, Mary Lou

2014-01-01

Children with temporal lobe epilepsy are at risk for deficits in new learning (episodic memory) and literacy skills. Semantic memory deficits and double dissociations between episodic and semantic memory have recently been found in this patient population. In the current study we investigate whether impairments of these 2 distinct memory systems relate to literacy skills. 57 children with unilateral temporal lobe epilepsy completed tests of verbal memory (episodic and semantic) and literacy skills (reading and spelling accuracy, and reading comprehension). For the entire group, semantic memory explained over 30% of variance in each of the literacy domains. Episodic memory explained a significant, but rather small proportion (< 10%) of variance in reading and spelling accuracy, but not in reading comprehension. Moreover, when children with opposite patterns of specific memory impairments (intact semantic/impaired episodic, intact episodic/impaired semantic) were compared, significant reductions in literacy skills were evident only in children with semantic memory impairments, but not in children with episodic memory impairments relative to the norms and to children with temporal lobe epilepsy who had intact memory. Our study provides the first evidence for differential relations between episodic and semantic memory impairments and literacy skills in children with temporal lobe epilepsy. As such, it highlights the urgent need to consider semantic memory deficits in management of children with temporal lobe epilepsy and undertake further research into the nature of reading difficulties of children with semantic memory impairments.

Detecting misinformation and knowledge conflicts in relational data

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Jackobsen, Matthew; Riordan, Brian

2014-06-01

Information fusion is required for many mission-critical intelligence analysis tasks. Using knowledge extracted from various sources, including entities, relations, and events, intelligence analysts respond to commander's information requests, integrate facts into summaries about current situations, augment existing knowledge with inferred information, make predictions about the future, and develop action plans. However, information fusion solutions often fail because of conflicting and redundant knowledge contained in multiple sources. Most knowledge conflicts in the past were due to translation errors and reporter bias, and thus could be managed. Current and future intelligence analysis, especially in denied areas, must deal with open source data processing, where there is much greater presence of intentional misinformation. In this paper, we describe a model for detecting conflicts in multi-source textual knowledge. Our model is based on constructing semantic graphs representing patterns of multi-source knowledge conflicts and anomalies, and detecting these conflicts by matching pattern graphs against the data graph constructed using soft co-reference between entities and events in multiple sources. The conflict detection process maintains the uncertainty throughout all phases, providing full traceability and enabling incremental updates of the detection results as new knowledge or modification to previously analyzed information are obtained. Detected conflicts are presented to analysts for further investigation. In the experimental study with SYNCOIN dataset, our algorithms achieved perfect conflict detection in ideal situation (no missing data) while producing 82% recall and 90% precision in realistic noise situation (15% of missing attributes).

Influences of motor contexts on the semantic processing of action-related language.

PubMed

Yang, Jie

2014-09-01

The contribution of the sensory-motor system to the semantic processing of language stimuli is still controversial. To address the issue, the present article focuses on the impact of motor contexts (i.e., comprehenders' motor behaviors, motor-training experiences, and motor expertise) on the semantic processing of action-related language and reviews the relevant behavioral and neuroimaging findings. The existing evidence shows that although motor contexts can influence the semantic processing of action-related concepts, the mechanism of the contextual influences is still far from clear. Future investigations will be needed to clarify (1) whether motor contexts only modulate activity in motor regions, (2) whether the contextual influences are specific to the semantic features of language stimuli, and (3) what factors can determine the facilitatory or inhibitory contextual influences on the semantic processing of action-related language.

The Relation between Thematic Role Computing and Semantic Relatedness Processing during On-Line Sentence Comprehension

PubMed Central

Li, Xiaoqing; Zhao, Haiyan; Lu, Yong

2014-01-01

Sentence comprehension involves timely computing different types of relations between its verbs and noun arguments, such as morphosyntactic, semantic, and thematic relations. Here, we used EEG technique to investigate the potential differences in thematic role computing and lexical-semantic relatedness processing during on-line sentence comprehension, and the interaction between these two types of processes. Mandarin Chinese sentences were used as materials. The basic structure of those sentences is “Noun+Verb+‘le’+a two-character word”, with the Noun being the initial argument. The verb disambiguates the initial argument as an agent or a patient. Meanwhile, the initial argument and the verb are highly or lowly semantically related. The ERPs at the verbs revealed that: relative to the agent condition, the patient condition evoked a larger N400 only when the argument and verb were lowly semantically related; however, relative to the high-relatedness condition, the low-relatedness condition elicited a larger N400 regardless of the thematic relation; although both thematic role variation and semantic relatedness variation elicited N400 effects, the N400 effect elicited by the former was broadly distributed and reached maximum over the frontal electrodes, and the N400 effect elicited by the latter had a posterior distribution. In addition, the brain oscillations results showed that, although thematic role variation (patient vs. agent) induced power decreases around the beta frequency band (15–30 Hz), semantic relatedness variation (low-relatedness vs. high-relatedness) induced power increases in the theta frequency band (4–7 Hz). These results suggested that, in the sentence context, thematic role computing is modulated by the semantic relatedness between the verb and its argument; semantic relatedness processing, however, is in some degree independent from the thematic relations. Moreover, our results indicated that, during on-line sentence comprehension, thematic role computing and semantic relatedness processing are mediated by distinct neural systems. PMID:24755643

Generation of Signs within Semantic and Phonological Categories: Data from Deaf Adults and Children Who Use American Sign Language

ERIC Educational Resources Information Center

Beal-Alvarez, Jennifer S.; Figueroa, Daileen M.

2017-01-01

Two key areas of language development include semantic and phonological knowledge. Semantic knowledge relates to word and concept knowledge. Phonological knowledge relates to how language parameters combine to create meaning. We investigated signing deaf adults' and children's semantic and phonological sign generation via one-minute tasks,…

Eye Movements to Pictures Reveal Transient Semantic Activation during Spoken Word Recognition

ERIC Educational Resources Information Center

Yee, Eiling; Sedivy, Julie C.

2006-01-01

Two experiments explore the activation of semantic information during spoken word recognition. Experiment 1 shows that as the name of an object unfolds (e.g., lock), eye movements are drawn to pictorial representations of both the named object and semantically related objects (e.g., key). Experiment 2 shows that objects semantically related to an…

Automating generation of textual class definitions from OWL to English.

PubMed

Stevens, Robert; Malone, James; Williams, Sandra; Power, Richard; Third, Allan

2011-05-17

Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag between specification of the main part of an ontology (logical descriptions and definitions of entities) and the development of the text-based definitions. The goal of natural language generation (NLG) from ontologies is to take the logical description of entities and generate fluent natural language. The application described here uses NLG to automatically provide text-based definitions from an ontology that has logical descriptions of its entities, so avoiding the bottleneck of authoring these definitions by hand. To produce the descriptions, the program collects all the axioms relating to a given entity, groups them according to common structure, realises each group through an English sentence, and assembles the resulting sentences into a paragraph, to form as 'coherent' a text as possible without human intervention. Sentence generation is accomplished using a generic grammar based on logical patterns in OWL, together with a lexicon for realising atomic entities. We have tested our output for the Experimental Factor Ontology (EFO) using a simple survey strategy to explore the fluency of the generated text and how well it conveys the underlying axiomatisation. Two rounds of survey and improvement show that overall the generated English definitions are found to convey the intended meaning of the axiomatisation in a satisfactory manner. The surveys also suggested that one form of generated English will not be universally liked; that intrusion of too much 'formal ontology' was not liked; and that too much explicit exposure of OWL semantics was also not liked. Our prototype tools can generate reasonable paragraphs of English text that can act as definitions. The definitions were found acceptable by our survey and, as a result, the developers of EFO are sufficiently satisfied with the output that the generated definitions have been incorporated into EFO. Whilst not a substitute for hand-written textual definitions, our generated definitions are a useful starting point. An on-line version of the NLG text definition tool can be found at http://swat.open.ac.uk/tools/. The questionaire and sample generated text definitions may be found at http://mcs.open.ac.uk/nlg/SWAT/bio-ontologies.html.

Automating generation of textual class definitions from OWL to English

PubMed Central

2011-01-01

Background Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag between specification of the main part of an ontology (logical descriptions and definitions of entities) and the development of the text-based definitions. The goal of natural language generation (NLG) from ontologies is to take the logical description of entities and generate fluent natural language. The application described here uses NLG to automatically provide text-based definitions from an ontology that has logical descriptions of its entities, so avoiding the bottleneck of authoring these definitions by hand. Results To produce the descriptions, the program collects all the axioms relating to a given entity, groups them according to common structure, realises each group through an English sentence, and assembles the resulting sentences into a paragraph, to form as ‘coherent’ a text as possible without human intervention. Sentence generation is accomplished using a generic grammar based on logical patterns in OWL, together with a lexicon for realising atomic entities. We have tested our output for the Experimental Factor Ontology (EFO) using a simple survey strategy to explore the fluency of the generated text and how well it conveys the underlying axiomatisation. Two rounds of survey and improvement show that overall the generated English definitions are found to convey the intended meaning of the axiomatisation in a satisfactory manner. The surveys also suggested that one form of generated English will not be universally liked; that intrusion of too much ‘formal ontology’ was not liked; and that too much explicit exposure of OWL semantics was also not liked. Conclusions Our prototype tools can generate reasonable paragraphs of English text that can act as definitions. The definitions were found acceptable by our survey and, as a result, the developers of EFO are sufficiently satisfied with the output that the generated definitions have been incorporated into EFO. Whilst not a substitute for hand-written textual definitions, our generated definitions are a useful starting point. Availability An on-line version of the NLG text definition tool can be found at http://swat.open.ac.uk/tools/. The questionaire and sample generated text definitions may be found at http://mcs.open.ac.uk/nlg/SWAT/bio-ontologies.html. PMID:21624160

Priming production: Neural evidence for enhanced automatic semantic activity preceding language production in schizophrenia.

PubMed

Kuperberg, Gina R; Delaney-Busch, Nathaniel; Fanucci, Kristina; Blackford, Trevor

2018-01-01

Lexico-semantic disturbances are considered central to schizophrenia. Clinically, their clearest manifestation is in language production. However, most studies probing their underlying mechanisms have used comprehension or categorization tasks. Here, we probed automatic semantic activity prior to language production in schizophrenia using event-related potentials (ERPs). 19 people with schizophrenia and 16 demographically-matched healthy controls named target pictures that were very quickly preceded by masked prime words. To probe automatic semantic activity prior to production, we measured the N400 ERP component evoked by these targets. To determine the origin of any automatic semantic abnormalities, we manipulated the type of relationship between prime and target such that they overlapped in (a) their semantic features (semantically related, e.g. "cake" preceding a < picture of a pie >, (b) their initial phonemes (phonemically related, e.g. "stomach" preceding a < picture of a starfish >), or (c) both their semantic features and their orthographic/phonological word form (identity related, e.g. "socks" preceding a < picture of socks >). For each of these three types of relationship, the same targets were paired with unrelated prime words (counterbalanced across lists). We contrasted ERPs and naming times to each type of related target with its corresponding unrelated target. People with schizophrenia showed abnormal N400 modulation prior to naming identity related (versus unrelated) targets: whereas healthy control participants produced a smaller amplitude N400 to identity related than unrelated targets, patients showed the opposite pattern, producing a larger N400 to identity related than unrelated targets. This abnormality was specific to the identity related targets. Just like healthy control participants, people with schizophrenia produced a smaller N400 to semantically related than to unrelated targets, and showed no difference in the N400 evoked by phonemically related and unrelated targets. There were no differences between the two groups in the pattern of naming times across conditions. People with schizophrenia can show abnormal neural activity associated with automatic semantic processing prior to language production. The specificity of this abnormality to the identity related targets suggests that that, rather than arising from abnormalities of either semantic features or lexical form alone, it may stem from disruptions of mappings (connections) between the meaning of words and their form.

Considering the role of semantic memory in episodic future thinking: evidence from semantic dementia.

PubMed

Irish, Muireann; Addis, Donna Rose; Hodges, John R; Piguet, Olivier

2012-07-01

Semantic dementia is a progressive neurodegenerative condition characterized by the profound and amodal loss of semantic memory in the context of relatively preserved episodic memory. In contrast, patients with Alzheimer's disease typically display impairments in episodic memory, but with semantic deficits of a much lesser magnitude than in semantic dementia. Our understanding of episodic memory retrieval in these cohorts has greatly increased over the last decade, however, we know relatively little regarding the ability of these patients to imagine and describe possible future events, and whether episodic future thinking is mediated by divergent neural substrates contingent on dementia subtype. Here, we explored episodic future thinking in patients with semantic dementia (n=11) and Alzheimer's disease (n=11), in comparison with healthy control participants (n=10). Participants completed a battery of tests designed to probe episodic and semantic thinking across past and future conditions, as well as standardized tests of episodic and semantic memory. Further, all participants underwent magnetic resonance imaging. Despite their relatively intact episodic retrieval for recent past events, the semantic dementia cohort showed significant impairments for episodic future thinking. In contrast, the group with Alzheimer's disease showed parallel deficits across past and future episodic conditions. Voxel-based morphometry analyses confirmed that atrophy in the left inferior temporal gyrus and bilateral temporal poles, regions strongly implicated in semantic memory, correlated significantly with deficits in episodic future thinking in semantic dementia. Conversely, episodic future thinking performance in Alzheimer's disease correlated with atrophy in regions associated with episodic memory, namely the posterior cingulate, parahippocampal gyrus and frontal pole. These distinct neuroanatomical substrates contingent on dementia group were further qualified by correlational analyses that confirmed the relation between semantic memory deficits and episodic future thinking in semantic dementia, in contrast with the role of episodic memory deficits and episodic future thinking in Alzheimer's disease. Our findings demonstrate that semantic knowledge is critical for the construction of novel future events, providing the necessary scaffolding into which episodic details can be integrated. Further research is necessary to elucidate the precise contribution of semantic memory to future thinking, and to explore how deficits in self-projection manifest on behavioural and social levels in different dementia subtypes.

Electrophysiological Evidence for Use of the Animacy Hierarchy, but not Thematic Role Assignment, During Verb Argument Processing

PubMed Central

Paczynski, Martin; Kuperberg, Gina R.

2011-01-01

Animacy is known to play an important role in language processing and production, but debate remains as to how it exerts its effects: 1) through links to syntactic ordering, 2) through inherent differences between animate and inanimate entities in their salience/lexico-semantic accessibility, 3) through links to specific thematic roles. We contrasted these three accounts in two event related potential (ERP) experiments examining the processing of direct object arguments in simple English sentences. In Experiment 1, we found a larger N400 to animate than inanimate direct object arguments assigned the Patient role, ruling out the second account. In Experiment 2 we found no difference in the N400 evoked by animate direct object arguments assigned the Patient role (prototypically inanimate) and those assigned the Experiencer role (prototypically animate), ruling out the third account. We therefore suggest that animacy may impact processing through a direct link to syntactic linear ordering, at least on post-verbal arguments in English. We also examined processing on direct object arguments that violated the animacy-based selection restriction constraints of their preceding verbs. These violations evoked a robust P600, which was not modulated by thematic role assignment or reversibility, suggesting that the so-called semantic P600 is driven by overall propositional impossibility, rather than thematic role reanalysis. PMID:22199415

Enhancing biomedical text summarization using semantic relation extraction.

PubMed

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.

Priming Addition Facts with Semantic Relations

ERIC Educational Resources Information Center

Bassok, Miriam; Pedigo, Samuel F.; Oskarsson, An T.

2008-01-01

Results from 2 relational-priming experiments suggest the existence of an automatic analogical coordination between semantic and arithmetic relations. Word pairs denoting object sets served as primes in a task that elicits "obligatory" activation of addition facts (5 + 3 activates 8; J. LeFevre, J. Bisanz, & L. Mrkonjic, 1988). Semantic relations…

Entity Profiling for Intelligence Using the Graphical Overview of Social and Semantic Interactions of People (GOSSIP) Software Tool

DTIC Science & Technology

2010-11-01

TR 2010-188; R & D pour la défense Canada – Toronto; Novembre 2010. Introduction ou contexte : En règle générale, l’analyste du renseignement ou...model humans Series3 DRDC Toronto TR 2010-188 13 Figure 4. continued. Profiles for famous names generated by subjects and the model...document is classified) 13 . ABSTRACT (A brief and factual summary of the document. It may also appear elsewhere in the body of the document itself. It is

A Framework for Classifying and Resolving Semantic Conflicts Using the Enhanced Entity-Relationship Model

DTIC Science & Technology

1992-09-01

rank, social security number, and date of birth, sex , race, etc. It also keeps data on marital status, number of dependents, and whether a member’s...specification as listed in the appendix. OPINS stores similar common personnel information to that in the ADMI database, such as name, rank, sex , etc.. The...34+ NAME (comp) "+ DATE..OF-.BIRTH (comp) "+ SEX "+ BACE-MIHNIC "+ ETHNIC..GROUP "+ PAYýENTRY-.BASE..DATE (comp) "+ SERVICE "+ MOS (comp) "+ DATE-OF

Selective impairment of living things and musical instruments on a verbal 'Semantic Knowledge Questionnaire' in a case of apperceptive visual agnosia.

PubMed

Masullo, Carlo; Piccininni, Chiara; Quaranta, Davide; Vita, Maria Gabriella; Gaudino, Simona; Gainotti, Guido

2012-10-01

Semantic memory was investigated in a patient (MR) affected by a severe apperceptive visual agnosia, due to an ischemic cerebral lesion, bilaterally affecting the infero-mesial parts of the temporo-occipital cortices. The study was made by means of a Semantic Knowledge Questionnaire (Laiacona, Barbarotto, Trivelli, & Capitani, 1993), which takes separately into account four categories of living beings (animals, fruits, vegetables and body parts) and of artefacts (furniture, tools, vehicles and musical instruments), does not require a visual analysis and allows to distinguish errors concerning super-ordinate categorization, perceptual features and functional/encyclopedic knowledge. When the total number of errors obtained on all the categories of living and non-living beings was considered, a non-significant trend toward a higher number of errors in living stimuli was observed. This difference, however, became significant when body parts and musical instruments were excluded from the analysis. Furthermore, the number of errors obtained on the musical instruments was similar to that obtained on the living categories of animals, fruits and vegetables and significantly higher of that obtained in the other artefact categories. This difference was still significant when familiarity, frequency of use and prototypicality of each stimulus entered into a logistic regression analysis. On the other hand, a separate analysis of errors obtained on questions exploring super-ordinate categorization, perceptual features and functional/encyclopedic attributes showed that the differences between living and non-living stimuli and between musical instruments and other artefact categories were mainly due to errors obtained on questions exploring perceptual features. All these data are at variance with the 'domains of knowledge' hypothesis', which assumes that the breakdown of different categories of living and non-living things respects the distinction between biological entities and artefacts and support the models assuming that 'category-specific semantic disorders' are the by-product of the differential weighting that visual-perceptual and functional (or action-related) attributes have in the construction of different biological and artefacts categories. Copyright © 2012 Elsevier Inc. All rights reserved.

Semantic Priming for Coordinate Distant Concepts in Alzheimer's Disease Patients

ERIC Educational Resources Information Center

Perri, R.; Zannino, G. D.; Caltagirone, C.; Carlesimo, G. A.

2011-01-01

Semantic priming paradigms have been used to investigate semantic knowledge in patients with Alzheimer's disease (AD). While priming effects produced by prime-target pairs with associative relatedness reflect processes at both lexical and semantic levels, priming effects produced by words that are semantically related but not associated should…

A practical approach to object based requirements analysis

NASA Technical Reports Server (NTRS)

Drew, Daniel W.; Bishop, Michael

1988-01-01

Presented here is an approach developed at the Unisys Houston Operation Division, which supports the early identification of objects. This domain oriented analysis and development concept is based on entity relationship modeling and object data flow diagrams. These modeling techniques, based on the GOOD methodology developed at the Goddard Space Flight Center, support the translation of requirements into objects which represent the real-world problem domain. The goal is to establish a solid foundation of understanding before design begins, thereby giving greater assurance that the system will do what is desired by the customer. The transition from requirements to object oriented design is also promoted by having requirements described in terms of objects. Presented is a five step process by which objects are identified from the requirements to create a problem definition model. This process involves establishing a base line requirements list from which an object data flow diagram can be created. Entity-relationship modeling is used to facilitate the identification of objects from the requirements. An example is given of how semantic modeling may be used to improve the entity-relationship model and a brief discussion on how this approach might be used in a large scale development effort.

42 CFR 417.484 - Requirement applicable to related entities.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 3 2011-10-01 2011-10-01 false Requirement applicable to related entities. 417.484... entities. (a) Definition. As used in this section, related entity means any entity that is related to the... agrees to require all related entities to agree that— (1) HHS, the Comptroller General, or their...

42 CFR 417.484 - Requirement applicable to related entities.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Requirement applicable to related entities. 417.484... entities. (a) Definition. As used in this section, related entity means any entity that is related to the... agrees to require all related entities to agree that— (1) HHS, the Comptroller General, or their...

Learning the Language of Healthcare Enabling Semantic Web Technology in CHCS

DTIC Science & Technology

2013-09-01

tuples”, (subject, predicate, object), to relate data and achieve semantic interoperability . Other similar technologies exist, but their... Semantic Healthcare repository [5]. Ultimately, both of our data approaches were successful. However, our current test system is based on the CPRS demo...to extract system dependencies and workflows; to extract semantically related patient data ; and to browse patient- centric views into the system . We

Meaningful Memory in Acute Anorexia Nervosa Patients-Comparing Recall, Learning, and Recognition of Semantically Related and Semantically Unrelated Word Stimuli.

PubMed

Terhoeven, Valentin; Kallen, Ursula; Ingenerf, Katrin; Aschenbrenner, Steffen; Weisbrod, Matthias; Herzog, Wolfgang; Brockmeyer, Timo; Friederich, Hans-Christoph; Nikendei, Christoph

2017-03-01

It is unclear whether observed memory impairment in anorexia nervosa (AN) depends on the semantic structure (categorized words) of material to be encoded. We aimed to investigate the processing of semantically related information in AN. Memory performance was assessed in a recall, learning, and recognition test in 27 adult women with AN (19 restricting, 8 binge-eating/purging subtype; average disease duration: 9.32 years) and 30 healthy controls using an extended version of the Rey Auditory Verbal Learning Test, applying semantically related and unrelated word stimuli. Short-term memory (immediate recall, learning), regardless of semantics of the words, was significantly worse in AN patients, whereas long-term memory (delayed recall, recognition) did not differ between AN patients and controls. Semantics of stimuli do not have a better effect on memory recall in AN compared to CO. Impaired short-term versus long-term memory is discussed in relation to dysfunctional working memory in AN. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.

Generation of Signs Within Semantic and Phonological Categories: Data from Deaf Adults and Children Who Use American Sign Language.

PubMed

Beal-Alvarez, Jennifer S; Figueroa, Daileen M

2017-04-01

Two key areas of language development include semantic and phonological knowledge. Semantic knowledge relates to word and concept knowledge. Phonological knowledge relates to how language parameters combine to create meaning. We investigated signing deaf adults' and children's semantic and phonological sign generation via one-minute tasks, including animals, foods, and specific handshapes. We investigated the effects of chronological age, age of sign language acquisition/years at school site, gender, presence of a disability, and geographical location (i.e., USA and Puerto Rico) on participants' performance and relations among tasks. In general, the phonological task appeared more difficult than the semantic tasks, students generated more animals than foods, age, and semantic performance correlated for the larger sample of U.S. students, and geographical variation included use of fingerspelling and specific signs. Compared to their peers, deaf students with disabilities generated fewer semantic items. These results provide an initial snapshot of students' semantic and phonological sign generation. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Intelligence related upper alpha desynchronization in a semantic memory task.

PubMed

Doppelmayr, M; Klimesch, W; Hödlmoser, K; Sauseng, P; Gruber, W

2005-07-30

Recent evidence shows that event-related (upper) alpha desynchronization (ERD) is related to cognitive performance. Several studies observed a positive, some a negative relationship. The latter finding, interpreted in terms of the neural efficiency hypothesis, suggests that good performance is associated with a more 'efficient', smaller extent of cortical activation. Other studies found that ERD increases with semantic processing demands and that this increase is larger for good performers. Studies supporting the neural efficiency hypothesis used tasks that do not specifically require semantic processing. Thus, we assume that the lack of semantic processing demands may at least in part be responsible for the reduced ERD. In the present study we measured ERD during a difficult verbal-semantic task. The findings demonstrate that during semantic processing, more intelligent (as compared to less intelligent) subjects exhibited a significantly larger upper alpha ERD over the left hemisphere. We conclude that more intelligent subjects exhibit a more extensive activation in a semantic processing system and suggest that divergent findings regarding the neural efficiency hypotheses are due to task specific differences in semantic processing demands.

Audio-Visual and Meaningful Semantic Context Enhancements in Older and Younger Adults.

PubMed

Smayda, Kirsten E; Van Engen, Kristin J; Maddox, W Todd; Chandrasekaran, Bharath

2016-01-01

Speech perception is critical to everyday life. Oftentimes noise can degrade a speech signal; however, because of the cues available to the listener, such as visual and semantic cues, noise rarely prevents conversations from continuing. The interaction of visual and semantic cues in aiding speech perception has been studied in young adults, but the extent to which these two cues interact for older adults has not been studied. To investigate the effect of visual and semantic cues on speech perception in older and younger adults, we recruited forty-five young adults (ages 18-35) and thirty-three older adults (ages 60-90) to participate in a speech perception task. Participants were presented with semantically meaningful and anomalous sentences in audio-only and audio-visual conditions. We hypothesized that young adults would outperform older adults across SNRs, modalities, and semantic contexts. In addition, we hypothesized that both young and older adults would receive a greater benefit from a semantically meaningful context in the audio-visual relative to audio-only modality. We predicted that young adults would receive greater visual benefit in semantically meaningful contexts relative to anomalous contexts. However, we predicted that older adults could receive a greater visual benefit in either semantically meaningful or anomalous contexts. Results suggested that in the most supportive context, that is, semantically meaningful sentences presented in the audiovisual modality, older adults performed similarly to young adults. In addition, both groups received the same amount of visual and meaningful benefit. Lastly, across groups, a semantically meaningful context provided more benefit in the audio-visual modality relative to the audio-only modality, and the presence of visual cues provided more benefit in semantically meaningful contexts relative to anomalous contexts. These results suggest that older adults can perceive speech as well as younger adults when both semantic and visual cues are available to the listener.

Audio-Visual and Meaningful Semantic Context Enhancements in Older and Younger Adults

PubMed Central

Smayda, Kirsten E.; Van Engen, Kristin J.; Maddox, W. Todd; Chandrasekaran, Bharath

2016-01-01

Speech perception is critical to everyday life. Oftentimes noise can degrade a speech signal; however, because of the cues available to the listener, such as visual and semantic cues, noise rarely prevents conversations from continuing. The interaction of visual and semantic cues in aiding speech perception has been studied in young adults, but the extent to which these two cues interact for older adults has not been studied. To investigate the effect of visual and semantic cues on speech perception in older and younger adults, we recruited forty-five young adults (ages 18–35) and thirty-three older adults (ages 60–90) to participate in a speech perception task. Participants were presented with semantically meaningful and anomalous sentences in audio-only and audio-visual conditions. We hypothesized that young adults would outperform older adults across SNRs, modalities, and semantic contexts. In addition, we hypothesized that both young and older adults would receive a greater benefit from a semantically meaningful context in the audio-visual relative to audio-only modality. We predicted that young adults would receive greater visual benefit in semantically meaningful contexts relative to anomalous contexts. However, we predicted that older adults could receive a greater visual benefit in either semantically meaningful or anomalous contexts. Results suggested that in the most supportive context, that is, semantically meaningful sentences presented in the audiovisual modality, older adults performed similarly to young adults. In addition, both groups received the same amount of visual and meaningful benefit. Lastly, across groups, a semantically meaningful context provided more benefit in the audio-visual modality relative to the audio-only modality, and the presence of visual cues provided more benefit in semantically meaningful contexts relative to anomalous contexts. These results suggest that older adults can perceive speech as well as younger adults when both semantic and visual cues are available to the listener. PMID:27031343

A method for named entity normalization in biomedical articles: application to diseases and plants.

PubMed

Cho, Hyejin; Choi, Wonjun; Lee, Hyunju

2017-10-13

In biomedical articles, a named entity recognition (NER) technique that identifies entity names from texts is an important element for extracting biological knowledge from articles. After NER is applied to articles, the next step is to normalize the identified names into standard concepts (i.e., disease names are mapped to the National Library of Medicine's Medical Subject Headings disease terms). In biomedical articles, many entity normalization methods rely on domain-specific dictionaries for resolving synonyms and abbreviations. However, the dictionaries are not comprehensive except for some entities such as genes. In recent years, biomedical articles have accumulated rapidly, and neural network-based algorithms that incorporate a large amount of unlabeled data have shown considerable success in several natural language processing problems. In this study, we propose an approach for normalizing biological entities, such as disease names and plant names, by using word embeddings to represent semantic spaces. For diseases, training data from the National Center for Biotechnology Information (NCBI) disease corpus and unlabeled data from PubMed abstracts were used to construct word representations. For plants, a training corpus that we manually constructed and unlabeled PubMed abstracts were used to represent word vectors. We showed that the proposed approach performed better than the use of only the training corpus or only the unlabeled data and showed that the normalization accuracy was improved by using our model even when the dictionaries were not comprehensive. We obtained F-scores of 0.808 and 0.690 for normalizing the NCBI disease corpus and manually constructed plant corpus, respectively. We further evaluated our approach using a data set in the disease normalization task of the BioCreative V challenge. When only the disease corpus was used as a dictionary, our approach significantly outperformed the best system of the task. The proposed approach shows robust performance for normalizing biological entities. The manually constructed plant corpus and the proposed model are available at http://gcancer.org/plant and http://gcancer.org/normalization , respectively.

Activation of semantic information at the sublexical level during handwriting production: Evidence from inhibition effects of Chinese semantic radicals in the picture-word interference paradigm.

PubMed

Chen, Xuqian; Liao, Yuanlan; Chen, Xianzhe

2017-08-01

Using a non-alphabetic language (e.g., Chinese), the present study tested a novel view that semantic information at the sublexical level should be activated during handwriting production. Over 80% of Chinese characters are phonograms, in which semantic radicals represent category information (e.g., 'chair,' 'peach,' 'orange' are related to plants) while phonetic radicals represent phonetic information (e.g., 'wolf,' 'brightness,' 'male,' are all pronounced /lang/). Under different semantic category conditions at the lexical level (semantically related in Experiment 1; semantically unrelated in Experiment 2), the orthographic relatedness and semantic relatedness of semantic radicals in the picture name and its distractor were manipulated under different SOAs (i.e., stimulus onset asynchrony, the interval between the onset of the picture and the onset of the interference word). Two questions were addressed: (1) Is it possible that semantic information could be activated in the sublexical level conditions? (2) How are semantic and orthographic information dynamically accessed in word production? Results showed that both orthographic and semantic information were activated under the present picture-word interference paradigm, dynamically under different SOAs, which supported our view that discussions on semantic processes in the writing modality should be extended to the sublexical level. The current findings provide possibility for building new orthography-phonology-semantics models in writing. © 2017 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Clever generation of rich SPARQL queries from annotated relational schema: application to Semantic Web Service creation for biological databases.

PubMed

Wollbrett, Julien; Larmande, Pierre; de Lamotte, Frédéric; Ruiz, Manuel

2013-04-15

In recent years, a large amount of "-omics" data have been produced. However, these data are stored in many different species-specific databases that are managed by different institutes and laboratories. Biologists often need to find and assemble data from disparate sources to perform certain analyses. Searching for these data and assembling them is a time-consuming task. The Semantic Web helps to facilitate interoperability across databases. A common approach involves the development of wrapper systems that map a relational database schema onto existing domain ontologies. However, few attempts have been made to automate the creation of such wrappers. We developed a framework, named BioSemantic, for the creation of Semantic Web Services that are applicable to relational biological databases. This framework makes use of both Semantic Web and Web Services technologies and can be divided into two main parts: (i) the generation and semi-automatic annotation of an RDF view; and (ii) the automatic generation of SPARQL queries and their integration into Semantic Web Services backbones. We have used our framework to integrate genomic data from different plant databases. BioSemantic is a framework that was designed to speed integration of relational databases. We present how it can be used to speed the development of Semantic Web Services for existing relational biological databases. Currently, it creates and annotates RDF views that enable the automatic generation of SPARQL queries. Web Services are also created and deployed automatically, and the semantic annotations of our Web Services are added automatically using SAWSDL attributes. BioSemantic is downloadable at http://southgreen.cirad.fr/?q=content/Biosemantic.

Clever generation of rich SPARQL queries from annotated relational schema: application to Semantic Web Service creation for biological databases

PubMed Central

2013-01-01

Background In recent years, a large amount of “-omics” data have been produced. However, these data are stored in many different species-specific databases that are managed by different institutes and laboratories. Biologists often need to find and assemble data from disparate sources to perform certain analyses. Searching for these data and assembling them is a time-consuming task. The Semantic Web helps to facilitate interoperability across databases. A common approach involves the development of wrapper systems that map a relational database schema onto existing domain ontologies. However, few attempts have been made to automate the creation of such wrappers. Results We developed a framework, named BioSemantic, for the creation of Semantic Web Services that are applicable to relational biological databases. This framework makes use of both Semantic Web and Web Services technologies and can be divided into two main parts: (i) the generation and semi-automatic annotation of an RDF view; and (ii) the automatic generation of SPARQL queries and their integration into Semantic Web Services backbones. We have used our framework to integrate genomic data from different plant databases. Conclusions BioSemantic is a framework that was designed to speed integration of relational databases. We present how it can be used to speed the development of Semantic Web Services for existing relational biological databases. Currently, it creates and annotates RDF views that enable the automatic generation of SPARQL queries. Web Services are also created and deployed automatically, and the semantic annotations of our Web Services are added automatically using SAWSDL attributes. BioSemantic is downloadable at http://southgreen.cirad.fr/?q=content/Biosemantic. PMID:23586394

Towards Infusing Giovanni with a Semantic and Provenance Aware Visualization System

NASA Astrophysics Data System (ADS)

Del Rio, N.; Pinheiro da Silva, P.; Leptoukh, G. G.; Lynnes, C.

2011-12-01

Giovanni is a Web-based application developed by GES DISC that provides simple and intuitive ways to visualize, analyze, and access vast amounts of Earth science remote sensed data. Currently, the Giovanni visualization module is only aware of the physical links (i.e., hard-coded) between data and services and consequently cannot be easily adapted to new visualization scenarios. VisKo, a semantically enabled visualization framework, can be leveraged by Giovanni as a semantic bridge between data and visualization. VisKo relates data and visualization services at conceptual (i.e., ontological) levels and relies on reasoning systems to leverage the conceptual relationships to automatically infer physical links, facilitating an adaptable environment for new visualization scenarios. This is particularly useful for Giovanni, which has been constantly retrofitted with new visualization software packages to keep up with advancement in visualization capabilities. During our prototype integration of Giovanni with VisKo, a number of future steps were identified that if implemented could cement the integration and promote our prototype to operational status. A number of integration issues arose including the mediation of different languages used by each system to characterize datasets; VisKo relies on semantic data characterization to "match-up" data with visualization processes. It was necessary to identify mappings between Giovanni XML provenance and Proof Markup Language, which is understood by VisKo. Although a translator was implemented based on identified mappings, a more elegant solution is to develop a domain data ontology specific to Giovanni and to "align" this ontology with PML, enabling VisKo to directly ingest the semantic descriptions of Giovanni data. Additionally, the relationship between dataset components (e.g., variables and attributes) and visualization plot components (e.g., geometries, axes, titles) should also be modeled. In Giovanni, meta-data descriptions are used to configure the different properties of the plots such as titles, color-tables, and variable-to-axis bindings. Giovanni services rely on a set of custom attributes and naming conventions that help identify the relationships between dataset components and plot properties. VisKo visualization services however are generic modules that do not rely on any domain specific conventions for identifying relationships between dataset attributes and plot configuration. Rather, VisKo services rely on parameters to configure specific behaviors of the generic services. The relationship between VisKo parameters and plot properties however has yet to formally documented, partly because VisKo regards plots as holistic entities without any internal structure from which to relate parameters. We understand the need for a visualization plot ontology that defines plot components, their retinal properties, such as position and color, and the relationship between the plot properties to controlling service parameter sets. The plot ontology would also be linked to our domain data ontology, providing VisKo with the comprehensive understanding about how data attributes can cue the configuration of plots, and how a specific plot configuration relates to service parameters.

Proactive interference in a semantic short-term memory deficit: role of semantic and phonological relatedness.

PubMed

Hamilton, A Cris; Martin, Randi C

2007-01-01

Previous research has indicated that patients with semantic short-term memory (STM) deficits demonstrate unusual intrusions of previously presented material during serial recall tasks (Martin and Lesch, 1996). These intrusions suggest excessive proactive interference (PI) from previous lists. Here, we explore one such patient's susceptibility to PI. Experiment 1 demonstrated patient M.L.'s extreme susceptibility to PI using a probe recognition task that manipulates the recency of negative probes (the recent negatives task). When stimuli consisted of letters, M.L. showed greatly exaggerated effects of PI, well outside of the range of healthy control participants. Experiment 2 used a variation of the recent negatives task to examine the relative contribution of semantic and phonological relatedness in PI. This task manipulated semantic and phonological relatedness of probes and recently presented list items. Relative to healthy control participants, patient M.L. showed exaggerated interference effects for both phonological and semantically related probes, both for probes related to the current list and for probes related to the previous list. These data have important implications for theories of semantic STM deficits. Specifically, these data suggest that it is not the rapid decay of semantic representations that is responsible for difficulties in short-term recall, but rather the abnormal persistence of previously presented material. We propose that this susceptibility to PI is the result of a deficit in control processes acting on STM.

Verb Production during Action Naming in Semantic Dementia

ERIC Educational Resources Information Center

Meligne, D.; Fossard, M.; Belliard, S.; Moreaud, O.; Duvignau, K.; Demonet, J.-F.

2011-01-01

In contrast with widely documented deficits of semantic knowledge relating to object concepts and the corresponding nouns in semantic dementia (SD), little is known about action semantics and verb production in SD. The degradation of action semantic knowledge was studied in 5 patients with SD compared with 17 matched control participants in an…

The Masked Semantic Priming Effect Is Task Dependent: Reconsidering the Automatic Spreading Activation Process

ERIC Educational Resources Information Center

de Wit, Bianca; Kinoshita, Sachiko

2015-01-01

Semantic priming effects are popularly explained in terms of an automatic spreading activation process, according to which the activation of a node in a semantic network spreads automatically to interconnected nodes, preactivating a semantically related word. It is expected from this account that semantic priming effects should be routinely…

The absoluteness of semantic processing: lessons from the analysis of temporal clusters in phonemic verbal fluency.

PubMed

Vonberg, Isabelle; Ehlen, Felicitas; Fromm, Ortwin; Klostermann, Fabian

2014-01-01

For word production, we may consciously pursue semantic or phonological search strategies, but it is uncertain whether we can retrieve the different aspects of lexical information independently from each other. We therefore studied the spread of semantic information into words produced under exclusively phonemic task demands. 42 subjects participated in a letter verbal fluency task, demanding the production of as many s-words as possible in two minutes. Based on curve fittings for the time courses of word production, output spurts (temporal clusters) considered to reflect rapid lexical retrieval based on automatic activation spread, were identified. Semantic and phonemic word relatedness within versus between these clusters was assessed by respective scores (0 meaning no relation, 4 maximum relation). Subjects produced 27.5 (±9.4) words belonging to 6.7 (±2.4) clusters. Both phonemically and semantically words were more related within clusters than between clusters (phon: 0.33±0.22 vs. 0.19±0.17, p<.01; sem: 0.65±0.29 vs. 0.37±0.29, p<.01). Whereas the extent of phonemic relatedness correlated with high task performance, the contrary was the case for the extent of semantic relatedness. The results indicate that semantic information spread occurs, even if the consciously pursued word search strategy is purely phonological. This, together with the negative correlation between semantic relatedness and verbal output suits the idea of a semantic default mode of lexical search, acting against rapid task performance in the given scenario of phonemic verbal fluency. The simultaneity of enhanced semantic and phonemic word relatedness within the same temporal cluster boundaries suggests an interaction between content and sound-related information whenever a new semantic field has been opened.

Lateralized direct and indirect semantic priming effects in subjects with paranormal experiences and beliefs.

PubMed

Pizzagalli, D; Lehmann, D; Brugger, P

2001-01-01

The present investigation tested the hypothesis that, as an aspect of schizotypal thinking, the formation of paranormal beliefs was related to spreading activation characteristics within semantic networks. From a larger student population (n = 117) prescreened for paranormal belief, 12 strong believers and 12 strong disbelievers (all women) were invited for a lateralized semantic priming task with directly and indirectly related prime-target pairs. Believers showed stronger indirect (but not direct) semantic priming effects than disbelievers after left (but not right) visual field stimulation, indicating faster appreciation of distant semantic relations specifically by the right hemisphere, reportedly specialized in coarse rather than focused semantic processing. These results are discussed in the light of recent findings in schizophrenic patients with thought disorders. They suggest that a disinhibition with semantic networks may underlie the formation of paranormal belief. The potential usefulness of work with healthy subjects for neuropsychiatric research is stressed. Copyright 2001 S. Karger AG, Basel

Cross-language parafoveal semantic processing: Evidence from Korean-Chinese bilinguals.

PubMed

Wang, Aiping; Yeon, Junmo; Zhou, Wei; Shu, Hua; Yan, Ming

2016-02-01

In the present study, we aimed at testing cross-language cognate and semantic preview effects. We tested how native Korean readers who learned Chinese as a second language make use of the parafoveal information during the reading of Chinese sentences. There were 3 types of Korean preview words: cognate translations of the Chinese target words, semantically related noncognate words, and unrelated words. Together with a highly significant cognate preview effect, more critically, we also observed reliable facilitation in processing of the target word from the semantically related previews in all fixation measures. Results from the present study provide first evidence for semantic processing from parafoveally presented Korean words and for cross-language parafoveal semantic processing.

Transformation of standardized clinical models based on OWL technologies: from CEM to OpenEHR archetypes

PubMed Central

Legaz-García, María del Carmen; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás; Chute, Christopher G; Tao, Cui

2015-01-01

Introduction The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies have frequently been used in semantic interoperability efforts. The objective of this paper is to propose a generic, ontology-based, flexible approach for supporting the automatic transformation of clinical models, which is illustrated for the transformation of Clinical Element Models (CEMs) into openEHR archetypes. Methods Our transformation method exploits the fact that the information models of the most relevant EHR specifications are available in the Web Ontology Language (OWL). The transformation approach is based on defining mappings between those ontological structures. We propose a way in which CEM entities can be transformed into openEHR by using transformation templates and OWL as common representation formalism. The transformation architecture exploits the reasoning and inferencing capabilities of OWL technologies. Results We have devised a generic, flexible approach for the transformation of clinical models, implemented for the unidirectional transformation from CEM to openEHR, a series of reusable transformation templates, a proof-of-concept implementation, and a set of openEHR archetypes that validate the methodological approach. Conclusions We have been able to transform CEM into archetypes in an automatic, flexible, reusable transformation approach that could be extended to other clinical model specifications. We exploit the potential of OWL technologies for supporting the transformation process. We believe that our approach could be useful for international efforts in the area of semantic interoperability of EHR systems. PMID:25670753

Neural correlates of semantic associations in patients with schizophrenia.

PubMed

Sass, Katharina; Heim, Stefan; Sachs, Olga; Straube, Benjamin; Schneider, Frank; Habel, Ute; Kircher, Tilo

2014-03-01

Patients with schizophrenia have semantic processing disturbances leading to expressive language deficits (formal thought disorder). The underlying pathology has been related to alterations in the semantic network and its neural correlates. Moreover, crossmodal processing, an important aspect of communication, is impaired in schizophrenia. Here we investigated specific processing abnormalities in patients with schizophrenia with regard to modality and semantic distance in a semantic priming paradigm. Fourteen patients with schizophrenia and fourteen demographically matched controls made visual lexical decisions on successively presented word-pairs (SOA = 350 ms) with direct or indirect relations, unrelated word-pairs, and pseudoword-target stimuli during fMRI measurement. Stimuli were presented in a unimodal (visual) or crossmodal (auditory-visual) fashion. On the neural level, the effect of semantic relation indicated differences (patients > controls) within the right angular gyrus and precuneus. The effect of modality revealed differences (controls > patients) within the left superior frontal, middle temporal, inferior occipital, right angular gyri, and anterior cingulate cortex. Semantic distance (direct vs. indirect) induced distinct activations within the left middle temporal, fusiform gyrus, right precuneus, and thalamus with patients showing fewer differences between direct and indirect word-pairs. The results highlight aberrant priming-related brain responses in patients with schizophrenia. Enhanced activation for patients possibly reflects deficits in semantic processes that might be caused by a delayed and enhanced spread of activation within the semantic network. Modality-specific decreases of activation in patients might be related to impaired perceptual integration. Those deficits could induce and increase the prominent symptoms of schizophrenia like impaired speech processing.

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

PubMed Central

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization. PMID:21887336

Differences in Processing of Taxonomic and Sequential Relations in Semantic Memory: An fMRI Investigation

ERIC Educational Resources Information Center

Kuchinke, Lars; van der Meer, Elke; Krueger, Frank

2009-01-01

Conceptual knowledge of our world is represented in semantic memory in terms of concepts and semantic relations between concepts. We used functional magnetic resonance imaging (fMRI) to examine the cortical regions underlying the processing of sequential and taxonomic relations. Participants were presented verbal cues and performed three tasks:…

Effects of Iconicity and Semantic Relatedness on Lexical Access in American Sign Language

PubMed Central

Bosworth, Rain G.; Emmorey, Karen

2010-01-01

Iconicity is a property that pervades the lexicon of many sign languages, including American Sign Language (ASL). Iconic signs exhibit a motivated, non-arbitrary mapping between the form of the sign and its meaning. We investigated whether iconicity enhances semantic priming effects for ASL and whether iconic signs are recognized more quickly than non-iconic signs (controlling for strength of iconicity, semantic relatedness, familiarity, and imageability). Twenty deaf signers made lexical decisions to the second item of a prime-target pair. Iconic target signs were preceded by prime signs that were a) iconic and semantically related, b) non-iconic and semantically related, or c) semantically unrelated. In addition, a set of non-iconic target signs was preceded by semantically unrelated primes. Significant facilitation was observed for target signs when preceded by semantically related primes. However, iconicity did not increase the priming effect (e.g., the target sign PIANO was primed equally by the iconic sign GUITAR and the non-iconic sign MUSIC). In addition, iconic signs were not recognized faster or more accurately than non-iconic signs. These results confirm the existence of semantic priming for sign language and suggest that iconicity does not play a robust role in on-line lexical processing. PMID:20919784

Identifying adverse drug event information in clinical notes with distributional semantic representations of context.

PubMed

Henriksson, Aron; Kvist, Maria; Dalianis, Hercules; Duneld, Martin

2015-10-01

For the purpose of post-marketing drug safety surveillance, which has traditionally relied on the voluntary reporting of individual cases of adverse drug events (ADEs), other sources of information are now being explored, including electronic health records (EHRs), which give us access to enormous amounts of longitudinal observations of the treatment of patients and their drug use. Adverse drug events, which can be encoded in EHRs with certain diagnosis codes, are, however, heavily underreported. It is therefore important to develop capabilities to process, by means of computational methods, the more unstructured EHR data in the form of clinical notes, where clinicians may describe and reason around suspected ADEs. In this study, we report on the creation of an annotated corpus of Swedish health records for the purpose of learning to identify information pertaining to ADEs present in clinical notes. To this end, three key tasks are tackled: recognizing relevant named entities (disorders, symptoms, drugs), labeling attributes of the recognized entities (negation, speculation, temporality), and relationships between them (indication, adverse drug event). For each of the three tasks, leveraging models of distributional semantics - i.e., unsupervised methods that exploit co-occurrence information to model, typically in vector space, the meaning of words - and, in particular, combinations of such models, is shown to improve the predictive performance. The ability to make use of such unsupervised methods is critical when faced with large amounts of sparse and high-dimensional data, especially in domains where annotated resources are scarce. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Integrating nursing diagnostic concepts into the medical entities dictionary using the ISO Reference Terminology Model for Nursing Diagnosis.

PubMed

Hwang, Jee-In; Cimino, James J; Bakken, Suzanne

2003-01-01

The purposes of the study were (1) to evaluate the usefulness of the International Standards Organization (ISO) Reference Terminology Model for Nursing Diagnoses as a terminology model for defining nursing diagnostic concepts in the Medical Entities Dictionary (MED) and (2) to create the additional hierarchical structures required for integration of nursing diagnostic concepts into the MED. The authors dissected nursing diagnostic terms from two source terminologies (Home Health Care Classification and the Omaha System) into the semantic categories of the ISO model. Consistent with the ISO model, they selected Focus and Judgment as required semantic categories for creating intensional definitions of nursing diagnostic concepts in the MED. Because the MED does not include Focus and Judgment hierarchies, the authors developed them to define the nursing diagnostic concepts. The ISO model was sufficient for dissecting the source terminologies into atomic terms. The authors identified 162 unique focus concepts from the 266 nursing diagnosis terms for inclusion in the Focus hierarchy. For the Judgment hierarchy, the authors precoordinated Judgment and Potentiality instead of using Potentiality as a qualifier of Judgment as in the ISO model. Impairment and Alteration were the most frequently occurring judgments. Nursing care represents a large proportion of health care activities; thus, it is vital that terms used by nurses are integrated into concept-oriented terminologies that provide broad coverage for the domain of health care. This study supports the utility of the ISO Reference Terminology Model for Nursing Diagnoses as a facilitator for the integration process.

Integrating Nursing Diagnostic Concepts into the Medical Entities Dictionary Using the ISO Reference Terminology Model for Nursing Diagnosis

PubMed Central

Hwang, Jee-In; Cimino, James J.; Bakken, Suzanne

2003-01-01

Objective: The purposes of the study were (1) to evaluate the usefulness of the International Standards Organization (ISO) Reference Terminology Model for Nursing Diagnoses as a terminology model for defining nursing diagnostic concepts in the Medical Entities Dictionary (MED) and (2) to create the additional hierarchical structures required for integration of nursing diagnostic concepts into the MED. Design and Measurements: The authors dissected nursing diagnostic terms from two source terminologies (Home Health Care Classification and the Omaha System) into the semantic categories of the ISO model. Consistent with the ISO model, they selected Focus and Judgment as required semantic categories for creating intensional definitions of nursing diagnostic concepts in the MED. Because the MED does not include Focus and Judgment hierarchies, the authors developed them to define the nursing diagnostic concepts. Results: The ISO model was sufficient for dissecting the source terminologies into atomic terms. The authors identified 162 unique focus concepts from the 266 nursing diagnosis terms for inclusion in the Focus hierarchy. For the Judgment hierarchy, the authors precoordinated Judgment and Potentiality instead of using Potentiality as a qualifier of Judgment as in the ISO model. Impairment and Alteration were the most frequently occurring judgments. Conclusions: Nursing care represents a large proportion of health care activities; thus, it is vital that terms used by nurses are integrated into concept-oriented terminologies that provide broad coverage for the domain of health care. This study supports the utility of the ISO Reference Terminology Model for Nursing Diagnoses as a facilitator for the integration process. PMID:12668692

Taxonomic and Thematic Semantic Systems

PubMed Central

Mirman, Daniel; Landrigan, Jon-Frederick; Britt, Allison E.

2017-01-01

Object concepts are critical for nearly all aspects of human cognition, from perception tasks like object recognition, to understanding and producing language, to making meaningful actions. Concepts can have two very different kinds of relations: similarity relations based on shared features (e.g., dog – bear), which are called “taxonomic” relations, and contiguity relations based on co-occurrence in events or scenarios (e.g., dog – leash), which are called “thematic” relations. Here we report a systematic review of experimental psychology and cognitive neuroscience evidence of this distinction in the structure of semantic memory. We propose two principles that may drive the development of distinct taxonomic and thematic semantic systems: (1) differences between which features determine taxonomic vs. thematic relations and (2) differences in the processing required to extract taxonomic vs. thematic relations. This review brings together distinct threads of behavioral, computational, and neuroscience research on semantic memory in support of a functional and neural dissociation, and defines a framework for future studies of semantic memory. PMID:28333494

A data model for environmental scientists

NASA Astrophysics Data System (ADS)

Kapeljushnik, O.; Beran, B.; Valentine, D.; van Ingen, C.; Zaslavsky, I.; Whitenack, T.

2008-12-01

Environmental science encompasses a wide range of disciplines from water chemistry to microbiology, ecology and atmospheric sciences. Studies often require working across disciplines which differ in their ways of describing and storing data such that it is not possible to devise a monolithic one-size-fits-all data solution. Based on our experiences with Consortium of the Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) Observations Data Model, Berkeley Water Center FLUXNET carbon-climate work and by examining standards like EPA's Water Quality Exchange (WQX), we have developed a flexible data model that allows extensions without need to altering the schema such that scientists can define custom metadata elements to describe their data including observations, analysis methods as well as sensors and geographical features. The data model supports various types of observations including fixed point and moving sensors, bottled samples, rasters from remote sensors and models, and categorical descriptions (e.g. taxonomy) by employing user-defined-types when necessary. It leverages ADO .NET Entity Framework to provide the semantic data models for differing disciplines, while maintaining a common schema below the entity layer. This abstraction layer simplifies data retrieval and manipulation by hiding the logic and complexity of the relational schema from users thus allows programmers and scientists to deal directly with objects such as observations, sensors, watersheds, river reaches, channel cross-sections, laboratory analysis methods and samples as opposed to table joins, columns and rows.

An Event Related Potentials Study of Semantic Coherence Effect during Episodic Encoding in Schizophrenia Patients

PubMed Central

Blanchet, Alain; Lockman, Hazlin

2018-01-01

The objective of this electrophysiological study was to investigate the processing of semantic coherence during encoding in relation to episodic memory processes promoted at test, in schizophrenia patients, by using the N400 paradigm. Eighteen schizophrenia patients and 15 healthy participants undertook a recognition memory task. The stimuli consisted of pairs of words either semantically related or unrelated to a given category name (context). During encoding, both groups exhibited an N400 external semantic coherence effect. Healthy controls also showed an N400 internal semantic coherence effect, but this effect was not present in patients. At test, related stimuli were accompanied by an FN400 old/new effect in both groups and by a parietal old/new effect in the control group alone. In the patient group, external semantic coherence effect was associated with FN400, while, in the control group, it was correlated to the parietal old/new effect. Our results indicate that schizophrenia patients can process the contextual information at encoding to enhance familiarity process for related stimuli at test. Therefore, cognitive rehabilitation therapies targeting the implementation of semantic encoding strategies can mobilize familiarity which in turn can overcome the recollection deficit, promoting successful episodic memory performance in schizophrenia patients. PMID:29535872

Long-Term Semantic Priming of Word Meaning

ERIC Educational Resources Information Center

Woltz, Dan J.

2010-01-01

Three experiments investigated facilitation in synonym decisions as a function of prior synonym decision trials that were either identical or semantically related. Experiment 1 demonstrated that semantically related prime trials produced less facilitation than identical prime trials, but facilitation from both persisted over 14 intervening trials.…

Aging and Semantic Activation.

ERIC Educational Resources Information Center

Howard, Darlene V.

Three studies tested the theory that long term memory consists of a semantically organized network of concept nodes interconnected by leveled associations or relations, and that when a stimulus is processed, the corresponding concept node is assumed to be temporarily activated and this activation spreads to nearby semantically related nodes. In…

Word and picture matching: a PET study of semantic category effects.

PubMed

Perani, D; Schnur, T; Tettamanti, M; Gorno-Tempini, M; Cappa, S F; Fazio, F

1999-03-01

We report two positron emission tomography (PET) studies of cerebral activation during picture and word matching tasks, in which we compared directly the processing of stimuli belonging to different semantic categories (animate and inanimate) in the visual (pictures) and verbal (words) modality. In the first experiment, brain activation was measured in eleven healthy adults during a same/different matching task for textures, meaningless shapes and pictures of animals and artefacts (tools). Activations for meaningless shapes when compared to visual texture discrimination were localized in the left occipital and inferior temporal cortex. Animal picture identification, either in the comparison with meaningless shapes and in the direct comparison with non-living pictures, involved primarily activation of occipital regions, namely the lingual gyrus bilaterally and the left fusiform gyrus. For artefact picture identification, in the same comparison with meaningless shape-baseline and in the direct comparison with living pictures, all activations were left hemispheric, through the dorsolateral frontal (Ba 44/6 and 45) and temporal (Ba 21, 20) cortex. In the second experiment, brain activation was measured in eight healthy adults during a same/different matching task for visually presented words referring to animals and manipulable objects (tools); the baseline was a pseudoword discrimination task. When compared with the tool condition, the animal condition activated posterior left hemispheric areas, namely the fusiform (Ba 37) and the inferior occipital gyrus (Ba 18). The right superior parietal lobule (Ba 7) and the left thalamus were also activated. The reverse comparison (tools vs animals) showed left hemispheric activations in the middle temporal gyrus (Ba 21) and precuneus (Ba 7), as well as bilateral activation in the occipital regions. These results are compatible with different brain networks subserving the identification of living and non-living entities; in particular, they indicate a crucial role of the left fusiform gyrus in the processing of animate entities and of the left middle temporal gyrus for tools, both from words and pictures. The activation of other areas, such as the dorsolateral frontal cortex, appears to be specific for the semantic access of tools only from pictures.

Dissociating the effects of semantic grouping and rehearsal strategies on event-related brain potentials.

PubMed

Schleepen, T M J; Markus, C R; Jonkman, L M

2014-12-01

The application of elaborative encoding strategies during learning, such as grouping items on similar semantic categories, increases the likelihood of later recall. Previous studies have suggested that stimuli that encourage semantic grouping strategies had modulating effects on specific ERP components. However, these studies did not differentiate between ERP activation patterns evoked by elaborative working memory strategies like semantic grouping and more simple strategies like rote rehearsal. Identification of neurocognitive correlates underlying successful use of elaborative strategies is important to understand better why certain populations, like children or elderly people, have problems applying such strategies. To compare ERP activation during the application of elaborative versus more simple strategies subjects had to encode either four semantically related or unrelated pictures by respectively applying a semantic category grouping or a simple rehearsal strategy. Another goal was to investigate if maintenance of semantically grouped vs. ungrouped pictures modulated ERP-slow waves differently. At the behavioral level there was only a semantic grouping benefit in terms of faster responding on correct rejections (i.e. when the memory probe stimulus was not part of the memory set). At the neural level, during encoding semantic grouping only had a modest specific modulatory effect on a fronto-central Late Positive Component (LPC), emerging around 650 ms. Other ERP components (i.e. P200, N400 and a second Late Positive Component) that had been earlier related to semantic grouping encoding processes now showed stronger modulation by rehearsal than by semantic grouping. During maintenance semantic grouping had specific modulatory effects on left and right frontal slow wave activity. These results stress the importance of careful control of strategy use when investigating the neural correlates of elaborative encoding. Copyright © 2014 Elsevier B.V. All rights reserved.

Behavioural and magnetoencephalographic evidence for the interaction between semantic and episodic memory in healthy elderly subjects.

PubMed

La Corte, Valentina; Dalla Barba, Gianfranco; Lemaréchal, Jean-Didier; Garnero, Line; George, Nathalie

2012-10-01

The relationship between episodic and semantic memory systems has long been debated. Some authors argue that episodic memory is contingent on semantic memory (Tulving 1984), while others postulate that both systems are independent since they can be selectively damaged (Squire 1987). The interaction between these memory systems is particularly important in the elderly, since the dissociation of episodic and semantic memory defects characterize different aging-related pathologies. Here, we investigated the interaction between semantic knowledge and episodic memory processes associated with faces in elderly subjects using an experimental paradigm where the semantic encoding of famous and unknown faces was compared to their episodic recognition. Results showed that the level of semantic awareness of items affected the recognition of those items in the episodic memory task. Event-related magnetic fields confirmed this interaction between episodic and semantic memory: ERFs related to the old/new effect during the episodic task were markedly different for famous and unknown faces. The old/new effect for famous faces involved sustained activities maximal over right temporal sensors, showing a spatio-temporal pattern partly similar to that found for famous versus unknown faces during the semantic task. By contrast, an old/new effect for unknown faces was observed on left parieto-occipital sensors. These findings suggest that the episodic memory for famous faces activated the retrieval of stored semantic information, whereas it was based on items' perceptual features for unknown faces. Overall, our results show that semantic information interfered markedly with episodic memory processes and suggested that the neural substrates of these two memory systems overlap.

Insights from child development on the relationship between episodic and semantic memory.

PubMed

Robertson, Erin K; Köhler, Stefan

2007-11-05

The present study was motivated by a recent controversy in the neuropsychological literature on semantic dementia as to whether episodic encoding requires semantic processing or whether it can proceed solely based on perceptual processing. We addressed this issue by examining the effect of age-related limitations in semantic competency on episodic memory in 4-6-year-old children (n=67). We administered three different forced-choice recognition memory tests for pictures previously encountered in a single study episode. The tests varied in the degree to which access to semantically encoded information was required at retrieval. Semantic competency predicted recognition performance regardless of whether access to semantic information was required. A direct relation between picture naming at encoding and subsequent recognition was also found for all tests. Our findings emphasize the importance of semantic encoding processes even in retrieval situations that purportedly do not require access to semantic information. They also highlight the importance of testing neuropsychological models of memory in different populations, healthy and brain damaged, at both ends of the developmental continuum.

The effects of associative and semantic priming in the lexical decision task.

PubMed

Perea, Manuel; Rosa, Eva

2002-08-01

Four lexical decision experiments were conducted to examine under which conditions automatic semantic priming effects can be obtained. Experiments 1 and 2 analyzed associative/semantic effects at several very short stimulus-onset asynchronies (SOAs), whereas Experiments 3 and 4 used a single-presentation paradigm at two response-stimulus intervals (RSIs). Experiment 1 tested associatively related pairs from three semantic categories (synonyms, antonyms, and category coordinates). The results showed reliable associative priming effects at all SOAs. In addition, the correlation between associative strength and magnitude of priming was significant only at the shortest SOA (66 ms). When prime-target pairs were semantically but not associatively related (Experiment 2), reliable priming effects were obtained at SOAs of 83 ms and longer. Using the single-presentation paradigm with a short RSI (200 ms, Experiment 3), the priming effect was equal in size for associative + semantic and for semantic-only pairs (a 21-ms effect). When the RSI was set much longer (1,750 ms, Experiment 4), only the associative + semantic pairs showed a reliable priming effect (23 ms). The results are interpreted in the context of models of semantic memory.

Limited role of phonology in reading Chinese two-character compounds: evidence from an ERP study.

PubMed

Wong, A W-K; Wu, Y; Chen, H-C

2014-01-03

This study investigates the role of phonology in reading logographic Chinese. Specifically, whether phonological information is obligatorily activated in reading Chinese two-character compounds was examined using the masked-priming paradigm with event-related potential (ERP) recordings. Twenty-two native Cantonese Chinese speakers participated in a lexical decision experiment. The targets were visually presented Chinese two-character strings and the participants were asked to judge whether the target in each trial was a legitimate compound word in Chinese. Each target was preceded by a briefly presented word prime. The prime and target shared an identical constituent character in the Character-related condition, a syllable in the Syllable-related condition, were semantically related in the Semantic-related condition, and were unrelated (both phonologically and semantically) in the control condition. The prime–target relationship was manipulated to probe the effects of word-form (i.e., character- or syllable-relatedness) and word-semantic relatedness on phonological (as indexed by an N250 ERP component) and semantic (as indexed by an N400 ERP component) processing. Significant and comparable facilitation effects in reaction time, relative to the control, were observed in the Character-related and the Semantic-related conditions. Furthermore, a significant reduction in ERP amplitudes (N250), relative to the control, was obtained in the Character-related condition in the time window of 150-250 ms post target. In addition, attenuation in ERP amplitudes was found in the Semantic-related condition in the window of 250-500 ms (N400). However, no significant results (neither behavioral nor ERP) were found in the Syllable-related condition. These results suggest that phonological activation is not mandatory and the role of phonology is minimal at best in reading Chinese two-character compounds.

Intrusive effects of semantic information on visual selective attention.

PubMed

Malcolm, George L; Rattinger, Michelle; Shomstein, Sarah

2016-10-01

Every object is represented by semantic information in extension to its low-level properties. It is well documented that such information biases attention when it is necessary for an ongoing task. However, whether semantic relationships influence attentional selection when they are irrelevant to the ongoing task remains an open question. The ubiquitous nature of semantic information suggests that it could bias attention even when these properties are irrelevant. In the present study, three objects appeared on screen, two of which were semantically related. After a varying time interval, a target or distractor appeared on top of each object. The objects' semantic relationships never predicted the target location. Despite this, a semantic bias on attentional allocation was observed, with an initial, transient bias to semantically related objects. Further experiments demonstrated that this effect was contingent on the objects being attended: if an object never contained the target, it no longer exerted a semantic influence. In a final set of experiments, we demonstrated that the semantic bias is robust and appears even in the presence of more predictive cues (spatial probability). These results suggest that as long as an object is attended, its semantic properties bias attention, even if it is irrelevant to an ongoing task and if more predictive factors are available.

Semantic querying of relational data for clinical intelligence: a semantic web services-based approach

PubMed Central

2013-01-01

Background Clinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management. Self-service ad hoc querying of clinical data is one desirable type of functionality. Since most of the data are currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas. Results A possible solution is semantic querying where the user formulates queries in terms of domain ontologies that are much easier to navigate and comprehend than data schemas. In this article, we are exploring the possibility of using SADI Semantic Web services for semantic querying of clinical data. We have developed a prototype of a semantic querying infrastructure for the surveillance of, and research on, hospital-acquired infections. Conclusions Our results suggest that SADI can support ad-hoc, self-service, semantic queries of relational data in a Clinical Intelligence context. The use of SADI compares favourably with approaches based on declarative semantic mappings from data schemas to ontologies, such as query rewriting and RDFizing by materialisation, because it can easily cope with situations when (i) some computation is required to turn relational data into RDF or OWL, e.g., to implement temporal reasoning, or (ii) integration with external data sources is necessary. PMID:23497556

Vowelling and semantic priming effects in Arabic.

PubMed

Mountaj, Nadia; El Yagoubi, Radouane; Himmi, Majid; Lakhdar Ghazal, Faouzi; Besson, Mireille; Boudelaa, Sami

2015-01-01

In the present experiment we used a semantic judgment task with Arabic words to determine whether semantic priming effects are found in the Arabic language. Moreover, we took advantage of the specificity of the Arabic orthographic system, which is characterized by a shallow (i.e., vowelled words) and a deep orthography (i.e., unvowelled words), to examine the relationship between orthographic and semantic processing. Results showed faster Reaction Times (RTs) for semantically related than unrelated words with no difference between vowelled and unvowelled words. By contrast, Event Related Potentials (ERPs) revealed larger N1 and N2 components to vowelled words than unvowelled words suggesting that visual-orthographic complexity taxes the early word processing stages. Moreover, semantically unrelated Arabic words elicited larger N400 components than related words thereby demonstrating N400 effects in Arabic. Finally, the Arabic N400 effect was not influenced by orthographic depth. The implications of these results for understanding the processing of orthographic, semantic, and morphological structures in Modern Standard Arabic are discussed. Copyright © 2014 Elsevier B.V. All rights reserved.

Methods and apparatus for capture and storage of semantic information with sub-files in a parallel computing system

DOEpatents

Faibish, Sorin; Bent, John M; Tzelnic, Percy; Grider, Gary; Torres, Aaron

2015-02-03

Techniques are provided for storing files in a parallel computing system using sub-files with semantically meaningful boundaries. A method is provided for storing at least one file generated by a distributed application in a parallel computing system. The file comprises one or more of a complete file and a plurality of sub-files. The method comprises the steps of obtaining a user specification of semantic information related to the file; providing the semantic information as a data structure description to a data formatting library write function; and storing the semantic information related to the file with one or more of the sub-files in one or more storage nodes of the parallel computing system. The semantic information provides a description of data in the file. The sub-files can be replicated based on semantically meaningful boundaries.

Why all the confusion? Experimental task explains discrepant semantic priming effects in schizophrenia under "automatic" conditions: evidence from Event-Related Potentials.

PubMed

Kreher, Donna A; Goff, Donald; Kuperberg, Gina R

2009-06-01

The schizophrenia research literature contains many differing accounts of semantic memory function in schizophrenia as assessed through the semantic priming paradigm. Most recently, Event-Related Potentials (ERPs) have been used to demonstrate both increased and decreased semantic priming at a neural level in schizophrenia patients, relative to healthy controls. The present study used ERPs to investigate the role of behavioral task in determining neural semantic priming effects in schizophrenia. The same schizophrenia patients and healthy controls completed two experiments in which word stimuli were identical, and the time between the onset of prime and target remained constant at 350 ms: in the first, participants monitored for words within a particular semantic category that appeared only in filler items (implicit task); in the second, participants explicitly rated the relatedness of word-pairs (explicit task). In the explicit task, schizophrenia patients showed reduced direct and indirect semantic priming in comparison with healthy controls. In contrast, in the implicit task, schizophrenia patients showed normal or, in positively thought-disordered patients, increased direct and indirect N400 priming effects compared with healthy controls. These data confirm that, although schizophrenia patients with positive thought disorder may show an abnormally increased automatic spreading activation, the introduction of semantic decision-making can result in abnormally reduced semantic priming in schizophrenia, even when other experimental conditions bias toward automatic processing.

CNTRO: A Semantic Web Ontology for Temporal Relation Inferencing in Clinical Narratives.

PubMed

Tao, Cui; Wei, Wei-Qi; Solbrig, Harold R; Savova, Guergana; Chute, Christopher G

2010-11-13

Using Semantic-Web specifications to represent temporal information in clinical narratives is an important step for temporal reasoning and answering time-oriented queries. Existing temporal models are either not compatible with the powerful reasoning tools developed for the Semantic Web, or designed only for structured clinical data and therefore are not ready to be applied on natural-language-based clinical narrative reports directly. We have developed a Semantic-Web ontology which is called Clinical Narrative Temporal Relation ontology. Using this ontology, temporal information in clinical narratives can be represented as RDF (Resource Description Framework) triples. More temporal information and relations can then be inferred by Semantic-Web based reasoning tools. Experimental results show that this ontology can represent temporal information in real clinical narratives successfully.

Controlled semantic cognition relies upon dynamic and flexible interactions between the executive 'semantic control' and hub-and-spoke 'semantic representation' systems.

PubMed

Chiou, Rocco; Humphreys, Gina F; Jung, JeYoung; Lambon Ralph, Matthew A

2018-06-01

Built upon a wealth of neuroimaging, neurostimulation, and neuropsychology data, a recent proposal set forth a framework termed controlled semantic cognition (CSC) to account for how the brain underpins the ability to flexibly use semantic knowledge (Lambon Ralph et al., 2017; Nature Reviews Neuroscience). In CSC, the 'semantic control' system, underpinned predominantly by the prefrontal cortex, dynamically monitors and modulates the 'semantic representation' system that consists of a 'hub' (anterior temporal lobe, ATL) and multiple 'spokes' (modality-specific areas). CSC predicts that unfamiliar and exacting semantic tasks should intensify communication between the 'control' and 'representation' systems, relative to familiar and less taxing tasks. In the present study, we used functional magnetic resonance imaging (fMRI) to test this hypothesis. Participants paired unrelated concepts by canonical colours (a less accustomed task - e.g., pairing ketchup with fire-extinguishers due to both being red) or paired well-related concepts by semantic relationship (a typical task - e.g., ketchup is related to mustard). We found the 'control' system was more engaged by atypical than typical pairing. While both tasks activated the ATL 'hub', colour pairing additionally involved occipitotemporal 'spoke' regions abutting areas of hue perception. Furthermore, we uncovered a gradient along the ventral temporal cortex, transitioning from the caudal 'spoke' zones preferring canonical colour processing to the rostral 'hub' zones preferring semantic relationship. Functional connectivity also differed between the tasks: Compared with semantic pairing, colour pairing relied more upon the inferior frontal gyrus, a key node of the control system, driving enhanced connectivity with occipitotemporal 'spoke'. Together, our findings characterise the interaction within the neural architecture of semantic cognition - the control system dynamically heightens its connectivity with relevant components of the representation system, in response to different semantic contents and difficulty levels. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Model Driven Engineering

NASA Astrophysics Data System (ADS)

Gaševic, Dragan; Djuric, Dragan; Devedžic, Vladan

A relevant initiative from the software engineering community called Model Driven Engineering (MDE) is being developed in parallel with the Semantic Web (Mellor et al. 2003a). The MDE approach to software development suggests that one should first develop a model of the system under study, which is then transformed into the real thing (i.e., an executable software entity). The most important research initiative in this area is the Model Driven Architecture (MDA), which is Model Driven Architecture being developed under the umbrella of the Object Management Group (OMG). This chapter describes the basic concepts of this software engineering effort.

[Classification of memory systems: a revision].

PubMed

Agrest, M

2001-12-01

The present paper exposes the arguments against considering memory as a monolytic entity and how is it to be divided into several systems in order to understand its operation. Historically this division was acknowledge by different authors but in the last few decades it received the confirmation from the scientific research. The most accepted taxonomy establishes the existence of two major memory systems: declarative and non declarative memory. The article also presents the arguments for and against this kind of division, as well as an alternative classification in five major systems: procedural, perceptual representation, semantic, primary and episodic.

Multivariate Pattern Analysis Reveals Category-Related Organization of Semantic Representations in Anterior Temporal Cortex.

PubMed

Malone, Patrick S; Glezer, Laurie S; Kim, Judy; Jiang, Xiong; Riesenhuber, Maximilian

2016-09-28

The neural substrates of semantic representation have been the subject of much controversy. The study of semantic representations is complicated by difficulty in disentangling perceptual and semantic influences on neural activity, as well as in identifying stimulus-driven, "bottom-up" semantic selectivity unconfounded by top-down task-related modulations. To address these challenges, we trained human subjects to associate pseudowords (TPWs) with various animal and tool categories. To decode semantic representations of these TPWs, we used multivariate pattern classification of fMRI data acquired while subjects performed a semantic oddball detection task. Crucially, the classifier was trained and tested on disjoint sets of TPWs, so that the classifier had to use the semantic information from the training set to correctly classify the test set. Animal and tool TPWs were successfully decoded based on fMRI activity in spatially distinct subregions of the left medial anterior temporal lobe (LATL). In addition, tools (but not animals) were successfully decoded from activity in the left inferior parietal lobule. The tool-selective LATL subregion showed greater functional connectivity with left inferior parietal lobule and ventral premotor cortex, indicating that each LATL subregion exhibits distinct patterns of connectivity. Our findings demonstrate category-selective organization of semantic representations in LATL into spatially distinct subregions, continuing the lateral-medial segregation of activation in posterior temporal cortex previously observed in response to images of animals and tools, respectively. Together, our results provide evidence for segregation of processing hierarchies for different classes of objects and the existence of multiple, category-specific semantic networks in the brain. The location and specificity of semantic representations in the brain are still widely debated. We trained human participants to associate specific pseudowords with various animal and tool categories, and used multivariate pattern classification of fMRI data to decode the semantic representations of the trained pseudowords. We found that: (1) animal and tool information was organized in category-selective subregions of medial left anterior temporal lobe (LATL); (2) tools, but not animals, were encoded in left inferior parietal lobe; and (3) LATL subregions exhibited distinct patterns of functional connectivity with category-related regions across cortex. Our findings suggest that semantic knowledge in LATL is organized in category-related subregions, providing evidence for the existence of multiple, category-specific semantic representations in the brain. Copyright © 2016 the authors 0270-6474/16/3610089-08$15.00/0.

Automated Semantic Indices Related to Cognitive Function and Rate of Cognitive Decline

ERIC Educational Resources Information Center

Pakhomov, Serguei V. S.; Hemmy, Laura S.; Lim, Kelvin O.

2012-01-01

The objective of our study is to introduce a fully automated, computational linguistic technique to quantify semantic relations between words generated on a standard semantic verbal fluency test and to determine its cognitive and clinical correlates. Cognitive differences between patients with Alzheimer's disease and mild cognitive impairment are…

How Different Types of Conceptual Relations Modulate Brain Activation during Semantic Priming

ERIC Educational Resources Information Center

Sachs, Olga; Weis, Susanne; Zellagui, Nadia; Sass, Katharina; Huber, Walter; Zvyagintsev, Mikhail; Mathiak, Klaus; Kircher, Tilo

2011-01-01

Semantic priming, a well-established technique to study conceptual representation, has thus far produced variable fMRI results, both regarding the type of priming effects and their correlation with brain activation. The aims of the current study were (a) to investigate two types of semantic relations--categorical versus associative--under…

Semantic Processing in Children and Adults: Incongruity and the N400

ERIC Educational Resources Information Center

Benau, Erik M.; Morris, Joanna; Couperus, J. W.

2011-01-01

Semantic processing in 10-year-old children and adults was examined using event related potentials (ERPs). The N400 component, an index of semantic processing, was studied in relation to sentences that ended with congruent, moderately incongruent, or strongly incongruent words. N400 amplitude in adults corresponded to levels of semantic…

A trade-off between local and distributed information processing associated with remote episodic versus semantic memory.

PubMed

Heisz, Jennifer J; Vakorin, Vasily; Ross, Bernhard; Levine, Brian; McIntosh, Anthony R

2014-01-01

Episodic memory and semantic memory produce very different subjective experiences yet rely on overlapping networks of brain regions for processing. Traditional approaches for characterizing functional brain networks emphasize static states of function and thus are blind to the dynamic information processing within and across brain regions. This study used information theoretic measures of entropy to quantify changes in the complexity of the brain's response as measured by magnetoencephalography while participants listened to audio recordings describing past personal episodic and general semantic events. Personal episodic recordings evoked richer subjective mnemonic experiences and more complex brain responses than general semantic recordings. Critically, we observed a trade-off between the relative contribution of local versus distributed entropy, such that personal episodic recordings produced relatively more local entropy whereas general semantic recordings produced relatively more distributed entropy. Changes in the relative contributions of local and distributed entropy to the total complexity of the system provides a potential mechanism that allows the same network of brain regions to represent cognitive information as either specific episodes or more general semantic knowledge.

Retrieval from Memory: Vulnerable or Inviolable?

ERIC Educational Resources Information Center

Jones, Dylan M.; Marsh, John E.; Hughes, Robert W.

2012-01-01

We show that retrieval from semantic memory is vulnerable even to the mere presence of speech. Irrelevant speech impairs semantic fluency--namely, lexical retrieval cued by a semantic category name--but only if it is meaningful (forward speech compared to reversed speech or words compared to nonwords). Moreover, speech related semantically to the…

Semantic Boost on Episodic Associations: An Empirically-Based Computational Model

ERIC Educational Resources Information Center

Silberman, Yaron; Bentin, Shlomo; Miikkulainen, Risto

2007-01-01

Words become associated following repeated co-occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words…

SEMANTICS AND CRITICAL READING.

ERIC Educational Resources Information Center

FLANIGAN, MICHAEL C.

PROFICIENCY IN CRITICAL READING CAN BE ACCELERATED BY MAKING STUDENTS AWARE OF VARIOUS SEMANTIC DEVICES THAT HELP CLARIFY MEANINGS AND PURPOSES. EXCERPTS FROM THE ARTICLE "TEEN-AGE CORRUPTION" FROM THE NINTH-GRADE SEMANTICS UNIT WRITTEN BY THE PROJECT ENGLISH DEMONSTRATION CENTER AT EUCLID, OHIO, ARE USED TO ILLUSTRATE HOW SEMANTICS RELATE TO…

Language abnormality in deaf people with schizophrenia: a problem with classifiers.

PubMed

Chatzidamianos, G; McCarthy, R A; Du Feu, M; Rosselló, J; McKenna, P J

2018-06-05

Although there is evidence for language abnormality in schizophrenia, few studies have examined sign language in deaf patients with the disorder. This is of potential interest because a hallmark of sign languages is their use of classifiers (semantic or entity classifiers), a reference-tracking device with few if any parallels in spoken languages. This study aimed to examine classifier production and comprehension in deaf signing adults with schizophrenia. Fourteen profoundly deaf signing adults with schizophrenia and 35 age- and IQ-matched deaf healthy controls completed a battery of tests assessing classifier and noun comprehension and production. The patients showed poorer performance than the healthy controls on comprehension and production of both nouns and entity classifiers, with the deficit being most marked in the production of classifiers. Classifier production errors affected handshape rather than other parameters such as movement and location. The findings suggest that schizophrenia affects language production in deaf patients with schizophrenia in a unique way not seen in hearing patients.

Social media analysis during political turbulence

PubMed Central

Spiliotopoulos, Dimitris; V. Samaras, Christos; Pratikakis, Polyvios; Ioannidis, Sotiris; Fragopoulou, Paraskevi

2017-01-01

Today, a considerable proportion of the public political discourse on nationwide elections proceeds in Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis, sarcasm correction and topic modeling. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions. PMID:29088263

Prediction in the Processing of Repair Disfluencies: Evidence from the Visual-World Paradigm

PubMed Central

Lowder, Matthew W.; Ferreira, Fernanda

2016-01-01

Two visual-world eye-tracking experiments investigated the role of prediction in the processing of repair disfluencies (e.g., The chef reached for some salt uh I mean some ketchup…). Experiment 1 showed that listeners were more likely to fixate a critical distractor item (e.g., pepper) during the processing of repair disfluencies compared to the processing of coordination structures (e.g., …some salt and also some ketchup…). Experiment 2 replicated the findings of Experiment 1 for disfluency versus coordination constructions and also showed that the pattern of fixations to the critical distractor for disfluency constructions was similar to the fixation patterns for sentences employing contrastive focus (e.g., …not some salt but rather some ketchup…). The results suggest that similar mechanisms underlie the processing of repair disfluencies and contrastive focus, with listeners generating sets of entities that stand in semantic contrast to the reparandum in the case of disfluencies or the negated entity in the case of contrastive focus. PMID:26866657

The NCGC Pharmaceutical Collection: A comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics

PubMed Central

Huang, Ruili; Southall, Noel; Wang, Yuhong; Yasgar, Adam; Shinn, Paul; Jadhav, Ajit; Nguyen, Dac-Trung; Austin, Christopher P.

2011-01-01

Small-molecule compounds approved for use as drugs may be “repurposed” for new indications and studied to determine the mechanisms of their beneficial and adverse effects. A comprehensive collection of all small-molecule drugs approved for human use would be invaluable for systematic repurposing across human diseases, particularly for rare and neglected diseases, for which the cost and time required for development of a new chemical entity are often prohibitive. Previous efforts to build such a comprehensive collection have been limited by the complexities, redundancies, and semantic inconsistencies of drug naming within and among regulatory agencies worldwide; a lack of clear conceptualization of what constitutes a drug; and a lack of access to physical samples. We report here the creation of a definitive, complete, and nonredundant list of all approved molecular entities as a freely available electronic resource and a physical collection of small molecules amenable to high-throughput screening. PMID:21525397

Social media analysis during political turbulence.

PubMed

Antonakaki, Despoina; Spiliotopoulos, Dimitris; V Samaras, Christos; Pratikakis, Polyvios; Ioannidis, Sotiris; Fragopoulou, Paraskevi

2017-01-01

Today, a considerable proportion of the public political discourse on nationwide elections proceeds in Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis, sarcasm correction and topic modeling. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions.

Auditing the NCI Thesaurus with Semantic Web Technologies

PubMed Central

Mougin, Fleur; Bodenreider, Olivier

2008-01-01

Auditing biomedical terminologies often results in the identification of inconsistencies and thus helps to improve their quality. In this paper, we present a method based on Semantic Web technologies for auditing biomedical terminologies and apply it to the NCI thesaurus. We stored the NCI thesaurus concepts and their properties in an RDF triple store. By querying this store, we assessed the consistency of both hierarchical and associative relations from the NCI thesaurus among themselves and with corresponding relations in the UMLS Semantic Network. We show that the consistency is better for associative relations than for hierarchical relations. Causes for inconsistency and benefits from using Semantic Web technologies for auditing purposes are discussed. PMID:18999265

Auditing the NCI thesaurus with semantic web technologies.

PubMed

Mougin, Fleur; Bodenreider, Olivier

2008-11-06

Auditing biomedical terminologies often results in the identification of inconsistencies and thus helps to improve their quality. In this paper, we present a method based on Semantic Web technologies for auditing biomedical terminologies and apply it to the NCI thesaurus. We stored the NCI thesaurus concepts and their properties in an RDF triple store. By querying this store, we assessed the consistency of both hierarchical and associative relations from the NCI thesaurus among themselves and with corresponding relations in the UMLS Semantic Network. We show that the consistency is better for associative relations than for hierarchical relations. Causes for inconsistency and benefits from using Semantic Web technologies for auditing purposes are discussed.

The Influence of Concreteness of Concepts on the Integration of Novel Words into the Semantic Network

PubMed Central

Ding, Jinfeng; Liu, Wenjuan; Yang, Yufang

2017-01-01

On the basis of previous studies revealing a processing advantage of concrete words over abstract words, the current study aimed to further explore the influence of concreteness on the integration of novel words into semantic memory with the event related potential (ERP) technique. In the experiment during the learning phase participants read two-sentence contexts and inferred the meaning of novel words. The novel words were two-character non-words in Chinese language. Their meaning was either a concrete or abstract known concept which could be inferred from the contexts. During the testing phase participants performed a lexical decision task in which the learned novel words served as primes for either their corresponding concepts, semantically related or unrelated targets. For the concrete novel words, the semantically related words belonged to the same semantic categories with their corresponding concepts. For the abstract novel words, the semantically related words were synonyms of their corresponding concepts. The unrelated targets were real words which were concrete or abstract for the concrete or abstract novel words respectively. The ERP results showed that the corresponding concepts and the semantically related words elicited smaller N400s than the unrelated words. The N400 effect was not modulated by the concreteness of the concepts. In addition, the concrete corresponding concepts elicited a smaller late positive component (LPC) than the concrete unrelated words. This LPC effect was absent for the abstract words. The results indicate that although both concrete and abstract novel words can be acquired and linked to their related words in the semantic network after a short learning phase, the concrete novel words are learned better. Our findings support the (extended) dual coding theory and broaden our understanding of adult word learning and changes in concept organization. PMID:29255440

The Influence of Concreteness of Concepts on the Integration of Novel Words into the Semantic Network.

PubMed

Ding, Jinfeng; Liu, Wenjuan; Yang, Yufang

2017-01-01

On the basis of previous studies revealing a processing advantage of concrete words over abstract words, the current study aimed to further explore the influence of concreteness on the integration of novel words into semantic memory with the event related potential (ERP) technique. In the experiment during the learning phase participants read two-sentence contexts and inferred the meaning of novel words. The novel words were two-character non-words in Chinese language. Their meaning was either a concrete or abstract known concept which could be inferred from the contexts. During the testing phase participants performed a lexical decision task in which the learned novel words served as primes for either their corresponding concepts, semantically related or unrelated targets. For the concrete novel words, the semantically related words belonged to the same semantic categories with their corresponding concepts. For the abstract novel words, the semantically related words were synonyms of their corresponding concepts. The unrelated targets were real words which were concrete or abstract for the concrete or abstract novel words respectively. The ERP results showed that the corresponding concepts and the semantically related words elicited smaller N400s than the unrelated words. The N400 effect was not modulated by the concreteness of the concepts. In addition, the concrete corresponding concepts elicited a smaller late positive component (LPC) than the concrete unrelated words. This LPC effect was absent for the abstract words. The results indicate that although both concrete and abstract novel words can be acquired and linked to their related words in the semantic network after a short learning phase, the concrete novel words are learned better. Our findings support the (extended) dual coding theory and broaden our understanding of adult word learning and changes in concept organization.

ADO: a disease ontology representing the domain knowledge specific to Alzheimer's disease.

PubMed

Malhotra, Ashutosh; Younesi, Erfan; Gündel, Michaela; Müller, Bernd; Heneka, Michael T; Hofmann-Apitius, Martin

2014-03-01

Biomedical ontologies offer the capability to structure and represent domain-specific knowledge semantically. Disease-specific ontologies can facilitate knowledge exchange across multiple disciplines, and ontology-driven mining approaches can generate great value for modeling disease mechanisms. However, in the case of neurodegenerative diseases such as Alzheimer's disease, there is a lack of formal representation of the relevant knowledge domain. Alzheimer's disease ontology (ADO) is constructed in accordance to the ontology building life cycle. The Protégé OWL editor was used as a tool for building ADO in Ontology Web Language format. ADO was developed with the purpose of containing information relevant to four main biological views-preclinical, clinical, etiological, and molecular/cellular mechanisms-and was enriched by adding synonyms and references. Validation of the lexicalized ontology by means of named entity recognition-based methods showed a satisfactory performance (F score = 72%). In addition to structural and functional evaluation, a clinical expert in the field performed a manual evaluation and curation of ADO. Through integration of ADO into an information retrieval environment, we show that the ontology supports semantic search in scientific text. The usefulness of ADO is authenticated by dedicated use case scenarios. Development of ADO as an open ADO is a first attempt to organize information related to Alzheimer's disease in a formalized, structured manner. We demonstrate that ADO is able to capture both established and scattered knowledge existing in scientific text. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Common world model for unmanned systems: Phase 2

NASA Astrophysics Data System (ADS)

Dean, Robert M. S.; Oh, Jean; Vinokurov, Jerry

2014-06-01

The Robotics Collaborative Technology Alliance (RCTA) seeks to provide adaptive robot capabilities which move beyond traditional metric algorithms to include cognitive capabilities. Key to this effort is the Common World Model, which moves beyond the state-of-the-art by representing the world using semantic and symbolic as well as metric information. It joins these layers of information to define objects in the world. These objects may be reasoned upon jointly using traditional geometric, symbolic cognitive algorithms and new computational nodes formed by the combination of these disciplines to address Symbol Grounding and Uncertainty. The Common World Model must understand how these objects relate to each other. It includes the concept of Self-Information about the robot. By encoding current capability, component status, task execution state, and their histories we track information which enables the robot to reason and adapt its performance using Meta-Cognition and Machine Learning principles. The world model also includes models of how entities in the environment behave which enable prediction of future world states. To manage complexity, we have adopted a phased implementation approach. Phase 1, published in these proceedings in 2013 [1], presented the approach for linking metric with symbolic information and interfaces for traditional planners and cognitive reasoning. Here we discuss the design of "Phase 2" of this world model, which extends the Phase 1 design API, data structures, and reviews the use of the Common World Model as part of a semantic navigation use case.

A category-specific advantage for numbers in verbal short-term memory: evidence from semantic dementia.

PubMed

Jefferies, Elizabeth; Patterson, Karalyn; Jones, Roy W; Bateman, David; Lambon Ralph, Matthew A

2004-01-01

This study explored possible reasons for the striking difference between digit span and word span in patients with semantic dementia. Immediate serial recall (ISR) of number and non-number words was examined in four patients. For every case, the recall of single-digit numbers was normal whereas the recall of non-number words was impaired relative to controls. This difference extended to multi-digit numbers, and remained even when frequency, imageability, word length, set size and size of semantic category were matched for the numbers and words. The advantage for number words also applied to the patients' reading performance. Previous studies have suggested that semantic memory plays a critical role in verbal short-term memory (STM) and reading: patients with semantic dementia show superior recall and reading of words that are still relatively well known compared to previously known but now semantically degraded words. Additional assessments suggested that this semantic locus was the basis of the patients' category-specific advantage for numbers. Comprehension was considerably better for number than non-number words. Number knowledge may be relatively preserved in semantic dementia because the cortical atrophy underlying the condition typically spares the areas of the parietal lobes thought to be crucial in numerical cognition but involves the inferolateral temporal-lobes known to support general conceptual knowledge.

Distinct neural substrates for semantic knowledge and naming in the temporoparietal network.

PubMed

Gesierich, Benno; Jovicich, Jorge; Riello, Marianna; Adriani, Michela; Monti, Alessia; Brentari, Valentina; Robinson, Simon D; Wilson, Stephen M; Fairhall, Scott L; Gorno-Tempini, Maria Luisa

2012-10-01

Patients with anterior temporal lobe (ATL) lesions show semantic and lexical retrieval deficits, and the differential role of this area in the 2 processes is debated. Functional neuroimaging in healthy individuals has not clarified the matter because semantic and lexical processes usually occur simultaneously and automatically. Furthermore, the ATL is a region challenging for functional magnetic resonance imaging (fMRI) due to susceptibility artifacts, especially at high fields. In this study, we established an optimized ATL-sensitive fMRI acquisition protocol at 4 T and applied an event-related paradigm to study the identification (i.e., association of semantic biographical information) of celebrities, with and without the ability to retrieve their proper names. While semantic processing reliably activated the ATL, only more posterior areas in the left temporal and temporal-parietal junction were significantly modulated by covert lexical retrieval. These results suggest that within a temporoparietal network, the ATL is relatively more important for semantic processing, and posterior language regions are relatively more important for lexical retrieval.

When bees hamper the production of honey: lexical interference from associates in speech production.

PubMed

Abdel Rahman, Rasha; Melinger, Alissa

2007-05-01

In this article, the authors explore semantic context effects in speaking. In particular, the authors investigate a marked discrepancy between categorically and associatively induced effects; only categorical relationships have been reported to cause interference in object naming. In Experiments 1 and 2, a variant of the semantic blocking paradigm was used to induce two different types of semantic context effects. Pictures were either named in the context of categorically related objects (e.g., animals: bee, cow, fish) or in the context of associatively related objects from different semantic categories (e.g., apiary: bee, honey, bee keeper). Semantic interference effects were observed in both conditions, relative to an unrelated context. Experiment 3 replicated the classic effects of categorical interference and associative facilitation in a picture-word interference paradigm with the material used in Experiment 2. These findings suggest that associates are active lexical competitors and that the microstructure of lexicalization is highly flexible and adjustable to the semantic context in which the utterance takes place.

Lexical-semantic processing in the semantic priming paradigm in aphasic patients.

PubMed

Salles, Jerusa Fumagalli de; Holderbaum, Candice Steffen; Parente, Maria Alice Mattos Pimenta; Mansur, Letícia Lessa; Ansaldo, Ana Inès

2012-09-01

There is evidence that the explicit lexical-semantic processing deficits which characterize aphasia may be observed in the absence of implicit semantic impairment. The aim of this article was to critically review the international literature on lexical-semantic processing in aphasia, as tested through the semantic priming paradigm. Specifically, this review focused on aphasia and lexical-semantic processing, the methodological strengths and weaknesses of the semantic paradigms used, and recent evidence from neuroimaging studies on lexical-semantic processing. Furthermore, evidence on dissociations between implicit and explicit lexical-semantic processing reported in the literature will be discussed and interpreted by referring to functional neuroimaging evidence from healthy populations. There is evidence that semantic priming effects can be found both in fluent and in non-fluent aphasias, and that these effects are related to an extensive network which includes the temporal lobe, the pre-frontal cortex, the left frontal gyrus, the left temporal gyrus and the cingulated cortex.

Semantic information mediates visual attention during spoken word recognition in Chinese: Evidence from the printed-word version of the visual-world paradigm.

PubMed

Shen, Wei; Qu, Qingqing; Li, Xingshan

2016-07-01

In the present study, we investigated whether the activation of semantic information during spoken word recognition can mediate visual attention's deployment to printed Chinese words. We used a visual-world paradigm with printed words, in which participants listened to a spoken target word embedded in a neutral spoken sentence while looking at a visual display of printed words. We examined whether a semantic competitor effect could be observed in the printed-word version of the visual-world paradigm. In Experiment 1, the relationship between the spoken target words and the printed words was manipulated so that they were semantically related (a semantic competitor), phonologically related (a phonological competitor), or unrelated (distractors). We found that the probability of fixations on semantic competitors was significantly higher than that of fixations on the distractors. In Experiment 2, the orthographic similarity between the spoken target words and their semantic competitors was manipulated to further examine whether the semantic competitor effect was modulated by orthographic similarity. We found significant semantic competitor effects regardless of orthographic similarity. Our study not only reveals that semantic information can affect visual attention, it also provides important new insights into the methodology employed to investigate the semantic processing of spoken words during spoken word recognition using the printed-word version of the visual-world paradigm.

Ontology Based Vocabulary Matching for Oceanographic Instruments

NASA Astrophysics Data System (ADS)

Chen, Yu; Shepherd, Adam; Chandler, Cyndy; Arko, Robert; Leadbetter, Adam

2014-05-01

Data integration act as the preliminary entry point as we enter the era of big data in many scientific domains. However the reusefulness of various dataset has met the hurdle due to different initial of interests of different parties, therefore different vocabularies in describing similar or semantically related concepts. In this scenario it is vital to devise an automatic or semi-supervised algorithm to facilitate the convergence of different vocabularies. The Ocean Data Interoperability Platform (ODIP) seeks to increase data sharing across scientific domains and international boundaries by providing a forum to harmonize diverse regional data systems. ODIP participants from the US include the Rolling Deck to Repository (R2R) program, whose mission is to capture, catalog, and describe the underway/environmental sensor data from US oceanographic research vessels and submit the data to public long-term archives. In an attempt to harmonize these regional data systems, especially vocabularies, R2R recognizes the value of the SeaDataNet vocabularies served by the NERC Vocabulary Server (NVS) hosted at the British Oceanographic Data Centre as a trusted, authoritative source for describing many oceanographic research concepts such as instrumentation. In this work, we make use of the semantic relations in the vocabularies served by NVS to build a Bayesian network and take advantage of the idea of entropy in evaluating the correlation between different concepts and keywords. The performance of the model is evaluated against matching instruments from R2R against the SeaDataNet instrument vocabularies based on calculated confidence scores in the instrument pairings. These pairings with their scores can then be analyzed for assertion growing the interoperability of the R2R vocabulary through its links to the SeaDataNet entities.

How Semantic Radicals in Chinese characters Facilitate Hierarchical Category-Based Induction.

PubMed

Wang, Xiaoxi; Ma, Xie; Tao, Yun; Tao, Yachen; Li, Hong

2018-04-03

Prior studies indicate that the semantic radical in Chinese characters contains category information that can support the independent retrieval of category information through the lexical network to the conceptual network. Inductive reasoning relies on category information; thus, semantic radicals may influence inductive reasoning. As most natural concepts are hierarchically structured in the human brain, this study examined how semantic radicals impact inductive reasoning for hierarchical concepts. The study used animal and plant nouns, organized in basic, superordinate, and subordinate levels; half had a semantic radical and half did not. Eighteen participants completed an inductive reasoning task. Behavioural and event-related potential (ERP) data were collected. The behavioural results showed that participants reacted faster and more accurately in the with-semantic-radical condition than in the without-semantic-radical condition. For the ERPs, differences between the conditions were found, and these differences lasted from the very early cognitive processing stage (i.e., the N1 time window) to the relatively late processing stages (i.e., the N400 and LPC time windows). Semantic radicals can help to distinguish the hierarchies earlier (in the N400 period) than characters without a semantic radical (in the LPC period). These results provide electrophysiological evidence that semantic radicals may improve sensitivity to distinguish between hierarchical concepts.

Grammatical markers switch roles and elicit different electrophysiological responses under shallow and deep semantic requirements.

PubMed

Soshi, Takahiro; Nakajima, Heizo; Hagiwara, Hiroko

2016-10-01

Static knowledge about the grammar of a natural language is represented in the cortico-subcortical system. However, the differences in dynamic verbal processing under different cognitive conditions are unclear. To clarify this, we conducted an electrophysiological experiment involving a semantic priming paradigm in which semantically congruent or incongruent word sequences (prime nouns-target verbs) were randomly presented. We examined the event-related brain potentials that occurred in response to congruent and incongruent target words that were preceded by primes with or without grammatical case markers. The two participant groups performed either the shallow (lexical judgment) or deep (direct semantic judgment) semantic tasks. We hypothesized that, irrespective of the case markers, the congruent targets would reduce centro-posterior N400 activities under the deep semantic condition, which induces selective attention to the semantic relatedness of content words. However, the same congruent targets with correct case markers would reduce lateralized negativity under the shallow semantic condition because grammatical case markers are related to automatic structural integration under semantically unattended conditions. We observed that congruent targets (e.g., 'open') that were preceded by primes with congruent case markers (e.g., 'shutter-object case') reduced lateralized negativity under the shallow semantic condition. In contrast, congruent targets, irrespective of case markers, consistently yielded N400 reductions under the deep semantic condition. To summarize, human neural verbal processing differed in response to the same grammatical markers in the same verbal expressions under semantically attended or unattended conditions.

Interactive entity resolution in relational data: a visual analytic tool and its evaluation.

PubMed

Kang, Hyunmo; Getoor, Lise; Shneiderman, Ben; Bilgic, Mustafa; Licamele, Louis

2008-01-01

Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

Research on geo-ontology construction based on spatial affairs

NASA Astrophysics Data System (ADS)

Li, Bin; Liu, Jiping; Shi, Lihong

2008-12-01

Geo-ontology, a kind of domain ontology, is used to make the knowledge, information and data of concerned geographical science in the abstract to form a series of single object or entity with common cognition. These single object or entity can compose a specific system in some certain way and can be disposed on conception and given specific definition at the same time. Ultimately, these above-mentioned worked results can be expressed in some manners of formalization. The main aim of constructing geo-ontology is to get the knowledge of the domain of geography, and provide the commonly approbatory vocabularies in the domain, as well as give the definite definition about these geographical vocabularies and mutual relations between them in the mode of formalization at different hiberarchy. Consequently, the modeling tool of conception model of describing geographic Information System at the hiberarchy of semantic meaning and knowledge can be provided to solve the semantic conception of information exchange in geographical space and make them possess the comparatively possible characters of accuracy, maturity and universality, etc. In fact, some experiments have been made to validate geo-ontology. During the course of studying, Geo-ontology oriented to flood can be described and constructed by making the method based on geo-spatial affairs to serve the governmental departments at all levels to deal with flood. Thereinto, intelligent retrieve and service based on geoontology of disaster are main functions known from the traditional manner by using keywords. For instance, the function of dealing with disaster information based on geo-ontology can be provided when a supposed flood happened in a certain city. The correlative officers can input some words, such as "city name, flood", which have been realized semantic label, to get the information they needed when they browse different websites. The information, including basic geographical information and flood distributing and change about flood with different scales and ranges in the city, can be distilled intellectively and on its own initiative from the geo-ontology database. Besides, correlative statistical information can also be provided to the governmental departments at all levels to help them to carry out timely measures of fighting back disaster and rescue. Compared with the past manners, the efficiency of dealing with flood information has been improved to some extent than ever because plenty of information irrespective and interferential to flood in different websites can be sieved in advance based on the retrieve method oriented to Geo-ontology. In a word, it will take the pursuers long time to study geo-ontology due to actual limited resource. But then, geo-ontology will be sure to further perfect correspondingly especially in the field of Geographic Information System owing to its more and more factual applications.

KneeTex: an ontology-driven system for information extraction from MRI reports.

PubMed

Spasić, Irena; Zhao, Bo; Jones, Christopher B; Button, Kate

2015-01-01

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00 %, recall of 97.63 % and F-measure of 97.81 %, the values of which are in line with human-like performance. KneeTex is an open-source, stand-alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

TaggerOne: joint named entity recognition and normalization with semi-Markov Models

PubMed Central

Leaman, Robert; Lu, Zhiyong

2016-01-01

Motivation: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. Methods: We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. Results: We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. Availability and Implementation: The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone Contact: zhiyong.lu@nih.gov Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27283952

TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

PubMed

Leaman, Robert; Lu, Zhiyong

2016-09-15

Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone zhiyong.lu@nih.gov Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

Linguistic and Non-Linguistic Semantic Processing in Individuals with Autism Spectrum Disorders: An ERP Study.

PubMed

Coderre, Emily L; Chernenok, Mariya; Gordon, Barry; Ledoux, Kerry

2017-03-01

Individuals with autism spectrum disorders (ASD) experience difficulties with language, particularly higher-level functions like semantic integration. Yet some studies indicate that semantic processing of non-linguistic stimuli is not impaired, suggesting a language-specific deficit in semantic processing. Using a semantic priming task, we compared event-related potentials (ERPs) in response to lexico-semantic processing (written words) and visuo-semantic processing (pictures) in adults with ASD and adults with typical development (TD). The ASD group showed successful lexico-semantic and visuo-semantic processing, indicated by similar N400 effects between groups for word and picture stimuli. However, differences in N400 latency and topography in word conditions suggested different lexico-semantic processing mechanisms: an expectancy-based strategy for the TD group but a controlled post-lexical integration strategy for the ASD group.

EEG Theta and Alpha Responses Reveal Qualitative Differences in Processing Taxonomic versus Thematic Semantic Relationships

ERIC Educational Resources Information Center

Maguire, Mandy J.; Brier, Matthew R.; Ferree, Thomas C.

2010-01-01

Despite the importance of semantic relationships to our understanding of semantic knowledge, the nature of the neural processes underlying these abilities are not well understood. In order to investigate these processes, 20 healthy adults listened to thematically related (e.g., leash-dog), taxonomically related (e.g., horse-dog), or unrelated…

Semantically Induced Distortions of Visual Awareness in a Patient with Balint's Syndrome

ERIC Educational Resources Information Center

Soto, David; Humphreys, Glyn W.

2009-01-01

We present data indicating that visual awareness for a basic perceptual feature (colour) can be influenced by the relation between the feature and the semantic properties of the stimulus. We examined semantic interference from the meaning of a colour word ("RED") on simple colour (ink related) detection responses in a patient with simultagnosia…

Do U Txt? Event-Related Potentials to Semantic Anomalies in Standard and Texted English

ERIC Educational Resources Information Center

Berger, Natalie I.; Coch, Donna

2010-01-01

Texted English is a hybrid, technology-based language derived from standard English modified to facilitate ease of communication via instant and text messaging. We compared semantic processing of texted and standard English sentences by recording event-related potentials in a classic semantic incongruity paradigm designed to elicit an N400 effect.…

Behavioral and fMRI Evidence that Cognitive Ability Modulates the Effect of Semantic Context on Speech Intelligibility

ERIC Educational Resources Information Center

Zekveld, Adriana A.; Rudner, Mary; Johnsrude, Ingrid S.; Heslenfeld, Dirk J.; Ronnberg, Jerker

2012-01-01

Text cues facilitate the perception of spoken sentences to which they are semantically related (Zekveld, Rudner, et al., 2011). In this study, semantically related and unrelated cues preceding sentences evoked more activation in middle temporal gyrus (MTG) and inferior frontal gyrus (IFG) than nonword cues, regardless of acoustic quality (speech…

Implicit Phonological and Semantic Processing in Children with Developmental Dyslexia: Evidence from Event-Related Potentials

ERIC Educational Resources Information Center

Jednorog, K.; Marchewka, A.; Tacikowski, P.; Grabowska, A.

2010-01-01

Dyslexia is characterized by a core phonological deficit, although recent studies indicate that semantic impairment also contributes to this condition. In this study, event-related potentials (ERP) were used to examine whether the N400 wave in dyslexic children is modulated by phonological or semantic priming, similarly to age-matched controls.…

Phonetic Pause Unites Phonology and Semantics against Morphology and Syntax

ERIC Educational Resources Information Center

Sakarna, Ahmad Khalaf; Mobaideen, Adnan

2012-01-01

The present study investigates the phonological effect triggered by the different types of phonetic pause used in Quran on morphology, syntax, and semantics. It argues that Quranic pause provides interesting evidence about the close relation between phonology and semantics, from one side, and semantics, morphology, and syntax, from the other…

Influences of Semantic and Prosodic Cues on Word Repetition and Categorization in Autism

ERIC Educational Resources Information Center

Singh, Leher; Harrow, MariLouise S.

2014-01-01

Purpose: To investigate sensitivity to prosodic and semantic cues to emotion in individuals with high-functioning autism (HFA). Method: Emotional prosody and semantics were independently manipulated to assess the relative influence of prosody versus semantics on speech processing. A sample of 10-year-old typically developing children (n = 10) and…

Transformation of standardized clinical models based on OWL technologies: from CEM to OpenEHR archetypes.

PubMed

Legaz-García, María del Carmen; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás; Chute, Christopher G; Tao, Cui

2015-05-01

The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies have frequently been used in semantic interoperability efforts. The objective of this paper is to propose a generic, ontology-based, flexible approach for supporting the automatic transformation of clinical models, which is illustrated for the transformation of Clinical Element Models (CEMs) into openEHR archetypes. Our transformation method exploits the fact that the information models of the most relevant EHR specifications are available in the Web Ontology Language (OWL). The transformation approach is based on defining mappings between those ontological structures. We propose a way in which CEM entities can be transformed into openEHR by using transformation templates and OWL as common representation formalism. The transformation architecture exploits the reasoning and inferencing capabilities of OWL technologies. We have devised a generic, flexible approach for the transformation of clinical models, implemented for the unidirectional transformation from CEM to openEHR, a series of reusable transformation templates, a proof-of-concept implementation, and a set of openEHR archetypes that validate the methodological approach. We have been able to transform CEM into archetypes in an automatic, flexible, reusable transformation approach that could be extended to other clinical model specifications. We exploit the potential of OWL technologies for supporting the transformation process. We believe that our approach could be useful for international efforts in the area of semantic interoperability of EHR systems. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The function of the left anterior temporal pole: evidence from acute stroke and infarct volume

PubMed Central

Tsapkini, Kyrana; Frangakis, Constantine E.

2011-01-01

The role of the anterior temporal lobes in cognition and language has been much debated in the literature over the last few years. Most prevailing theories argue for an important role of the anterior temporal lobe as a semantic hub or a place for the representation of unique entities such as proper names of peoples and places. Lately, a few studies have investigated the role of the most anterior part of the left anterior temporal lobe, the left temporal pole in particular, and argued that the left anterior temporal pole is the area responsible for mapping meaning on to sound through evidence from tasks such as object naming. However, another recent study indicates that bilateral anterior temporal damage is required to cause a clinically significant semantic impairment. In the present study, we tested these hypotheses by evaluating patients with acute stroke before reorganization of structure–function relationships. We compared a group of 20 patients with acute stroke with anterior temporal pole damage to a group of 28 without anterior temporal pole damage matched for infarct volume. We calculated the average percent error in auditory comprehension and naming tasks as a function of infarct volume using a non-parametric regression method. We found that infarct volume was the only predictive variable in the production of semantic errors in both auditory comprehension and object naming tasks. This finding favours the hypothesis that left unilateral anterior temporal pole lesions, even acutely, are unlikely to cause significant deficits in mapping meaning to sound by themselves, although they contribute to networks underlying both naming and comprehension of objects. Therefore, the anterior temporal lobe may be a semantic hub for object meaning, but its role must be represented bilaterally and perhaps redundantly. PMID:21685458

Discovery of novel biomarkers and phenotypes by semantic technologies

PubMed Central

2013-01-01

Background Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents. Results This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated. Conclusions The reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions. PMID:23402646

The effect of concurrent semantic categorization on delayed serial recall.

PubMed

Acheson, Daniel J; MacDonald, Maryellen C; Postle, Bradley R

2011-01-01

The influence of semantic processing on the serial ordering of items in short-term memory was explored using a novel dual-task paradigm. Participants engaged in 2 picture-judgment tasks while simultaneously performing delayed serial recall. List material varied in the presence of phonological overlap (Experiments 1 and 2) and in semantic content (concrete words in Experiment 1 and 3; nonwords in Experiments 2 and 3). Picture judgments varied in the extent to which they required accessing visual semantic information (i.e., semantic categorization and line orientation judgments). Results showed that, relative to line-orientation judgments, engaging in semantic categorization judgments increased the proportion of item-ordering errors for concrete lists but did not affect error proportions for nonword lists. Furthermore, although more ordering errors were observed for phonologically similar relative to dissimilar lists, no interactions were observed between the phonological overlap and picture-judgment task manipulations. These results demonstrate that lexical-semantic representations can affect the serial ordering of items in short-term memory. Furthermore, the dual-task paradigm provides a new method for examining when and how semantic representations affect memory performance.

Atypical temporal activation pattern and central-right brain compensation during semantic judgment task in children with early left brain damage.

PubMed

Chang, Yi-Tzu; Lin, Shih-Che; Meng, Ling-Fu; Fan, Yang-Teng

In this study we investigated the event-related potentials (ERPs) during the semantic judgment task (deciding if the two Chinese characters were semantically related or unrelated) to identify the timing of neural activation in children with early left brain damage (ELBD). The results demonstrated that compared with the controls, children with ELBD had (1) competitive accuracy and reaction time in the semantic judgment task, (2) weak operation of the N400, (3) stronger, earlier and later compensational positivities (referred to the enhanced P200, P250, and P600 amplitudes) in the central and right region of the brain to successfully engage in semantic judgment. Our preliminary findings indicate that temporally postlesional reorganization is in accordance with the proposed right-hemispheric organization of speech after early left-sided brain lesion. During semantic processing, the orthography has a greater effect on the children with ELBD, and a later semantic reanalysis (P600) is required due to the less efficient N400 at the former stage for semantic integration. Copyright © 2018 Elsevier Inc. All rights reserved.

Influence of auditory spatial attention on cross-modal semantic priming effect: evidence from N400 effect.

PubMed

Wang, Hongyan; Zhang, Gaoyan; Liu, Baolin

2017-01-01

Semantic priming is an important research topic in the field of cognitive neuroscience. Previous studies have shown that the uni-modal semantic priming effect can be modulated by attention. However, the influence of attention on cross-modal semantic priming is unclear. To investigate this issue, the present study combined a cross-modal semantic priming paradigm with an auditory spatial attention paradigm, presenting the visual pictures as the prime stimuli and the semantically related or unrelated sounds as the target stimuli. Event-related potentials results showed that when the target sound was attended to, the N400 effect was evoked. The N400 effect was also observed when the target sound was not attended to, demonstrating that the cross-modal semantic priming effect persists even though the target stimulus is not focused on. Further analyses revealed that the N400 effect evoked by the unattended sound was significantly lower than the effect evoked by the attended sound. This contrast provides new evidence that the cross-modal semantic priming effect can be modulated by attention.

The Effect of Concurrent Semantic Categorization on Delayed Serial Recall

PubMed Central

Acheson, Daniel J.; MacDonald, Maryellen C.; Postle, Bradley R.

2010-01-01

The influence of semantic processing on the serial ordering of items in short-term memory was explored using a novel dual-task paradigm. Subjects engaged in two picture judgment tasks while simultaneously performing delayed serial recall. List material varied in the presence of phonological overlap (Experiments 1 and 2) and in semantic content (concrete words in Experiment 1 and 3; nonwords in Experiments 2 and 3). Picture judgments varied in the extent to which they required accessing visual semantic information (i.e., semantic categorization and line orientation judgments). Results showed that, relative to line orientation judgments, engaging in semantic categorization judgments increased the proportion of item ordering errors for concrete lists but did not affect error proportions for nonword lists. Furthermore, although more ordering errors were observed for phonologically similar relative to dissimilar lists, no interactions were observed between the phonological overlap and picture judgment task manipulations. These results thus demonstrate that lexical-semantic representations can affect the serial ordering of items in short-term memory. Furthermore, the dual-task paradigm provides a new method for examining when and how semantic representations affect memory performance. PMID:21058880

Effect of hearing loss on semantic access by auditory and audiovisual speech in children.

PubMed

Jerger, Susan; Tye-Murray, Nancy; Damian, Markus F; Abdi, Hervé

2013-01-01

This research studied whether the mode of input (auditory versus audiovisual) influenced semantic access by speech in children with sensorineural hearing impairment (HI). Participants, 31 children with HI and 62 children with normal hearing (NH), were tested with the authors' new multimodal picture word task. Children were instructed to name pictures displayed on a monitor and ignore auditory or audiovisual speech distractors. The semantic content of the distractors was varied to be related versus unrelated to the pictures (e.g., picture distractor of dog-bear versus dog-cheese, respectively). In children with NH, picture-naming times were slower in the presence of semantically related distractors. This slowing, called semantic interference, is attributed to the meaning-related picture-distractor entries competing for selection and control of the response (the lexical selection by competition hypothesis). Recently, a modification of the lexical selection by competition hypothesis, called the competition threshold (CT) hypothesis, proposed that (1) the competition between the picture-distractor entries is determined by a threshold, and (2) distractors with experimentally reduced fidelity cannot reach the CT. Thus, semantically related distractors with reduced fidelity do not produce the normal interference effect, but instead no effect or semantic facilitation (faster picture naming times for semantically related versus unrelated distractors). Facilitation occurs because the activation level of the semantically related distractor with reduced fidelity (1) is not sufficient to exceed the CT and produce interference but (2) is sufficient to activate its concept, which then strengthens the activation of the picture and facilitates naming. This research investigated whether the proposals of the CT hypothesis generalize to the auditory domain, to the natural degradation of speech due to HI, and to participants who are children. Our multimodal picture word task allowed us to (1) quantify picture naming results in the presence of auditory speech distractors and (2) probe whether the addition of visual speech enriched the fidelity of the auditory input sufficiently to influence results. In the HI group, the auditory distractors produced no effect or a facilitative effect, in agreement with proposals of the CT hypothesis. In contrast, the audiovisual distractors produced the normal semantic interference effect. Results in the HI versus NH groups differed significantly for the auditory mode, but not for the audiovisual mode. This research indicates that the lower fidelity auditory speech associated with HI affects the normalcy of semantic access by children. Further, adding visual speech enriches the lower fidelity auditory input sufficiently to produce the semantic interference effect typical of children with NH.

Acoustic and semantic interference effects in words and pictures.

PubMed

Dhawan, M; Pellegrino, J W

1977-05-01

Interference effects for pictures and words were investigated using a probe-recall task. Word stimuli showed acoustic interference effects for items at the end of the list and semantic interference effects for items at the beginning of the list, similar to results of Kintsch and Buschke (1969). Picture stimuli showed large semantic interference effects at all list positions with smaller acoustic interference effects. The results were related to latency data on picture-word processing and interpreted in terms of the differential order, probability, and/or speed of access to acoustic and semantic levels of processing. A levels of processing explanation of picture-word retention differences was related to dual coding theory. Both theoretical positions converge on an explanation of picture-word retention differences as a function of the relative capacity for semantic or associative processing.

When Sufficiently Processed, Semantically Related Distractor Pictures Hamper Picture Naming.

PubMed

Matushanskaya, Asya; Mädebach, Andreas; Müller, Matthias M; Jescheniak, Jörg D

2016-11-01

Prominent speech production models view lexical access as a competitive process. According to these models, a semantically related distractor picture should interfere with target picture naming more strongly than an unrelated one. However, several studies failed to obtain such an effect. Here, we demonstrate that semantic interference is obtained, when the distractor picture is sufficiently processed. Participants named one of two pictures presented in close temporal succession, with color cueing the target. Experiment 1 induced the prediction that the target appears first. When this prediction was violated (distractor first), semantic interference was observed. Experiment 2 ruled out that the time available for distractor processing was the driving force. These results show that semantically related distractor pictures interfere with the naming response when they are sufficiently processed. The data thus provide further support for models viewing lexical access as a competitive process.

When Wine and Apple Both Help the Production of Grapes: ERP Evidence for Post-lexical Semantic Facilitation in Picture Naming

PubMed Central

Python, Grégoire; Fargier, Raphaël; Laganaro, Marina

2018-01-01

Background: Producing a word in referential naming requires to select the right word in our mental lexicon among co-activated semantically related words. The mechanisms underlying semantic context effects during speech planning are still controversial, particularly for semantic facilitation which investigation remains under-represented in contrast to the plethora of studies dealing with interference. Our aim is to study the time-course of semantic facilitation in picture naming, using a picture-word “interference” paradigm and event-related potentials (ERPs). Methods: We compared two different types of semantic relationships, associative and categorical, in a single word priming and a double word priming paradigm. The primes were presented visually with a long negative Stimulus Onset Asynchrony (SOA), which is expected to cause facilitation. Results: Shorter naming latencies were observed after both associative and categorical primes, as compared to unrelated primes, and even shorter latencies after two primes. Electrophysiological results showed relatively late modulations of waveform amplitudes for both types of primes (beginning ~330 ms post picture onset with a single prime and ~275 ms post picture onset with two primes), corresponding to a shift in latency of similar topographic maps across conditions. Conclusion: The present results are in favor of a post-lexical locus of semantic facilitation for associative and categorical priming in picture naming and confirm that semantic facilitation is as relevant as semantic interference to inform on word production. The post-lexical locus argued here might be related to self-monitoting or/and to modulations at the level of word-form planning, without excluding the participation of strategic processes. PMID:29692716

Lexicality Effects in Word and Nonword Recall of Semantic Dementia and Progressive Nonfluent Aphasia

PubMed Central

Reilly, Jamie; Troche, Joshua; Chatel, Alison; Park, Hyejin; Kalinyak-Fliszar, Michelene; Antonucci, Sharon M.; Martin, Nadine

2012-01-01

Background Verbal working memory is an essential component of many language functions, including sentence comprehension and word learning. As such, working memory has emerged as a domain of intense research interest both in aphasiology and in the broader field of cognitive neuroscience. The integrity of verbal working memory encoding relies on a fluid interaction between semantic and phonological processes. That is, we encode verbal detail using many cues related to both the sound and meaning of words. Lesion models can provide an effective means of parsing the contributions of phonological or semantic impairment to recall performance. Methods and Procedures We employed the lesion model approach here by contrasting the nature of lexicality errors incurred during recall of word and nonword sequences by 3individuals with progressive nonfluent aphasia (a phonological dominant impairment) compared to that of 2 individuals with semantic dementia (a semantic dominant impairment). We focused on psycholinguistic attributes of correctly recalled stimuli relative to those that elicited a lexicality error (i.e., nonword → word OR word → nonword). Outcomes and results Patients with semantic dementia showed greater sensitivity to phonological attributes (e.g., phoneme length, wordlikeness) of the target items relative to semantic attributes (e.g., familiarity). Patients with PNFA showed the opposite pattern, marked by sensitivity to word frequency, age of acquisition, familiarity, and imageability. Conclusions We interpret these results in favor of a processing strategy such that in the context of a focal phonological impairment patients revert to an over-reliance on preserved semantic processing abilities. In contrast, a focal semantic impairment forces both reliance upon and hypersensitivity to phonological attributes of target words. We relate this interpretation to previous hypotheses about the nature of verbal short-term memory in progressive aphasia. PMID:23486736

Distinct loci of lexical and semantic access deficits in aphasia: Evidence from voxel-based lesion-symptom mapping and diffusion tensor imaging.

PubMed

Harvey, Denise Y; Schnur, Tatiana T

2015-06-01

Naming pictures and matching words to pictures belonging to the same semantic category negatively affects language production and comprehension. By most accounts, semantic interference arises when accessing lexical representations in naming (e.g., Damian, Vigliocco, & Levelt, 2001) and semantic representations in comprehension (e.g., Forde & Humphreys, 1997). Further, damage to the left inferior frontal gyrus (LIFG), a region implicated in cognitive control, results in increasing semantic interference when items repeat across cycles in both language production and comprehension (Jefferies, Baker, Doran, & Lambon Ralph, 2007). This generates the prediction that the LIFG via white matter connections supports resolution of semantic interference arising from different loci (lexical vs semantic) in the temporal lobe. However, it remains unclear whether the cognitive and neural mechanisms that resolve semantic interference are the same across tasks. Thus, we examined which gray matter structures [using whole brain and region of interest (ROI) approaches] and white matter connections (using deterministic tractography) when damaged impact semantic interference and its increase across cycles when repeatedly producing and understanding words in 15 speakers with varying lexical-semantic deficits from left hemisphere stroke. We found that damage to distinct brain regions, the posterior versus anterior temporal lobe, was associated with semantic interference (collapsed across cycles) in naming and comprehension, respectively. Further, those with LIFG damage compared to those without exhibited marginally larger increases in semantic interference across cycles in naming but not comprehension. Lastly, the inferior fronto-occipital fasciculus, connecting the LIFG with posterior temporal lobe, related to semantic interference in naming, whereas the inferior longitudinal fasciculus (ILF), connecting posterior with anterior temporal regions related to semantic interference in comprehension. These neuroanatomical-behavioral findings have implications for models of the lexical-semantic language network by demonstrating that semantic interference in language production and comprehension involves different representations which differentially recruit a cognitive control mechanism for interference resolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exploring semantic and phonological picture-word priming in adults who stutter using event-related potentials

PubMed Central

Maxfield, Nathan D.; Pizon-Moore, Angela A.; Frisch, Stefan A.; Constantine, Joseph L.

2011-01-01

Objective Our aim was to investigate how semantic and phonological information is processed in adults who stutter (AWS) preparing to name pictures, following-up a report that event-related potentials (ERPs) in AWS evidenced atypical semantic picture-word priming (Maxfield et al., 2010). Methods Fourteen AWS and 14 typically-fluent adults (TFA) participated. Pictures, named at a delay, were followed by probe words. Design elements not used in Maxfield et al. (2010) let us evaluate both phonological and semantic picture-word priming. Results TFA evidenced typical priming effects in probe-elicited ERPs. AWS evidenced diminished Semantic priming, and reverse Phonological N400 priming. Conclusions Results point to atypical processing of semantic and phonological information in AWS. Discussion considers whether AWS ERP effects reflect unstable activation of target label semantic and phonological representations, strategic inhibition of target label phonological neighbors, and/or phonological label-probe competition. Significance Results raise questions about how mechanisms that regulate activation spreading operate in AWS. PMID:22055837

Rewriting Logic Semantics of a Plan Execution Language

NASA Technical Reports Server (NTRS)

Dowek, Gilles; Munoz, Cesar A.; Rocha, Camilo

2009-01-01

The Plan Execution Interchange Language (PLEXIL) is a synchronous language developed by NASA to support autonomous spacecraft operations. In this paper, we propose a rewriting logic semantics of PLEXIL in Maude, a high-performance logical engine. The rewriting logic semantics is by itself a formal interpreter of the language and can be used as a semantic benchmark for the implementation of PLEXIL executives. The implementation in Maude has the additional benefit of making available to PLEXIL designers and developers all the formal analysis and verification tools provided by Maude. The formalization of the PLEXIL semantics in rewriting logic poses an interesting challenge due to the synchronous nature of the language and the prioritized rules defining its semantics. To overcome this difficulty, we propose a general procedure for simulating synchronous set relations in rewriting logic that is sound and, for deterministic relations, complete. We also report on the finding of two issues at the design level of the original PLEXIL semantics that were identified with the help of the executable specification in Maude.

Ontology-based systematic representation and analysis of traditional Chinese drugs against rheumatism.

PubMed

Liu, Qingping; Wang, Jiahao; Zhu, Yan; He, Yongqun

2017-12-21

Rheumatism represents any disease condition marked with inflammation and pain in the joints, muscles, or connective tissues. Many traditional Chinese drugs have been used for a long time to treat rheumatism. However, a comprehensive information source for these drugs is still missing, and their anti-rheumatism mechanisms remain unclear. An ontology for anti-rheumatism traditional Chinese drugs would strongly support the representation, analysis, and understanding of these drugs. In this study, we first systematically collected reported information about 26 traditional Chinese decoction pieces drugs, including their chemical ingredients and adverse events (AEs). By mostly reusing terms from existing ontologies (e.g., TCMDPO for traditional Chinese medicines, NCBITaxon for taxonomy, ChEBI for chemical elements, and OAE for adverse events) and making semantic axioms linking different entities, we developed the Ontology of Chinese Medicine for Rheumatism (OCMR) that includes over 3000 class terms. Our OCMR analysis found that these 26 traditional Chinese decoction pieces are made from anatomic entities (e.g., root and stem) from 3 Bilateria animals and 23 Mesangiospermae plants. Anti-inflammatory and antineoplastic roles are important for anti-rheumatism drugs. Using the total of 555 unique ChEBI chemical entities identified from these drugs, our ChEBI-based classification analysis identified 18 anti-inflammatory, 33 antineoplastic chemicals, and 9 chemicals (including 3 diterpenoids and 3 triterpenoids) having both anti-inflammatory and antineoplastic roles. Furthermore, our study detected 22 diterpenoids and 23 triterpenoids, including 16 pentacyclic triterpenoids that are likely bioactive against rheumatism. Six drugs were found to be associated with 184 unique AEs, including three AEs (i.e., dizziness, nausea and vomiting, and anorexia) each associated with 5 drugs. Several chemical entities are classified as neurotoxins (e.g., diethyl phthalate) and allergens (e.g., eugenol), which may explain the formation of some TCD AEs. The OCMR could be efficiently queried for useful information using SPARQL scripts. The OCMR ontology was developed to systematically represent 26 traditional anti-rheumatism Chinese drugs and their related information. The OCMR analysis identified possible anti-rheumatism and AE mechanisms of these drugs. Our novel ontology-based approach can also be applied to systematic representation and analysis of other traditional Chinese drugs.

Type-specific proactive interference in patients with semantic and phonological STM deficits.

PubMed

Harris, Lara; Olson, Andrew; Humphreys, Glyn

2014-01-01

Prior neuropsychological evidence suggests that semantic and phonological components of short-term memory (STM) are functionally and neurologically distinct. The current paper examines proactive interference (PI) from semantic and phonological information in two STM-impaired patients, DS (semantic STM deficit) and AK (phonological STM deficit). In Experiment 1 probe recognition tasks with open and closed sets of stimuli were used. Phonological PI was assessed using nonword items, and semantic and phonological PI was assessed using words. In Experiment 2 phonological and semantic PI was elicited by an item recognition probe test with stimuli that bore phonological and semantic relations to the probes. The data suggested heightened phonological PI for the semantic STM patient, and exaggerated effects of semantic PI in the phonological STM case. The findings are consistent with an account of extremely rapid decay of activated type-specific representations in cases of severely impaired phonological and semantic STM.

Project Integration Architecture

NASA Technical Reports Server (NTRS)

Jones, William Henry

2008-01-01

The Project Integration Architecture (PIA) is a distributed, object-oriented, conceptual, software framework for the generation, organization, publication, integration, and consumption of all information involved in any complex technological process in a manner that is intelligible to both computers and humans. In the development of PIA, it was recognized that in order to provide a single computational environment in which all information associated with any given complex technological process could be viewed, reviewed, manipulated, and shared, it is necessary to formulate all the elements of such a process on the most fundamental level. In this formulation, any such element is regarded as being composed of any or all of three parts: input information, some transformation of that input information, and some useful output information. Another fundamental principle of PIA is the assumption that no consumer of information, whether human or computer, can be assumed to have any useful foreknowledge of an element presented to it. Consequently, a PIA-compliant computing system is required to be ready to respond to any questions, posed by the consumer, concerning the nature of the proffered element. In colloquial terms, a PIA-compliant system must be prepared to provide all the information needed to place the element in context. To satisfy this requirement, PIA extends the previously established object-oriented- programming concept of self-revelation and applies it on a grand scale. To enable pervasive use of self-revelation, PIA exploits another previously established object-oriented-programming concept - that of semantic infusion through class derivation. By means of self-revelation and semantic infusion through class derivation, a consumer of information can inquire about the contents of all information entities (e.g., databases and software) and can interact appropriately with those entities. Other key features of PIA are listed.

Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands

PubMed Central

Arroyo Ohori, Ken; Ledoux, Hugo; Peters, Ravi; Stoter, Jantien

2016-01-01

The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate to what extent they can be used for the same purpose. Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach. PMID:27254151

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words "lick" or "pick") and between affirmative and negated sentence meanings. Copyright © 2017 Grisoni et al.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words “lick” or “pick”) and between affirmative and negated sentence meanings. PMID:28411271

NASA Taxonomies for Searching Problem Reports and FMEAs

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Throop, David R.

2006-01-01

Many types of hazard and risk analyses are used during the life cycle of complex systems, including Failure Modes and Effects Analysis (FMEA), Hazard Analysis, Fault Tree and Event Tree Analysis, Probabilistic Risk Assessment, Reliability Analysis and analysis of Problem Reporting and Corrective Action (PRACA) databases. The success of these methods depends on the availability of input data and the analysts knowledge. Standard nomenclature can increase the reusability of hazard, risk and problem data. When nomenclature in the source texts is not standard, taxonomies with mapping words (sets of rough synonyms) can be combined with semantic search to identify items and tag them with metadata based on a rich standard nomenclature. Semantic search uses word meanings in the context of parsed phrases to find matches. The NASA taxonomies provide the word meanings. Spacecraft taxonomies and ontologies (generalization hierarchies with attributes and relationships, based on terms meanings) are being developed for types of subsystems, functions, entities, hazards and failures. The ontologies are broad and general, covering hardware, software and human systems. Semantic search of Space Station texts was used to validate and extend the taxonomies. The taxonomies have also been used to extract system connectivity (interaction) models and functions from requirements text. Now the Reconciler semantic search tool and the taxonomies are being applied to improve search in the Space Shuttle PRACA database, to discover recurring patterns of failure. Usual methods of string search and keyword search fall short because the entries are terse and have numerous shortcuts (irregular abbreviations, nonstandard acronyms, cryptic codes) and modifier words cannot be used in sentence context to refine the search. The limited and fixed FMEA categories associated with the entries do not make the fine distinctions needed in the search. The approach assigns PRACA report titles to problem classes in the taxonomy. Each ontology class includes mapping words - near-synonyms naming different manifestations of that problem class. The mapping words for Problems, Entities and Functions are converted to a canonical form plus any of a small set of modifier words (e.g. non-uniformity NOT + UNIFORM.) The report titles are parsed as sentences if possible, or treated as a flat sequence of word tokens if parsing fails. When canonical forms in the title match mapping words, the PRACA entry is associated with the corresponding Problem, Entity or Function in the ontology. The user can search for types of failures associated with types of equipment, clustering by type of problem (e.g., all bearings found with problems of being uneven: rough, irregular, gritty ). The results could also be used for tagging PRACA report entries with rich metadata. This approach could also be applied to searching and tagging failure modes, failure effects and mitigations in FMEAs. In the pilot work, parsing 52K+ truncated titles (the test cases that were available), has resulted in identification of both a type of equipment and type of problem in about 75% of the cases. The results are displayed in a manner analogous to Google search results. The effort has also led to the enrichment of the taxonomy, adding some new categories and many new mapping words. Further work would make enhancements that have been identified for improving the clustering and further reducing the false alarm rate. (In searching for recurring problems, good clustering is more important than reducing false alarms). Searching complete PRACA reports should lead to immediate improvement.

Semantic Relatedness for Evaluation of Course Equivalencies

ERIC Educational Resources Information Center

Yang, Beibei

2012-01-01

Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the…

Is Semantic Priming (Ir)rational? Insights from the Speeded Word Fragment Completion Task

ERIC Educational Resources Information Center

Heyman, Tom; Hutchison, Keith A.; Storms, Gert

2016-01-01

Semantic priming, the phenomenon that a target is recognized faster if it is preceded by a semantically related prime, is a well-established effect. However, the mechanisms producing semantic priming are subject of debate. Several theories assume that the underlying processes are controllable and tuned to prime utility. In contrast, purely…

Multiple Meanings Are Not Necessarily a Disadvantage in Semantic Processing: Evidence from Homophone Effects in Semantic Categorisation

ERIC Educational Resources Information Center

Siakaluk, Paul D.; Pexman, Penny M.; Sears, Christopher R.; Owen, William J.

2007-01-01

The ambiguity disadvantage (slower processing of ambiguous words relative to unambiguous words) has been taken as evidence for a distributed semantic representational system like that embodied in parallel distributed processing (PDP) models. In the present study, we investigated whether semantic ambiguity slows meaning activation, as PDP models…

Preview Fixation Duration Modulates Identical and Semantic Preview Benefit in Chinese Reading

ERIC Educational Resources Information Center

Yan, Ming; Risse, Sarah; Zhou, Xiaolin; Kliegl, Reinhold

2012-01-01

Semantic preview benefit from parafoveal words is critical for proposals of distributed lexical processing during reading. Semantic preview benefit has been demonstrated for Chinese reading with the boundary paradigm in which unrelated or semantically related previews of a target word "N" + 1 are replaced by the target word once the eyes cross an…

Relatedness Proportion Effects in Semantic Categorization: Reconsidering the Automatic Spreading Activation Process

ERIC Educational Resources Information Center

de Wit, Bianca; Kinoshita, Sachiko

2014-01-01

Semantic priming effects at a short prime-target stimulus onset asynchrony are commonly explained in terms of an automatic spreading activation process. According to this view, the proportion of related trials should have no impact on the size of the semantic priming effect. Using a semantic categorization task ("Is this a living…

Lexical-Semantic Processing and Reading: Relations between Semantic Priming, Visual Word Recognition and Reading Comprehension

ERIC Educational Resources Information Center

Nobre, Alexandre de Pontes; de Salles, Jerusa Fumagalli

2016-01-01

The aim of this study was to investigate relations between lexical-semantic processing and two components of reading: visual word recognition and reading comprehension. Sixty-eight children from private schools in Porto Alegre, Brazil, from 7 to 12 years, were evaluated. Reading was assessed with a word/nonword reading task and a reading…

Body-part-specific representations of semantic noun categories.

PubMed

Carota, Francesca; Moseley, Rachel; Pulvermüller, Friedemann

2012-06-01

Word meaning processing in the brain involves ventrolateral temporal cortex, but a semantic contribution of the dorsal stream, especially frontocentral sensorimotor areas, has been controversial. We here examine brain activation during passive reading of object-related nouns from different semantic categories, notably animal, food, and tool words, matched for a range of psycholinguistic features. Results show ventral stream activation in temporal cortex along with category-specific activation patterns in both ventral and dorsal streams, including sensorimotor systems and adjacent pFC. Precentral activation reflected action-related semantic features of the word categories. Cortical regions implicated in mouth and face movements were sparked by food words, and hand area activation was seen for tool words, consistent with the actions implicated by the objects the words are used to speak about. Furthermore, tool words specifically activated the right cerebellum, and food words activated the left orbito-frontal and fusiform areas. We discuss our results in the context of category-specific semantic deficits in the processing of words and concepts, along with previous neuroimaging research, and conclude that specific dorsal and ventral areas in frontocentral and temporal cortex index visual and affective-emotional semantic attributes of object-related nouns and action-related affordances of their referent objects.

Explicit semantic tasks are necessary to study semantic priming effects with high rates of repetition.

PubMed

Renoult, Louis; Wang, Xiaoxiao; Mortimer, Jennifer; Debruille, J Bruno

2012-04-01

The purpose of the present study was to clarify in which experimental conditions the semantic processing of repeated words is preserved. We contrasted a short (250 ms) and a long (1000 ms) stimulus onset asynchrony (SOA) in two different experiments, using a relatively low proportion of related words (30%). One group of participants performed a lexical decision task (LDT) and a second group performed an explicit semantic matching task with the same words (except for pseudowords) and the same task parameters. In both tasks, word stimuli consisted solely of two prime and two target words repeated throughout the experiment. The effects of semantic priming on reaction time (RT) and the amplitude of the N400 ERP were absent for both the short and the long SOA in the LDT. In contrast, in the explicit semantic task, these effects were significant. In this task, the activity of N400 generators in the left superior temporal gyrus and the inferior parietal cortex significantly differentiated primed and unprimed trials but this effect did not interact with SOA. Our results indicate that task instruction is critical to preserve semantic processing with repeated presentations. Using explicit semantic designs, it may be possible to study associative or categorical relations between individual concepts. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Semantic knowledge fractionations: verbal propositions vs. perceptual input? Evidence from a child with Klinefelter syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2013-04-01

This paper addresses the relative independence of different types of lexical- and factually-based semantic knowledge in JM, a 9-year-old boy with Klinefelter syndrome (KS). JM was matched to typically developing (TD) controls on the basis of chronological age. Lexical-semantic knowledge was investigated for common noun (CN) and mathematical vocabulary items (MV). Factually-based semantic knowledge was investigated for general and number facts. For CN items, JM's lexical stores were of a normal size but the volume of correct 'sensory feature' semantic knowledge he generated within verbal item descriptions was significantly reduced. He was also significantly impaired at naming item descriptions and pictures, particularly for fruit and vegetables. There was also weak object decision for fruit and vegetables. In contrast, for MV items, JM's lexical stores were elevated, with no significant difference in the amount and type of correct semantic knowledge generated within verbal item descriptions and normal naming. JM's fact retrieval accuracy was normal for all types of factual knowledge. JM's performance indicated a dissociation between the representation of CN and MV vocabulary items during development. JM's preserved semantic knowledge of facts in the face of impaired semantic knowledge of vocabulary also suggests that factually-based semantic knowledge representation is not dependent on normal lexical-semantic knowledge during development. These findings are discussed in relation to the emergence of distinct semantic knowledge representations during development, due to differing degrees of dependency upon the acquisition and representation of semantic knowledge from verbal propositions and perceptual input.

Memory as discrimination: what distraction reveals.

PubMed

Beaman, C Philip; Hanczakowski, Maciej; Hodgetts, Helen M; Marsh, John E; Jones, Dylan M

2013-11-01

Recalling information involves the process of discriminating between relevant and irrelevant information stored in memory. Not infrequently, the relevant information needs to be selected from among a series of related possibilities. This is likely to be particularly problematic when the irrelevant possibilities not only are temporally or contextually appropriate, but also overlap semantically with the target or targets. Here, we investigate the extent to which purely perceptual features that discriminate between irrelevant and target material can be used to overcome the negative impact of contextual and semantic relatedness. Adopting a distraction paradigm, it is demonstrated that when distractors are interleaved with targets presented either visually (Experiment 1) or auditorily (Experiment 2), a within-modality semantic distraction effect occurs; semantically related distractors impact upon recall more than do unrelated distractors. In the semantically related condition, the number of intrusions in recall is reduced, while the number of correctly recalled targets is simultaneously increased by the presence of perceptual cues to relevance (color features in Experiment 1 or speaker's gender in Experiment 2). However, as is demonstrated in Experiment 3, even presenting semantically related distractors in a language and a sensory modality (spoken Welsh) distinct from that of the targets (visual English) is insufficient to eliminate false recalls completely or to restore correct recall to levels seen with unrelated distractors . Together, the study shows how semantic and nonsemantic discriminability shape patterns of both erroneous and correct recall.

Facilitation and interference in naming: A consequence of the same learning process?

PubMed

Hughes, Julie W; Schnur, Tatiana T

2017-08-01

Our success with naming depends on what we have named previously, a phenomenon thought to reflect learning processes. Repeatedly producing the same name facilitates language production (i.e., repetition priming), whereas producing semantically related names hinders subsequent performance (i.e., semantic interference). Semantic interference is found whether naming categorically related items once (continuous naming) or multiple times (blocked cyclic naming). A computational model suggests that the same learning mechanism responsible for facilitation in repetition creates semantic interference in categorical naming (Oppenheim, Dell, & Schwartz, 2010). Accordingly, we tested the predictions that variability in semantic interference is correlated across categorical naming tasks and is caused by learning, as measured by two repetition priming tasks (picture-picture repetition priming, Exp. 1; definition-picture repetition priming, Exp. 2, e.g., Wheeldon & Monsell, 1992). In Experiment 1 (77 subjects) semantic interference and repetition priming effects were robust, but the results revealed no relationship between semantic interference effects across contexts. Critically, learning (picture-picture repetition priming) did not predict semantic interference effects in either task. We replicated these results in Experiment 2 (81 subjects), finding no relationship between semantic interference effects across tasks or between semantic interference effects and learning (definition-picture repetition priming). We conclude that the changes underlying facilitatory and interfering effects inherent to lexical access are the result of distinct learning processes where multiple mechanisms contribute to semantic interference in naming. Copyright © 2017 Elsevier B.V. All rights reserved.

Recovering time-varying networks of dependencies in social and biological studies.

PubMed

Ahmed, Amr; Xing, Eric P

2009-07-21

A plausible representation of the relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network that is topologically rewiring and semantically evolving over time. Although there is a rich literature in modeling static or temporally invariant networks, little has been done toward recovering the network structure when the networks are not observable in a dynamic context. In this article, we present a machine learning method called TESLA, which builds on a temporally smoothed l(1)-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently by using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks and on reverse engineering the latent sequence of temporally rewiring political and academic social networks from longitudinal data, and the evolving gene networks over >4,000 genes during the life cycle of Drosophila melanogaster from a microarray time course at a resolution limited only by sample frequency.

Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

NASA Astrophysics Data System (ADS)

Satyasree, K. P. N. V., Dr; Lalitha Kumari, B., Dr; Jyotsna Devi, K. S. N. V.; Choudri, S. M. Roy; Pratap Joshi, K.

2017-08-01

Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria - Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

Extraction of CYP chemical interactions from biomedical literature using natural language processing methods.

PubMed

Jiao, Dazhi; Wild, David J

2009-02-01

This paper proposes a system that automatically extracts CYP protein and chemical interactions from journal article abstracts, using natural language processing (NLP) and text mining methods. In our system, we employ a maximum entropy based learning method, using results from syntactic, semantic, and lexical analysis of texts. We first present our system architecture and then discuss the data set for training our machine learning based models and the methods in building components in our system, such as part of speech (POS) tagging, Named Entity Recognition (NER), dependency parsing, and relation extraction. An evaluation of the system is conducted at the end, yielding very promising results: The POS, dependency parsing, and NER components in our system have achieved a very high level of accuracy as measured by precision, ranging from 85.9% to 98.5%, and the precision and the recall of the interaction extraction component are 76.0% and 82.6%, and for the overall system are 68.4% and 72.2%, respectively.

Can Computers be Social?

NASA Astrophysics Data System (ADS)

Ekdahl, Bertil

2002-09-01

Of main concern in agent based computing is the conception that software agents can attain socially responsible behavior. This idea has its origin in the need for agents to interact with one another in a cooperating manner. Such interplay between several agents can be seen as a combinatorial play where the rules are fixed and the actors are supposed to closely analyze the play in order to behave rational. This kind of rationality has successfully being mathematically described. When the social behavior is extended beyond rational behavior, mere mathematical analysis falls short. For such behavior language is decisive for transferring concepts and language is a holistic entity that cannot be analyzed and defined mathematically. Accordingly, computers cannot be furnished with a language in the sense that meaning can be conveyed and consequently they lack all the necessary properties to be made social. The attempts to postulate mental properties to computer programs are a misconception that is blamed the lack of true understanding of language and especially the relation between formal system and its semantics.

Relative category-specific preservation in semantic dementia? Evidence from 35 cases.

PubMed

Merck, Catherine; Jonin, Pierre-Yves; Vichard, Hélène; Boursiquot, Sandrine Le Moal; Leblay, Virginie; Belliard, Serge

2013-03-01

Category-specific deficits have rarely been reported in semantic dementia (SD). To our knowledge, only four previous studies have documented category-specific deficits, and these have focused on the living versus non-living things contrast rather than on more fine-grained semantic categories. This study aimed to determine whether a category-specific effect could be highlighted by a semantic sorting task administered to 35 SD patients once at baseline and again after 2 years and to 10 Alzheimer's disease patients (AD). We found a relative preservation of fruit and vegetables only in SD. This relative preservation of fruit and vegetables could be considered with regard to the importance of color knowledge in their discrimination. Indeed, color knowledge retrieval is known to depend on the left posterior fusiform gyrus which is relatively spared in SD. Finally, according to predictions of semantic memory models, our findings best fitted the Devlin and Gonnerman's computational account. Copyright © 2013 Elsevier Inc. All rights reserved.

Age-Related Brain Activation Changes during Rule Repetition in Word-Matching.

PubMed

Methqal, Ikram; Pinsard, Basile; Amiri, Mahnoush; Wilson, Maximiliano A; Monchi, Oury; Provost, Jean-Sebastien; Joanette, Yves

2017-01-01

Objective: The purpose of this study was to explore the age-related brain activation changes during a word-matching semantic-category-based task, which required either repeating or changing a semantic rule to be applied. In order to do so, a word-semantic rule-based task was adapted from the Wisconsin Sorting Card Test, involving the repeated feedback-driven selection of given pairs of words based on semantic category-based criteria. Method: Forty healthy adults (20 younger and 20 older) performed a word-matching task while undergoing a fMRI scan in which they were required to pair a target word with another word from a group of three words. The required pairing is based on three word-pair semantic rules which correspond to different levels of semantic control demands: functional relatedness, moderately typical-relatedness (which were considered as low control demands), and atypical-relatedness (high control demands). The sorting period consisted of a continuous execution of the same sorting rule and an inferred trial-by-trial feedback was given. Results: Behavioral performance revealed increases in response times and decreases of correct responses according to the level of semantic control demands (functional vs. typical vs. atypical) for both age groups (younger and older) reflecting graded differences in the repetition of the application of a given semantic rule. Neuroimaging findings of significant brain activation showed two main results: (1) Greater task-related activation changes for the repetition of the application of atypical rules relative to typical and functional rules, and (2) Changes (older > younger) in the inferior prefrontal regions for functional rules and more extensive and bilateral activations for typical and atypical rules. Regarding the inter-semantic rules comparison, only task-related activation differences were observed for functional > typical (e.g., inferior parietal and temporal regions bilaterally) and atypical > typical (e.g., prefrontal, inferior parietal, posterior temporal, and subcortical regions). Conclusion: These results suggest that healthy cognitive aging relies on the adaptive changes of inferior prefrontal resources involved in the repetitive execution of semantic rules, thus reflecting graded differences in support of task demands.

Retrieval and Monitoring Processes during Visual Working Memory: An ERP Study of the Benefit of Visual Semantics

PubMed Central

Orme, Elizabeth; Brown, Louise A.; Riby, Leigh M.

2017-01-01

In this study, we examined electrophysiological indices of episodic remembering whilst participants recalled novel shapes, with and without semantic content, within a visual working memory paradigm. The components of interest were the parietal episodic (PE; 400–800 ms) and late posterior negativity (LPN; 500–900 ms), as these have previously been identified as reliable markers of recollection and post-retrieval monitoring, respectively. Fifteen young adults completed a visual matrix patterns task, assessing memory for low and high semantic visual representations. Matrices with either low semantic or high semantic content (containing familiar visual forms) were briefly presented to participants for study (1500 ms), followed by a retention interval (6000 ms) and finally a same/different recognition phase. The event-related potentials of interest were tracked from the onset of the recognition test stimuli. Analyses revealed equivalent amplitude for the earlier PE effect for the processing of both low and high semantic stimulus types. However, the LPN was more negative-going for the processing of the low semantic stimuli. These data are discussed in terms of relatively ‘pure’ and complete retrieval of high semantic items, where support can readily be recruited from semantic memory. However, for the low semantic items additional executive resources, as indexed by the LPN, are recruited when memory monitoring and uncertainty exist in order to recall previously studied items more effectively. PMID:28725203

Retrieval and Monitoring Processes during Visual Working Memory: An ERP Study of the Benefit of Visual Semantics.

PubMed

Orme, Elizabeth; Brown, Louise A; Riby, Leigh M

2017-01-01

In this study, we examined electrophysiological indices of episodic remembering whilst participants recalled novel shapes, with and without semantic content, within a visual working memory paradigm. The components of interest were the parietal episodic (PE; 400-800 ms) and late posterior negativity (LPN; 500-900 ms), as these have previously been identified as reliable markers of recollection and post-retrieval monitoring, respectively. Fifteen young adults completed a visual matrix patterns task, assessing memory for low and high semantic visual representations. Matrices with either low semantic or high semantic content (containing familiar visual forms) were briefly presented to participants for study (1500 ms), followed by a retention interval (6000 ms) and finally a same/different recognition phase. The event-related potentials of interest were tracked from the onset of the recognition test stimuli. Analyses revealed equivalent amplitude for the earlier PE effect for the processing of both low and high semantic stimulus types. However, the LPN was more negative-going for the processing of the low semantic stimuli. These data are discussed in terms of relatively 'pure' and complete retrieval of high semantic items, where support can readily be recruited from semantic memory. However, for the low semantic items additional executive resources, as indexed by the LPN, are recruited when memory monitoring and uncertainty exist in order to recall previously studied items more effectively.

Representation and Management of the Knowledge of Brittle Deformation in Shear Zones Using Microstructural Data From the SAFOD Core Samples

NASA Astrophysics Data System (ADS)

Babaie, H. A.; Broda, C. M.; Kumar, A.; Hadizadeh, J.

2010-12-01

Web access to data that represent knowledge acquired by investigators studying the microstructures in the core samples of the SAFOD (San Andreas Observatory at Depth) project can help scientists efficiently integrate and share knowledge, query the data, and update the knowledge base on the Web. To achieve this, we have used OWL (Web Ontology Language) to build the brittle deformation ontology for the microstructures observed in the SAFOD core samples, by explicitly formalizing the knowledge about deformational processes, geological objects undergoing deformation, and the underlying mechanical and environmental conditions in brittle shear zones. The developed Web-based ‘SAFOD Brittle Microstructure and Mechanics Knowledge base’ (SAFOD BM2KB), which instantiates this ontology and is available at http://codd.cs.gsu.edu:9999/safod/index.jsp, will host and serve data that pertains to spatial objects, such as microstructure, gouge, fault, and SEM image, acquired by the SAFOD investigators through the studies of the SAFOD core samples. Deformation in shear zones involves complex brittle and ductile processes that alter, create, and/or destroy a wide variety of one- to three-dimensional, multi-scale spatial entities such as rocks and their constituent minerals and structure. These processes occur through a series of sub-processes that happen in different time intervals, and affect the spatial objects at granular to regional scales within shear zones. The processes bring about qualitative change to the spatial entities over time intervals that start and end with events. Processes, such as mylonitization and cataclastic flow, change the spatial location, distribution, dimension, size, shape, and orientation of some objects through translation, rotation and strain. These processes may also result in newly formed entities, such as a new mineral, gouge, vein, or fault, during one or more phases of deformation. Deformation processes may also destroy entities, such as a mineral, fossil, or original structure. Laboratory investigations by the SAFOD scientists result in ever-increasing volumes of complex data related to different tectonic processes, deformed rocks, and structures. These data are often published in the tables of scientific articles or are stored in personal Excel worksheets or, in rare cases, in a network community database. It is extremely hard to integrate autonomously built databases distributed on the Web because of their heterogeneous schemas. As a closed world model, databases can only store and serve a finite set of static data that are known to be true. They cannot represent knowledge in a constantly changing, open world. In contrast, integration of scientific data and presentation of their underlying knowledge can be achieved through the use of Semantic Web technologies. These technologies are capable of handling an infinite supply of known and yet to be known facts due to their open world model. The inference rules of OWL and its underlying RDFS and RDF semantic languages allow formal and explicit specification of the theories and knowledge of a particular domain such as brittle deformation in shear zone.

Effect of semantic coherence on episodic memory processes in schizophrenia.

PubMed

Battal Merlet, Lâle; Morel, Shasha; Blanchet, Alain; Lockman, Hazlin; Kostova, Milena

2014-12-30

Schizophrenia is associated with severe episodic retrieval impairment. The aim of this study was to investigate the possibility that schizophrenia patients could improve their familiarity and/or recollection processes by manipulating the semantic coherence of to-be-learned stimuli and using deep encoding. Twelve schizophrenia patients and 12 healthy controls of comparable age, gender, and educational level undertook an associative recognition memory task. The stimuli consisted of pairs of words that were either related or unrelated to a given semantic category. The process dissociation procedure was used to calculate the estimates of familiarity and recollection processes. Both groups showed enhanced memory performances for semantically related words. However, in healthy controls, semantic relatedness led to enhanced recollection, while in schizophrenia patients, it induced enhanced familiarity. The familiarity estimates for related words were comparable in both groups, indicating that familiarity could be used as a compensatory mechanism in schizophrenia patients. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Measuring content overlap during handoff communication using distributional semantics: An exploratory study.

PubMed

Abraham, Joanna; Kannampallil, Thomas G; Srinivasan, Vignesh; Galanter, William L; Tagney, Gail; Cohen, Trevor

2017-01-01

We develop and evaluate a methodological approach to measure the degree and nature of overlap in handoff communication content within and across clinical professions. This extensible, exploratory approach relies on combining techniques from conversational analysis and distributional semantics. We audio-recorded handoff communication of residents and nurses on the General Medicine floor of a large academic hospital (n=120 resident and n=120 nurse handoffs). We measured semantic similarity, a proxy for content overlap, between resident-resident and nurse-nurse communication using multiple steps: a qualitative conversational content analysis; an automated semantic similarity analysis using Reflective Random Indexing (RRI); and comparing semantic similarity generated by RRI analysis with human ratings of semantic similarity. There was significant association between the semantic similarity as computed by the RRI method and human rating (ρ=0.88). Based on the semantic similarity scores, content overlap was relatively higher for content related to patient active problems, assessment of active problems, patient-identifying information, past medical history, and medications/treatments. In contrast, content overlap was limited on content related to allergies, family-related information, code status, and anticipatory guidance. Our approach using RRI analysis provides new opportunities for characterizing the nature and degree of overlap in handoff communication. Although exploratory, this method provides a basis for identifying content that can be used for determining shared understanding across clinical professions. Additionally, this approach can inform the development of flexibly standardized handoff tools that reflect clinical content that are most appropriate for fostering shared understanding during transitions of care. Copyright © 2016 Elsevier Inc. All rights reserved.

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

PubMed Central

Cimino, J J; Clayton, P D; Hripcsak, G; Johnson, S B

1994-01-01

OBJECTIVE: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. DESIGN: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. RESULTS: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. CONCLUSION: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification. PMID:7719786

Neural foundations and functional specificity of number representations.

PubMed

Piazza, Manuela; Eger, Evelyn

2016-03-01

Number is a complex category, as with the word "number" we may refer to different entities. First, it is a perceptual property that characterizes any set of individual items, namely its cardinality. The ability to extract the (approximate) cardinality of sets is almost universal in the animal domain and present in humans since birth. In primates, posterior parietal cortex seems to be a crucial site for this ability, even if the degree of selectivity of numerical representations in parietal cortex reported to date appears much lower compared to that of other semantic categories in the ventral stream. Number can also be intended as a mathematical object, which we humans use to count, measure, and order: a (verbal or visual) symbol that stands for the cardinality of a set, the intensity of a continuous quantity or the position of an item on a list. Evidence points to a convergence towards parietal cortex for the semantic coding of numerical symbols and to the bilateral occipitotemporal cortex for the shape coding of Arabic digits and other number symbols. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tissue Engineering and Regenerative Medicine: Semantic Considerations for an Evolving Paradigm

PubMed Central

Katari, Ravi; Peloso, Andrea; Orlando, Giuseppe

2015-01-01

Tissue engineering (TE) and regenerative medicine (RM) are rapidly evolving fields that are often obscured by a dense cloud of hype and commercialization potential. We find, in the literature and general commentary, that several of the associated terms are casually referenced in varying contexts that ultimately result in the blurring of the distinguishing boundaries which define them. “TE” and “RM” are often used interchangeably, though some experts vehemently argue that they, in fact, represent different conceptual entities. Nevertheless, contemporary scientists have a general idea of the experiments and milestones that can be classified within either or both categories. Given the groundbreaking achievements reported within the past decade and consequent watershed potential of this field, we feel that it would be useful to properly contextualize these terms semantically and historically. In this concept paper, we explore the various definitions proposed in the literature and emphasize that ambiguous terminology can lead to misplaced apprehension. We assert that the central motifs of both concepts have existed within the surgical sciences long before their appearance as terms in the scientific literature. PMID:25629029

Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems.

PubMed

Mohammed, Abdul-Wahid; Xu, Yang; Hu, Haixiao; Agyemang, Brighter

2016-09-21

In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach.

Anterior temporal cortex and semantic memory: reconciling findings from neuropsychology and functional imaging.

PubMed

Rogers, Timothy T; Hocking, Julia; Noppeney, Uta; Mechelli, Andrea; Gorno-Tempini, Maria Luisa; Patterson, Karalyn; Price, Cathy J

2006-09-01

Studies of semantic impairment arising from brain disease suggest that the anterior temporal lobes are critical for semantic abilities in humans; yet activation of these regions is rarely reported in functional imaging studies of healthy controls performing semantic tasks. Here, we combined neuropsychological and PET functional imaging data to show that when healthy subjects identify concepts at a specific level, the regions activated correspond to the site of maximal atrophy in patients with relatively pure semantic impairment. The stimuli were color photographs of common animals or vehicles, and the task was category verification at specific (e.g., robin), intermediate (e.g., bird), or general (e.g., animal) levels. Specific, relative to general, categorization activated the antero-lateral temporal cortices bilaterally, despite matching of these experimental conditions for difficulty. Critically, in patients with atrophy in precisely these areas, the most pronounced deficit was in the retrieval of specific semantic information.

ERP Evidence for the Activation of Syntactic Structure During Comprehension of Lexical Idiom.

PubMed

Zhang, Meichao; Lu, Aitao; Song, Pingfang

2017-10-01

The present study used event-related potentials to investigate whether the syntactic structure was activated in the comprehension of lexical idioms, and if so, whether it varied as a function of familiarity and semantic transparency. Participants were asked to passively read the "1+2" structural Chinese lexical idioms with each being presented following 3-5 contextual "1+2" (congruent-structure condition) or "2+1" structural Chinese phrases (incongruent-structure condition). The N400 ERP responses showed more positivity in congruent-structure condition relative to incongruent-structure condition in idioms with high familiarity and high semantic transparency, but less positivity in congruent-structure condition in idioms with high familiarity but low semantic transparency, idioms with low familiarity but high semantic transparency, and idioms with low familiarity and low semantic transparency. Our results suggest that syntactic structure, as the unnecessarity of lexical idiomatic words, was nevertheless activated, independent of familiarity and semantic transparency.

Imageability and semantic association in the representation and processing of event verbs.

PubMed

Xu, Xu; Kang, Chunyan; Guo, Taomei

2016-05-01

This study examined the relative salience of imageability (the degree to which a word evokes mental imagery) versus semantic association (the density of semantic network in which a word is embedded) in the representation and processing of four types of event verbs: sensory, cognitive, speech, and motor verbs. ERP responses were recorded, while 34 university students performed on a lexical decision task. Analysis focused primarily on amplitude differences across verb conditions within the N400 time window where activities are considered representing meaning activation. Variation in N400 amplitude across four types of verbs was found significantly associated with the level of imageability, but not the level of semantic association. The findings suggest imageability as a more salient factor relative to semantic association in the processing of these verbs. The role of semantic association and the representation of speech verbs are also discussed.

The origins of age of acquisition and typicality effects: Semantic processing in aphasia and the ageing brain.

PubMed

Räling, Romy; Schröder, Astrid; Wartenburger, Isabell

2016-06-01

Age of acquisition (AOA) has frequently been shown to influence response times and accuracy rates in word processing and constitutes a meaningful variable in aphasic language processing, while its origin in the language processing system is still under debate. To find out where AOA originates and whether and how it is related to another important psycholinguistic variable, namely semantic typicality (TYP), we studied healthy, elderly controls and semantically impaired individuals using semantic priming. For this purpose, we collected reaction times and accuracy rates as well as event-related potential data in an auditory category-member-verification task. The present results confirm a semantic origin of TYP, but question the same for AOA while favouring its origin at the phonology-semantics interface. The data are further interpreted in consideration of recent theories of ageing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Adult and Child Semantic Neighbors of the Kroll and Potter (1984) Nonobjects

PubMed Central

Storkel, Holly L.; Adlof, Suzanne M.

2008-01-01

Purpose The purpose was to determine the number of semantic neighbors, namely semantic set size, for 88 nonobjects (Kroll & Potter, 1984) and determine how semantic set size related to other measures and age. Method Data were collected from 82 adults and 92 preschool children in a discrete association task. The nonobjects were presented via computer, and participants reported the first word that came to mind that was meaningfully related to the nonobject. Words reported by two or more participants were considered semantic neighbors. The strength of each neighbor was computed as the proportion of participants who reported the neighbor. Results Results showed that semantic set size was not significantly correlated with objectlikeness ratings or object decision reaction times from Kroll and Potter (1984). However, semantic set size was significantly negatively correlated with the strength of the strongest neighbor(s). In terms of age effects, adult and child semantic set sizes were significantly positively correlated and the majority of numeric differences were on the order of 0–3 neighbors. Comparison of actual neighbors showed greater discrepancies; however, this varied by neighbor strength. Conclusions Semantic set size can be determined for nonobjects. Specific guidelines are suggested for using these nonobjects in future research. PMID:19252127

What is in a contour map? A region-based logical formalization of contour semantics

USGS Publications Warehouse

Usery, E. Lynn; Hahmann, Torsten

2015-01-01

This paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours.

A Combination of Thematic and Similarity-Based Semantic Processes Confers Resistance to Deficit Following Left Hemisphere Stroke

PubMed Central

Kalénine, Solène; Mirman, Daniel; Buxbaum, Laurel J.

2012-01-01

Semantic knowledge may be organized in terms of similarity relations based on shared features and/or complementary relations based on co-occurrence in events. Thus, relationships between manipulable objects such as tools may be defined by their functional properties (what the objects are used for) or thematic properties (e.g., what the objects are used with or on). A recent study from our laboratory used eye-tracking to examine incidental activation of semantic relations in a word–picture matching task and found relatively early activation of thematic relations (e.g., broom–dustpan), later activation of general functional relations (e.g., broom–sponge), and an intermediate pattern for specific functional relations (e.g., broom–vacuum cleaner). Combined with other recent studies, these results suggest that there are distinct semantic systems for thematic and similarity-based knowledge and that the “specific function” condition drew on both systems. This predicts that left hemisphere stroke that damages either system (but not both) may spare specific function processing. The present experiment tested these hypotheses using the same experimental paradigm with participants with left hemisphere lesions (N = 17). The results revealed that, compared to neurologically intact controls (N = 12), stroke participants showed later activation of thematic and general function relations, but activation of specific function relations was spared and was significantly earlier for stroke participants than controls. Across the stroke participants, activation of thematic and general function relations was negatively correlated, further suggesting that damage tended to affect either one semantic system or the other. These results support the distinction between similarity-based and complementarity-based semantic relations and suggest that relations that draw on both systems are relatively more robust to damage. PMID:22586383

Late positive slow waves as markers of chunking during encoding

PubMed Central

Nogueira, Ana M. L.; Bueno, Orlando F. A.; Manzano, Gilberto M.; Kohn, André F.; Pompéia, Sabine

2015-01-01

Electrophysiological markers of chunking of words during encoding have mostly been shown in studies that present pairs of related stimuli. In these cases it is difficult to disentangle cognitive processes that reflect distinctiveness (i.e., conspicuous items because they are related), perceived association between related items and unified representations of various items, or chunking. Here, we propose a paradigm that enables the determination of a separate Event-related Potential (ERP) marker of these cognitive processes using sequentially related word triads. Twenty-three young healthy individuals viewed 80 15-word lists composed of unrelated items except for the three words in the middle serial positions (triads), which could be either unrelated (control list), related perceptually, phonetically or semantically. ERP amplitudes were measured at encoding of each one of the words in the triads. We analyzed two latency intervals (350–400 and 400–800 ms) at midline locations. Behaviorally, we observed a progressive facilitation in the immediate free recall of the words in the triads depending on the relations between their items (control < perceptual < phonetic < semantic), but only semantically related items were recalled as chunks. P300-like deflections were observed for perceptually deviant stimuli. A reduction of amplitude of a component akin to the N400 was found for words that were phonetically and semantically associated with prior items and therefore were not associated to chunking. Positive slow wave (PSW) amplitudes increased as successive phonetically and semantically related items were presented, but they were observed earlier and were more prominent at Fz for semantic associates. PSWs at Fz and Cz also correlated with recall of semantic word chunks. This confirms prior claims that PSWs at Fz are potential markers of chunking which, in the proposed paradigm, were modulated differently from the detection of deviant stimuli and of relations between stimuli. PMID:26283984

A funny thing happened on the way to articulation: N400 attenuation despite behavioral interference in picture naming

PubMed Central

Blackford, Trevor; Holcomb, Phillip J.; Grainger, Jonathan; Kuperberg, Gina R.

2013-01-01

We measured Event-Related Potentials (ERPs) and naming times to picture targets preceded by masked words (stimulus onset asynchrony: 80 ms) that shared one of three different types of relationship with the names of the pictures: (1) Identity related, in which the prime was the name of the picture (“socks” – ), (2) Phonemic Onset related, in which the initial segment of the prime was the same as the name of the picture (“log” – ), and (3) Semantically related in which the prime was a co–category exemplar and associated with the name of the picture (“cake” – ). Each type of related picture target was contrasted with an Unrelated picture target, resulting in a 3 × 2 design that crossed Relationship Type between the word and the target picture (Identity, Phonemic Onset and Semantic) with Relatedness (Related and Unrelated). Modulation of the N400 component to related (versus unrelated) pictures was taken to reflect semantic processing at the interface between the picture's conceptual features and its lemma, while naming times reflected the end product of all stages of processing. Both attenuation of the N400 and shorter naming times were observed to pictures preceded by Identity related (versus Unrelated) words. No ERP effects within 600 ms, but shorter naming times, were observed to pictures preceded by Phonemic Onset related (versus Unrelated) words. An attenuated N400 (electrophysiological semantic priming) but longer naming times (behavioral semantic interference) were observed to pictures preceded by Semantically related (versus Unrelated) words. These dissociations between ERP modulation and naming times suggest that (a) phonemic onset priming occurred late, during encoding of the articulatory response, and (b) semantic behavioral interference was not driven by competition at the lemma level of representation, but rather occurred at a later stage of production. PMID:22245030

Modulation of alpha oscillations is required for the suppression of semantic interference.

PubMed

Melnik, Natalia; Mapelli, Igor; Özkurt, Tolga Esat

2017-10-01

Recent findings on alpha band oscillations suggest their important role in memory consolidation and suppression of external distractors such as environmental noise. However, less attention was given to the phenomenon of internal distracting information being solely inherent to the stimuli content. Human memory may be prone to internal distractions caused by semantic relatedness between the meaning of words (e.g., atom, neutron, nucleus, etc.) to be encoded, i.e., semantic interference. Our study investigates the brain oscillatory dynamics behind the semantic interference phenomenon, whose possible outcome is known as false memories. In this direction, Deese-Roediger-McDermott word lists were appropriated for a modified Sternberg paradigm in auditory modality. Participants received semantically related and unrelated word lists via headphones while EEG data were acquired. Semantic interference triggered the false memory rates to be higher than those of other types of memory errors. Analysis demonstrated that the upper part of alpha band (∼10-12Hz) power decreases on parieto-occipital channels in the retention interval, prior to the probe item for semantically related condition. Our study elucidates the oscillatory mechanisms behind semantic interference by relying on alpha functional inhibition theory. Copyright © 2017 Elsevier Inc. All rights reserved.

Self-referential processing is distinct from semantic elaboration: evidence from long-term memory effects in a patient with amnesia and semantic impairments.

PubMed

Sui, Jie; Humphreys, Glyn W

2013-11-01

We report data demonstrating that self-referential encoding facilitates memory performance in the absence of effects of semantic elaboration in a severely amnesic patient also suffering semantic problems. In Part 1, the patient, GA, was trained to associate items with the self or a familiar other during the encoding phase of a memory task (self-ownership decisions in Experiment 1 and self-evaluation decisions in Experiment 2). Tests of memory showed a consistent self-reference advantage, relative to a condition where the reference was another person in both experiments. The pattern of the self-reference advantage was similar to that in healthy controls. In Part 2 we demonstrate that GA showed minimal effects of semantic elaboration on memory for items he semantically classified, compared with items subject to physical size decisions; in contrast, healthy controls demonstrated enhanced memory performance after semantic relative to physical encoding. The results indicate that self-referential encoding, not semantic elaboration, improves memory in amnesia. Self-referential processing may provide a unique scaffold to help improve learning in amnesic cases. Copyright © 2013 Elsevier Ltd. All rights reserved.

Varieties of semantic ‘access’ deficit in Wernicke’s aphasia and semantic aphasia

PubMed Central

Robson, Holly; Lambon Ralph, Matthew A.; Jefferies, Elizabeth

2015-01-01

Comprehension deficits are common in stroke aphasia, including in cases with (i) semantic aphasia, characterized by poor executive control of semantic processing across verbal and non-verbal modalities; and (ii) Wernicke’s aphasia, associated with poor auditory–verbal comprehension and repetition, plus fluent speech with jargon. However, the varieties of these comprehension problems, and their underlying causes, are not well understood. Both patient groups exhibit some type of semantic ‘access’ deficit, as opposed to the ‘storage’ deficits observed in semantic dementia. Nevertheless, existing descriptions suggest that these patients might have different varieties of ‘access’ impairment—related to difficulty resolving competition (in semantic aphasia) versus initial activation of concepts from sensory inputs (in Wernicke’s aphasia). We used a case series design to compare patients with Wernicke’s aphasia and those with semantic aphasia on Warrington’s paradigmatic assessment of semantic ‘access’ deficits. In these verbal and non-verbal matching tasks, a small set of semantically-related items are repeatedly presented over several cycles so that the target on one trial becomes a distractor on another (building up interference and eliciting semantic ‘blocking’ effects). Patients with Wernicke’s aphasia and semantic aphasia were distinguished according to lesion location in the temporal cortex, but in each group, some individuals had additional prefrontal damage. Both of these aspects of lesion variability—one that mapped onto classical ‘syndromes’ and one that did not—predicted aspects of the semantic ‘access’ deficit. Both semantic aphasia and Wernicke’s aphasia cases showed multimodal semantic impairment, although as expected, the Wernicke’s aphasia group showed greater deficits on auditory-verbal than picture judgements. Distribution of damage in the temporal lobe was crucial for predicting the initially ‘beneficial’ effects of stimulus repetition: cases with Wernicke’s aphasia showed initial improvement with repetition of words and pictures, while in semantic aphasia, semantic access was initially good but declined in the face of competition from previous targets. Prefrontal damage predicted the ‘harmful’ effects of repetition: the ability to reselect both word and picture targets in the face of mounting competition was linked to left prefrontal damage in both groups. Therefore, patients with semantic aphasia and Wernicke’s aphasia have partially distinct impairment of semantic ‘access’ but, across these syndromes, prefrontal lesions produce declining comprehension with repetition in both verbal and non-verbal tasks. PMID:26454668

Varieties of semantic 'access' deficit in Wernicke's aphasia and semantic aphasia.

PubMed

Thompson, Hannah E; Robson, Holly; Lambon Ralph, Matthew A; Jefferies, Elizabeth

2015-12-01

Comprehension deficits are common in stroke aphasia, including in cases with (i) semantic aphasia, characterized by poor executive control of semantic processing across verbal and non-verbal modalities; and (ii) Wernicke's aphasia, associated with poor auditory-verbal comprehension and repetition, plus fluent speech with jargon. However, the varieties of these comprehension problems, and their underlying causes, are not well understood. Both patient groups exhibit some type of semantic 'access' deficit, as opposed to the 'storage' deficits observed in semantic dementia. Nevertheless, existing descriptions suggest that these patients might have different varieties of 'access' impairment-related to difficulty resolving competition (in semantic aphasia) versus initial activation of concepts from sensory inputs (in Wernicke's aphasia). We used a case series design to compare patients with Wernicke's aphasia and those with semantic aphasia on Warrington's paradigmatic assessment of semantic 'access' deficits. In these verbal and non-verbal matching tasks, a small set of semantically-related items are repeatedly presented over several cycles so that the target on one trial becomes a distractor on another (building up interference and eliciting semantic 'blocking' effects). Patients with Wernicke's aphasia and semantic aphasia were distinguished according to lesion location in the temporal cortex, but in each group, some individuals had additional prefrontal damage. Both of these aspects of lesion variability-one that mapped onto classical 'syndromes' and one that did not-predicted aspects of the semantic 'access' deficit. Both semantic aphasia and Wernicke's aphasia cases showed multimodal semantic impairment, although as expected, the Wernicke's aphasia group showed greater deficits on auditory-verbal than picture judgements. Distribution of damage in the temporal lobe was crucial for predicting the initially 'beneficial' effects of stimulus repetition: cases with Wernicke's aphasia showed initial improvement with repetition of words and pictures, while in semantic aphasia, semantic access was initially good but declined in the face of competition from previous targets. Prefrontal damage predicted the 'harmful' effects of repetition: the ability to reselect both word and picture targets in the face of mounting competition was linked to left prefrontal damage in both groups. Therefore, patients with semantic aphasia and Wernicke's aphasia have partially distinct impairment of semantic 'access' but, across these syndromes, prefrontal lesions produce declining comprehension with repetition in both verbal and non-verbal tasks. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

PubMed Central

Crangle, Colleen E.; Perreau-Guimaraes, Marcos; Suppes, Patrick

2013-01-01

This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model. PMID:23799009

Workspaces in the Semantic Web

NASA Technical Reports Server (NTRS)

Wolfe, Shawn R.; Keller, RIchard M.

2005-01-01

Due to the recency and relatively limited adoption of Semantic Web technologies. practical issues related to technology scaling have received less attention than foundational issues. Nonetheless, these issues must be addressed if the Semantic Web is to realize its full potential. In particular, we concentrate on the lack of scoping methods that reduce the size of semantic information spaces so they are more efficient to work with and more relevant to an agent's needs. We provide some intuition to motivate the need for such reduced information spaces, called workspaces, give a formal definition, and suggest possible methods of deriving them.

Effects of semantic relatedness on recall of stimuli preceding emotional oddballs.

PubMed

Smith, Ryan M; Beversdorf, David Q

2008-07-01

Semantic and episodic memory networks function as highly interconnected systems, both relying on the hippocampal/medial temporal lobe complex (HC/MTL). Episodic memory encoding triggers the retrieval of semantic information, serving to incorporate contextual relationships between the newly acquired memory and existing semantic representations. While emotional material augments episodic memory encoding at the time of stimulus presentation, interactions between emotion and semantic memory that contribute to subsequent episodic recall are not well understood. Using a modified oddball task, we examined the modulatory effects of negative emotion on semantic interactions with episodic memory by measuring the free-recall of serially presented neutral or negative words varying in semantic relatedness. We found increased free-recall for words related to and preceding emotionally negative oddballs, suggesting that negative emotion can indirectly facilitate episodic free-recall by enhancing semantic contributions during encoding. Our findings demonstrate the ability of emotion and semantic memory to interact to mutually enhance free-recall.

Natural speech reveals the semantic maps that tile human cerebral cortex

PubMed Central

Huth, Alexander G.; de Heer, Wendy A.; Griffiths, Thomas L.; Theunissen, Frédéric E.; Gallant, Jack L.

2016-01-01

The meaning of language is represented in regions of the cerebral cortex collectively known as the “semantic system”. However, little of the semantic system has been mapped comprehensively, and the semantic selectivity of most regions is unknown. Here we systematically map semantic selectivity across the cortex using voxel-wise modeling of fMRI data collected while subjects listened to hours of narrative stories. We show that the semantic system is organized into intricate patterns that appear consistent across individuals. We then use a novel generative model to create a detailed semantic atlas. Our results suggest that most areas within the semantic system represent information about specific semantic domains, or groups of related concepts, and our atlas shows which domains are represented in each area. This study demonstrates that data-driven methods—commonplace in studies of human neuroanatomy and functional connectivity—provide a powerful and efficient means for mapping functional representations in the brain. PMID:27121839

An Electrophysiological Investigation of Early Effects of Masked Morphological Priming

ERIC Educational Resources Information Center

Morris, Joanna; Grainger, Jonathan; Holcomb, Phillip J.

2008-01-01

This experiment examined event-related responses to targets preceded by semantically transparent morphologically related primes (e.g., farmer-farm), semantically opaque primes with an apparent morphological relation (corner-corn), and orthographically, but not morphologically, related primes (scandal-scan) using the masked priming technique…

Direct evidence for the contributive role of the right inferior fronto-occipital fasciculus in non-verbal semantic cognition.

PubMed

Herbet, Guillaume; Moritz-Gasser, Sylvie; Duffau, Hugues

2017-05-01

The neural foundations underlying semantic processing have been extensively investigated, highlighting a pivotal role of the ventral stream. However, although studies concerning the involvement of the left ventral route in verbal semantics are proficient, the potential implication of the right ventral pathway in non-verbal semantics has been to date unexplored. To gain insights on this matter, we used an intraoperative direct electrostimulation to map the structures mediating the non-verbal semantic system in the right hemisphere. Thirteen patients presenting with a right low-grade glioma located within or close to the ventral stream were included. During the 'awake' procedure, patients performed both a visual non-verbal semantic task and a verbal (control) task. At the cortical level, in the right hemisphere, we found non-verbal semantic-related sites (n = 7 in 6 patients) in structures commonly associated with verbal semantic processes in the left hemisphere, including the superior temporal gyrus, the pars triangularis, and the dorsolateral prefrontal cortex. At the subcortical level, we found non-verbal semantic-related sites in all but one patient (n = 15 sites in 12 patients). Importantly, all these responsive stimulation points were located on the spatial course of the right inferior fronto-occipital fasciculus (IFOF). These findings provide direct support for a critical role of the right IFOF in non-verbal semantic processing. Based upon these original data, and in connection with previous findings showing the involvement of the left IFOF in non-verbal semantic processing, we hypothesize the existence of a bilateral network underpinning the non-verbal semantic system, with a homotopic connectional architecture.

Semantic relations differentially impact associative recognition memory: electrophysiological evidence.

PubMed

Kriukova, Olga; Bridger, Emma; Mecklinger, Axel

2013-10-01

Though associative recognition memory is thought to rely primarily on recollection, recent research indicates that familiarity might also make a substantial contribution when to-be-learned items are integrated into a coherent structure by means of an existing semantic relation. It remains unclear how different types of semantic relations, such as categorical (e.g., dancer-singer) and thematic (e.g., dancer-stage) relations might affect associative recognition, however. Using event-related potentials (ERPs), we addressed this question by manipulating the type of semantic link between paired words in an associative recognition memory experiment. An early midfrontal old/new effect, typically linked to familiarity, was observed across the relation types. In contrast, a robust left parietal old/new effect was found in the categorical condition only, suggesting a clear contribution of recollection to associative recognition for this kind of pairs. One interpretation of this pattern is that familiarity was sufficiently diagnostic for associative recognition of thematic relations, which could result from the integrative nature of the thematic relatedness compared to the similarity-based nature of categorical pairs. The present study suggests that the extent to which recollection and familiarity are involved in associative recognition is at least in part determined by the properties of semantic relations between the paired associates. Copyright © 2013 Elsevier Inc. All rights reserved.

Damage to temporo-parietal cortex decreases incidental activation of thematic relations during spoken word comprehension

PubMed Central

Mirman, Daniel; Graziano, Kristen M.

2012-01-01

Both taxonomic and thematic semantic relations have been studied extensively in behavioral studies and there is an emerging consensus that the anterior temporal lobe plays a particularly important role in the representation and processing of taxonomic relations, but the neural basis of thematic semantics is less clear. We used eye tracking to examine incidental activation of taxonomic and thematic relations during spoken word comprehension in participants with aphasia. Three groups of participants were tested: neurologically intact control participants (N=14), individuals with aphasia resulting from lesions in left hemisphere BA 39 and surrounding temporo-parietal cortex regions (N=7), and individuals with the same degree of aphasia severity and semantic impairment and anterior left hemisphere lesions (primarily inferior frontal gyrus and anterior temporal lobe) that spared BA 39 (N=6). The posterior lesion group showed reduced and delayed activation of thematic relations, but not taxonomic relations. In contrast, the anterior lesion group exhibited longer-lasting activation of taxonomic relations and did not differ from control participants in terms of activation of thematic relations. These results suggest that taxonomic and thematic semantic knowledge are functionally and neuroanatomically distinct, with the temporo-parietal cortex playing a particularly important role in thematic semantics. PMID:22571932

Effects of semantic relatedness on age-related associative memory deficits: the role of theta oscillations.

PubMed

Crespo-Garcia, Maite; Cantero, Jose L; Atienza, Mercedes

2012-07-16

Growing evidence suggests that age-related deficits in associative memory are alleviated when the to-be-associated items are semantically related. Here we investigate whether this beneficial effect of semantic relatedness is paralleled by spatio-temporal changes in cortical EEG dynamics during incidental encoding. Young and older adults were presented with faces at a particular spatial location preceded by a biographical cue that was either semantically related or unrelated. As expected, automatic encoding of face-location associations benefited from semantic relatedness in the two groups of age. This effect correlated with increased power of theta oscillations over medial and anterior lateral regions of the prefrontal cortex (PFC) and lateral regions of the posterior parietal cortex (PPC) in both groups. But better-performing elders also showed increased brain-behavior correlation in the theta band over the right inferior frontal gyrus (IFG) as compared to young adults. Semantic relatedness was, however, insufficient to fully eliminate age-related differences in associative memory. In line with this finding, poorer-performing elders relative to young adults showed significant reductions of theta power in the left IFG that were further predictive of behavioral impairment in the recognition task. All together, these results suggest that older adults benefit less than young adults from executive processes during encoding mainly due to neural inefficiency over regions of the left ventrolateral prefrontal cortex (VLPFC). But this associative deficit may be partially compensated for by engaging preexistent semantic knowledge, which likely leads to an efficient recruitment of attentional and integration processes supported by the left PPC and left anterior PFC respectively, together with neural compensatory mechanisms governed by the right VLPFC. Copyright © 2012 Elsevier Inc. All rights reserved.

Deep processing activates the medial temporal lobe in young but not in old adults.

PubMed

Daselaar, Sander M; Veltman, Dick J; Rombouts, Serge A R B; Raaijmakers, Jeroen G W; Jonker, Cees

2003-11-01

Age-related impairments in episodic memory have been related to a deficiency in semantic processing, based on the finding that elderly adults typically benefit less than young adults from deep, semantic as opposed to shallow, nonsemantic processing of study items. In the present study, we tested the hypothesis that elderly adults are not able to perform certain cognitive operations under deep processing conditions. We further hypothesised that this inability does not involve regions commonly associated with lexical/semantic retrieval processes, but rather involves a dysfunction of the medial temporal lobe (MTL) memory system. To this end, we used functional MRI on rather extensive groups of young and elderly adults to compare brain activity patterns obtained during a deep (living/nonliving) and a shallow (uppercase/lowercase) classification task. Common activity in relation to semantic classification was observed in regions that have been previously related to semantic retrieval, including mainly left-lateralised activity in the inferior prefrontal, middle temporal, and middle frontal/anterior cingulate gyrus. Although the young adults showed more activity in some of these areas, the finding of mainly overlapping activation patterns during semantic classification supports the idea that lexical/semantic retrieval processes are still intact in elderly adults. This received further support by the finding that both groups showed similar behavioural performances as well on the deep and shallow classification tasks. Importantly, though, the young revealed significantly more activity than the elderly adults in the left anterior hippocampus during deep relative to shallow classification. This finding is in line with the idea that age-related impairments in episodic encoding are, at least partly, due to an under-recruitment of the medial temporal lobe memory system.

Neural correlates of lexical-semantic memory: A voxel-based morphometry study in mild AD, aMCI and normal aging

PubMed Central

Balthazar, Marcio L.F.; Yasuda, Clarissa L.; Lopes, Tátila M.; Pereira, Fabrício R.S.; Damasceno, Benito Pereira; Cendes, Fernando

2011-01-01

Neuroanatomical correlations of naming and lexical-semantic memory are not yet fully understood. The most influential approaches share the view that semantic representations reflect the manner in which information has been acquired through perception and action, and that each brain area processes different modalities of semantic representations. Despite these anatomical differences in semantic processing, generalization across different features that have similar semantic significance is one of the main characteristics of human cognition. Methods We evaluated the brain regions related to naming, and to the semantic generalization, of visually presented drawings of objects from the Boston Naming Test (BNT), which comprises different categories, such as animals, vegetables, tools, food, and furniture. In order to create a model of lesion method, a sample of 48 subjects presenting with a continuous decline both in cognitive functions, including naming skills, and in grey matter density (GMD) was compared to normal young adults with normal aging, amnestic mild cognitive impairment (aMCI) and mild Alzheimer’s disease (AD). Semantic errors on the BNT, as well as naming performance, were correlated with whole brain GMD as measured by voxel-based morphometry (VBM). Results The areas most strongly related to naming and to semantic errors were the medial temporal structures, thalami, superior and inferior temporal gyri, especially their anterior parts, as well as prefrontal cortices (inferior and superior frontal gyri). Conclusion The possible role of each of these areas in the lexical-semantic networks was discussed, along with their contribution to the models of semantic memory organization. PMID:29213726

Supporting infobuttons with terminological knowledge.

PubMed Central

Cimino, J. J.; Elhanan, G.; Zeng, Q.

1997-01-01

We have developed several prototype applications which integrate clinical systems with on-line information resources by using patient data to drive queries in response to user information needs. We refer to these collectively as infobuttons because they are evoked with a minimum of keyboard entry. We make use of knowledge in our terminology, the Medical Entities Dictionary (MED) to assist with the selection of appropriate queries and resources, as well as the translation of patient data to forms recognized by the resources. This paper describes the kinds of knowledge in the MED, including literal attributes, hierarchical links and other semantic links, and how this knowledge is used in system integration. PMID:9357682

Learning to Understand Natural Language with Less Human Effort

DTIC Science & Technology

2015-05-01

j ); if one of these has the correct logical form, ` j = `i, then tj is taken as the approximate maximizer. 29 2.3 Discussion This chapter...where j indexes entity tuples (e1, e2). Training optimizes the semantic parser parameters θ to predict Y = yj,Z = zj given S = sj . The parameters θ...be au tif ul / J J N 1 /N 1 λ f .f L on do n /N N P N λ x .M (x ,“ lo nd on ”, C IT Y ) N : λ x .M (x ,“ lo nd on ”, C IT Y ) (S [d cl ]\\N

Supporting infobuttons with terminological knowledge.

PubMed

Cimino, J J; Elhanan, G; Zeng, Q

1997-01-01

We have developed several prototype applications which integrate clinical systems with on-line information resources by using patient data to drive queries in response to user information needs. We refer to these collectively as infobuttons because they are evoked with a minimum of keyboard entry. We make use of knowledge in our terminology, the Medical Entities Dictionary (MED) to assist with the selection of appropriate queries and resources, as well as the translation of patient data to forms recognized by the resources. This paper describes the kinds of knowledge in the MED, including literal attributes, hierarchical links and other semantic links, and how this knowledge is used in system integration.

Naming and categorizing objects: task differences modulate the polarity of semantic effects in the picture-word interference paradigm.

PubMed

Hantsch, Ansgar; Jescheniak, Jörg D; Mädebach, Andreas

2012-07-01

The picture-word interference paradigm is a prominent tool for studying lexical retrieval during speech production. When participants name the pictures, interference from semantically related distractor words has regularly been shown. By contrast, when participants categorize the pictures, facilitation from semantically related distractors has typically been found. In the extant studies, however, differences in the task instructions (naming vs. categorizing) were confounded with the response level: While responses in naming were typically located at the basic level (e.g., "dog"), responses were located at the superordinate level in categorization (e.g., "animal"). The present study avoided this confound by having participants respond at the basic level in both naming and categorization, using the same pictures, distractors, and verbal responses. Our findings confirm the polarity reversal of the semantic effects--that is, semantic interference in naming, and semantic facilitation in categorization. These findings show that the polarity reversal of the semantic effect is indeed due to the different tasks and is not an artifact of the different response levels used in previous studies. Implications for current models of language production are discussed.

Suggestion-Induced Modulation of Semantic Priming during Functional Magnetic Resonance Imaging

PubMed Central

Ulrich, Martin; Kiefer, Markus; Bongartz, Walter; Grön, Georg; Hoenig, Klaus

2015-01-01

Using functional magnetic resonance imaging during a primed visual lexical decision task, we investigated the neural and functional mechanisms underlying modulations of semantic word processing through hypnotic suggestions aimed at altering lexical processing of primes. The priming task was to discriminate between target words and pseudowords presented 200 ms after the prime word which was semantically related or unrelated to the target. In a counterbalanced study design, each participant performed the task once at normal wakefulness and once after the administration of hypnotic suggestions to perceive the prime as a meaningless symbol of a foreign language. Neural correlates of priming were defined as significantly lower activations upon semantically related compared to unrelated trials. We found significant suggestive treatment-induced reductions in neural priming, albeit irrespective of the degree of suggestibility. Neural priming was attenuated upon suggestive treatment compared with normal wakefulness in brain regions supporting automatic (fusiform gyrus) and controlled semantic processing (superior and middle temporal gyri, pre- and postcentral gyri, and supplementary motor area). Hence, suggestions reduced semantic word processing by conjointly dampening both automatic and strategic semantic processes. PMID:25923740

22 CFR 96.103 - Oversight by accrediting entities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 22 Foreign Relations 1 2011-04-01 2011-04-01 false Oversight by accrediting entities. 96.103... Relating to Temporary Accreditation § 96.103 Oversight by accrediting entities. (a) The accrediting entity... agency's application for full accreditation when it is filed. The accrediting entity must also...

22 CFR 96.103 - Oversight by accrediting entities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Oversight by accrediting entities. 96.103... Relating to Temporary Accreditation § 96.103 Oversight by accrediting entities. (a) The accrediting entity... agency's application for full accreditation when it is filed. The accrediting entity must also...

The role of left prefrontal cortex in language and memory

PubMed Central

Gabrieli, John D. E.; Poldrack, Russell A.; Desmond, John E.

1998-01-01

This article reviews attempts to characterize the mental operations mediated by left inferior prefrontal cortex, especially the anterior and inferior portion of the gyrus, with the functional neuroimaging techniques of positron emission tomography and functional magnetic resonance imaging. Activations in this region occur during semantic, relative to nonsemantic, tasks for the generation of words to semantic cues or the classification of words or pictures into semantic categories. This activation appears in the right prefrontal cortex of people known to be atypically right-hemisphere dominant for language. In this region, activations are associated with meaningful encoding that leads to superior explicit memory for stimuli and deactivations with implicit semantic memory (repetition priming) for words and pictures. New findings are reported showing that patients with global amnesia show deactivations in the same region associated with repetition priming, that activation in this region reflects selection of a response from among numerous relative to few alternatives, and that activations in a portion of this region are associated specifically with semantic relative to phonological processing. It is hypothesized that activations in left inferior prefrontal cortex reflect a domain-specific semantic working memory capacity that is invoked more for semantic than nonsemantic analyses regardless of stimulus modality, more for initial than for repeated semantic analysis of a word or picture, more when a response must be selected from among many than few legitimate alternatives, and that yields superior later explicit memory for experiences. PMID:9448258

Long-term interference at the semantic level: Evidence from blocked-cyclic picture matching.

PubMed

Wei, Tao; Schnur, Tatiana T

2016-01-01

Processing semantically related stimuli creates interference across various domains of cognition, including language and memory. In this study, we identify the locus and mechanism of interference when retrieving meanings associated with words and pictures. Subjects matched a probe stimulus (e.g., cat) to its associated target picture (e.g., yarn) from an array of unrelated pictures. Across trials, probes were either semantically related or unrelated. To test the locus of interference, we presented probes as either words or pictures. If semantic interference occurs at the stage common to both tasks, that is, access to semantic representations, then interference should occur in both probe presentation modalities. Results showed clear semantic interference effects independent of presentation modality and lexical frequency, confirming a semantic locus of interference in comprehension. To test the mechanism of interference, we repeated trials across 4 presentation cycles and manipulated the number of unrelated intervening trials (zero vs. two). We found that semantic interference was additive across cycles and survived 2 intervening trials, demonstrating interference to be long-lasting as opposed to short-lived. However, interference was smaller with zero versus 2 intervening trials, which we interpret to suggest that short-lived facilitation counteracted the long-lived interference. We propose that retrieving meanings associated with words/pictures from the same semantic category yields both interference due to long-lasting changes in connection strength between semantic representations (i.e., incremental learning) and facilitation caused by short-lived residual activation. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Neural Substrates of Semantic Prospection – Evidence from the Dementias

PubMed Central

Irish, Muireann; Eyre, Nadine; Dermody, Nadene; O’Callaghan, Claire; Hodges, John R.; Hornberger, Michael; Piguet, Olivier

2016-01-01

The ability to envisage personally relevant events at a future time point represents an incredibly sophisticated cognitive endeavor and one that appears to be intimately linked to episodic memory integrity. Far less is known regarding the neurocognitive mechanisms underpinning the capacity to envisage non-personal future occurrences, known as semantic future thinking. Moreover the degree of overlap between the neural substrates supporting episodic and semantic forms of prospection remains unclear. To this end, we sought to investigate the capacity for episodic and semantic future thinking in Alzheimer’s disease (n = 15) and disease-matched behavioral-variant frontotemporal dementia (n = 15), neurodegenerative disorders characterized by significant medial temporal lobe (MTL) and frontal pathology. Participants completed an assessment of past and future thinking across personal (episodic) and non-personal (semantic) domains, as part of a larger neuropsychological battery investigating episodic and semantic processing, and their performance was contrasted with 20 age- and education-matched healthy older Controls. Participants underwent whole-brain T1-weighted structural imaging and voxel-based morphometry analysis was conducted to determine the relationship between gray matter integrity and episodic and semantic future thinking. Relative to Controls, both patient groups displayed marked future thinking impairments, extending across episodic and semantic domains. Analyses of covariance revealed that while episodic future thinking deficits could be explained solely in terms of episodic memory proficiency, semantic prospection deficits reflected the interplay between episodic and semantic processing. Distinct neural correlates emerged for each form of future simulation with differential involvement of prefrontal, lateral temporal, and medial temporal regions. Notably, the hippocampus was implicated irrespective of future thinking domain, with the suggestion of lateralization effects depending on the type of information being simulated. Whereas episodic future thinking related to right hippocampal integrity, semantic future thinking was found to relate to left hippocampal integrity. Our findings support previous observations of significant MTL involvement for semantic forms of prospection and point to distinct neurocognitive mechanisms which must be functional to support future-oriented forms of thought across personal and non-personal contexts. PMID:27252632

A Semantic-Relational-Concepts Based Theory of Language Acquisition as Applied to Down's Syndrome Children: Implication for a Language Enhancement Program. Research Report No. 62.

ERIC Educational Resources Information Center

Buium, Nissan; And Others

Speech samples were collected from three 48-month-old children with Down's Syndrome over an 11-month period after Ss had reached the one word utterance stage. Each S's linguistic utterances were semantically evaluated in terms of M. Bowerman's, R. Brown's, and I. Schlesinger's semantic relational concepts. Generally, findings suggested that Ss…

Productive extension of semantic memory in school-aged children: Relations with reading comprehension and deployment of cognitive resources.

PubMed

Bauer, Patricia J; Blue, Shala N; Xu, Aoxiang; Esposito, Alena G

2016-07-01

We investigated 7- to 10-year-old children's productive extension of semantic memory through self-generation of new factual knowledge derived through integration of separate yet related facts learned through instruction or through reading. In Experiment 1, an experimenter read the to-be-integrated facts. Children successfully learned and integrated the information and used it to further extend their semantic knowledge, as evidenced by high levels of correct responses in open-ended and forced-choice testing. In Experiment 2, on half of the trials, the to-be-integrated facts were read by an experimenter (as in Experiment 1) and on half of the trials, children read the facts themselves. Self-generation performance was high in both conditions (experimenter- and self-read); in both conditions, self-generation of new semantic knowledge was related to an independent measure of children's reading comprehension. In Experiment 3, the way children deployed cognitive resources during reading was predictive of their subsequent recall of newly learned information derived through integration. These findings indicate self-generation of new semantic knowledge through integration in school-age children as well as relations between this productive means of extension of semantic memory and cognitive processes engaged during reading. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Productive Extension of Semantic Memory in School-aged Children: Relations with Reading Comprehension and Deployment of Cognitive Resources

PubMed Central

Bauer, Patricia J.; Blue, Shala N.; Xu, Aoxiang; Esposito, Alena G.

2016-01-01

We investigated 7- to 10-year-old children’s productive extension of semantic memory through self-generation of new factual knowledge derived through integration of separate yet related facts learned through instruction or through reading. In Experiment 1, an experimenter read the to-be-integrated facts. Children successfully learned and integrated the information and used it to further extend their semantic knowledge, as evidenced by high levels of correct responses in open-ended and forced-choice testing. In Experiment 2, on half of the trials, the to-be-integrated facts were read by an experimenter (as in Experiment 1) and on half of the trials, children read the facts themselves. Self-generation performance was high in both conditions (experimenter- and self-read); in both conditions, self-generation of new semantic knowledge was related to an independent measure of children’s reading comprehension. In Experiment 3, the way children deployed cognitive resources during reading was predictive of their subsequent recall of newly learned information derived through integration. These findings indicate self-generation of new semantic knowledge through integration in school-age children as well as relations between this productive means of extension of semantic memory and cognitive processes engaged during reading. PMID:27253263

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

Effects of Embedded Processing Tasks on Learning Outcomes.

ERIC Educational Resources Information Center

Hobbs, D. J.

1987-01-01

Describes a British study with undergraduate accountancy students which compared the quantitative and qualitative effects of three types of embedded tasks or questions--relational-semantic, transpose-semantic, and non-semantic--on learning outcomes. Variables investigated included mathematical background, recall, and comprehension. Relevance of…

Electrophysiological correlates of cross-linguistic semantic integration in hearing signers: N400 and LPC.

PubMed

Zachau, Swantje; Korpilahti, Pirjo; Hämäläinen, Jarmo A; Ervast, Leena; Heinänen, Kaisu; Suominen, Kalervo; Lehtihalmes, Matti; Leppänen, Paavo H T

2014-07-01

We explored semantic integration mechanisms in native and non-native hearing users of sign language and non-signing controls. Event-related brain potentials (ERPs) were recorded while participants performed a semantic decision task for priming lexeme pairs. Pairs were presented either within speech or across speech and sign language. Target-related ERP responses were subjected to principal component analyses (PCA), and neurocognitive basis of semantic integration processes were assessed by analyzing the N400 and the late positive complex (LPC) components in response to spoken (auditory) and signed (visual) antonymic and unrelated targets. Semantically-related effects triggered across modalities would indicate a similar tight interconnection between the signers׳ two languages like that described for spoken language bilinguals. Remarkable structural similarity of the N400 and LPC components with varying group differences between the spoken and signed targets were found. The LPC was the dominant response. The controls׳ LPC differed from the LPC of the two signing groups. It was reduced to the auditory unrelated targets and was less frontal for all the visual targets. The visual LPC was more broadly distributed in native than non-native signers and was left-lateralized for the unrelated targets in the native hearing signers only. Semantic priming effects were found for the auditory N400 in all groups, but only native hearing signers revealed a clear N400 effect to the visual targets. Surprisingly, the non-native signers revealed no semantically-related processing effect to the visual targets reflected in the N400 or the LPC; instead they appeared to rely more on visual post-lexical analyzing stages than native signers. We conclude that native and non-native signers employed different processing strategies to integrate signed and spoken semantic content. It appeared that the signers׳ semantic processing system was affected by group-specific factors like language background and/or usage. Copyright © 2014 Elsevier Ltd. All rights reserved.

A VGI data integration framework based on linked data model

NASA Astrophysics Data System (ADS)

Wan, Lin; Ren, Rongrong

2015-12-01

This paper aims at the geographic data integration and sharing method for multiple online VGI data sets. We propose a semantic-enabled framework for online VGI sources cooperative application environment to solve a target class of geospatial problems. Based on linked data technologies - which is one of core components of semantic web, we can construct the relationship link among geographic features distributed in diverse VGI platform by using linked data modeling methods, then deploy these semantic-enabled entities on the web, and eventually form an interconnected geographic data network to support geospatial information cooperative application across multiple VGI data sources. The mapping and transformation from VGI sources to RDF linked data model is presented to guarantee the unique data represent model among different online social geographic data sources. We propose a mixed strategy which combined spatial distance similarity and feature name attribute similarity as the measure standard to compare and match different geographic features in various VGI data sets. And our work focuses on how to apply Markov logic networks to achieve interlinks of the same linked data in different VGI-based linked data sets. In our method, the automatic generating method of co-reference object identification model according to geographic linked data is discussed in more detail. It finally built a huge geographic linked data network across loosely-coupled VGI web sites. The results of the experiment built on our framework and the evaluation of our method shows the framework is reasonable and practicable.

Translating standards into practice - one Semantic Web API for Gene Expression.

PubMed

Deus, Helena F; Prud'hommeaux, Eric; Miller, Michael; Zhao, Jun; Malone, James; Adamusiak, Tomasz; McCusker, Jim; Das, Sudeshna; Rocca Serra, Philippe; Fox, Ronan; Marshall, M Scott

2012-08-01

Sharing and describing experimental results unambiguously with sufficient detail to enable replication of results is a fundamental tenet of scientific research. In today's cluttered world of "-omics" sciences, data standards and standardized use of terminologies and ontologies for biomedical informatics play an important role in reporting high-throughput experiment results in formats that can be interpreted by both researchers and analytical tools. Increasing adoption of Semantic Web and Linked Data technologies for the integration of heterogeneous and distributed health care and life sciences (HCLSs) datasets has made the reuse of standards even more pressing; dynamic semantic query federation can be used for integrative bioinformatics when ontologies and identifiers are reused across data instances. We present here a methodology to integrate the results and experimental context of three different representations of microarray-based transcriptomic experiments: the Gene Expression Atlas, the W3C BioRDF task force approach to reporting Provenance of Microarray Experiments, and the HSCI blood genomics project. Our approach does not attempt to improve the expressivity of existing standards for genomics but, instead, to enable integration of existing datasets published from microarray-based transcriptomic experiments. SPARQL Construct is used to create a posteriori mappings of concepts and properties and linking rules that match entities based on query constraints. We discuss how our integrative approach can encourage reuse of the Experimental Factor Ontology (EFO) and the Ontology for Biomedical Investigations (OBIs) for the reporting of experimental context and results of gene expression studies. Copyright © 2012 Elsevier Inc. All rights reserved.

An individual differences approach to semantic cognition: Divergent effects of age on representation, retrieval and selection.

PubMed

Hoffman, Paul

2018-05-25

Semantic cognition refers to the appropriate use of acquired knowledge about the world. This requires representation of knowledge as well as control processes which ensure that currently-relevant aspects of knowledge are retrieved and selected. Although these abilities can be impaired selectively following brain damage, the relationship between them in healthy individuals is unclear. It is also commonly assumed that semantic cognition is preserved in later life, because older people have greater reserves of knowledge. However, this claim overlooks the possibility of decline in semantic control processes. Here, semantic cognition was assessed in 100 young and older adults. Despite having a broader knowledge base, older people showed specific impairments in semantic control, performing more poorly than young people when selecting among competing semantic representations. Conversely, they showed preserved controlled retrieval of less salient information from the semantic store. Breadth of semantic knowledge was positively correlated with controlled retrieval but was unrelated to semantic selection ability, which was instead correlated with non-semantic executive function. These findings indicate that three distinct elements contribute to semantic cognition: semantic representations that accumulate throughout the lifespan, processes for controlled retrieval of less salient semantic information, which appear age-invariant, and mechanisms for selecting task-relevant aspects of semantic knowledge, which decline with age and may relate more closely to domain-general executive control.

Relation Extraction with Weak Supervision and Distributional Semantics

DTIC Science & Technology

2013-05-01

DATES COVERED 00-00-2013 to 00-00-2013 4 . TITLE AND SUBTITLE Relation Extraction with Weak Supervision and Distributional Semantics 5a...ix List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 Introduction 1 2 Prior Work 4 ...2.1 Supervised relation extraction . . . . . . . . . . . . . . . . . . . . . 4 2.2 Distant supervision for relation extraction

Semantic Bias in the Acquisition of Relative Clauses in Japanese

ERIC Educational Resources Information Center

Ozeki, Hiromi; Shirai, Yasuhiro

2010-01-01

This study analyzes the acquisition of relative clauses in Japanese to determine the semantic and functional characteristics of children's relative clauses in spontaneous speech. Longitudinal data from five Japanese children are analyzed and compared with English data (Diessel & Tomasello, 2000). The results show that the relative clauses produced…

What role does the anterior temporal lobe play in sentence-level processing? Neural correlates of syntactic processing in semantic variant primary progressive aphasia.

PubMed

Wilson, Stephen M; DeMarco, Andrew T; Henry, Maya L; Gesierich, Benno; Babiak, Miranda; Mandelli, Maria Luisa; Miller, Bruce L; Gorno-Tempini, Maria Luisa

2014-05-01

Neuroimaging and neuropsychological studies have implicated the anterior temporal lobe (ATL) in sentence-level processing, with syntactic structure-building and/or combinatorial semantic processing suggested as possible roles. A potential challenge to the view that the ATL is involved in syntactic aspects of sentence processing comes from the clinical syndrome of semantic variant primary progressive aphasia (semantic PPA; also known as semantic dementia). In semantic PPA, bilateral neurodegeneration of the ATLs is associated with profound lexical semantic deficits, yet syntax is strikingly spared. The goal of this study was to investigate the neural correlates of syntactic processing in semantic PPA to determine which regions normally involved in syntactic processing are damaged in semantic PPA and whether spared syntactic processing depends on preserved functionality of intact regions, preserved functionality of atrophic regions, or compensatory functional reorganization. We scanned 20 individuals with semantic PPA and 24 age-matched controls using structural MRI and fMRI. Participants performed a sentence comprehension task that emphasized syntactic processing and minimized lexical semantic demands. We found that, in controls, left inferior frontal and left posterior temporal regions were modulated by syntactic processing, whereas anterior temporal regions were not significantly modulated. In the semantic PPA group, atrophy was most severe in the ATLs but extended to the posterior temporal regions involved in syntactic processing. Functional activity for syntactic processing was broadly similar in patients and controls; in particular, whole-brain analyses revealed no significant differences between patients and controls in the regions modulated by syntactic processing. The atrophic left ATL did show abnormal functionality in semantic PPA patients; however, this took the unexpected form of a failure to deactivate. Taken together, our findings indicate that spared syntactic processing in semantic PPA depends on preserved functionality of structurally intact left frontal regions and moderately atrophic left posterior temporal regions, but no functional reorganization was apparent as a consequence of anterior temporal atrophy and dysfunction. These results suggest that the role of the ATL in sentence processing is less likely to relate to syntactic structure-building and more likely to relate to higher-level processes such as combinatorial semantic processing.

The Ontology for Biomedical Investigations.

PubMed

Bandrowski, Anita; Brinkman, Ryan; Brochhausen, Mathias; Brush, Matthew H; Bug, Bill; Chibucos, Marcus C; Clancy, Kevin; Courtot, Mélanie; Derom, Dirk; Dumontier, Michel; Fan, Liju; Fostel, Jennifer; Fragoso, Gilberto; Gibson, Frank; Gonzalez-Beltran, Alejandra; Haendel, Melissa A; He, Yongqun; Heiskanen, Mervi; Hernandez-Boussard, Tina; Jensen, Mark; Lin, Yu; Lister, Allyson L; Lord, Phillip; Malone, James; Manduchi, Elisabetta; McGee, Monnie; Morrison, Norman; Overton, James A; Parkinson, Helen; Peters, Bjoern; Rocca-Serra, Philippe; Ruttenberg, Alan; Sansone, Susanna-Assunta; Scheuermann, Richard H; Schober, Daniel; Smith, Barry; Soldatova, Larisa N; Stoeckert, Christian J; Taylor, Chris F; Torniai, Carlo; Turner, Jessica A; Vita, Randi; Whetzel, Patricia L; Zheng, Jie

2016-01-01

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

The Ontology for Biomedical Investigations

PubMed Central

Bandrowski, Anita; Brinkman, Ryan; Brochhausen, Mathias; Brush, Matthew H.; Chibucos, Marcus C.; Clancy, Kevin; Courtot, Mélanie; Derom, Dirk; Dumontier, Michel; Fan, Liju; Fostel, Jennifer; Fragoso, Gilberto; Gibson, Frank; Gonzalez-Beltran, Alejandra; Haendel, Melissa A.; He, Yongqun; Heiskanen, Mervi; Hernandez-Boussard, Tina; Jensen, Mark; Lin, Yu; Lister, Allyson L.; Lord, Phillip; Malone, James; Manduchi, Elisabetta; McGee, Monnie; Morrison, Norman; Overton, James A.; Parkinson, Helen; Peters, Bjoern; Rocca-Serra, Philippe; Ruttenberg, Alan; Sansone, Susanna-Assunta; Scheuermann, Richard H.; Schober, Daniel; Smith, Barry; Soldatova, Larisa N.; Stoeckert, Christian J.; Taylor, Chris F.; Torniai, Carlo; Turner, Jessica A.; Vita, Randi; Whetzel, Patricia L.; Zheng, Jie

2016-01-01

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl. PMID:27128319

Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology

PubMed Central

2013-01-01

Background The Gene Ontology (GO) facilitates the description of the action of gene products in a biological context. Many GO terms refer to chemical entities that participate in biological processes. To facilitate accurate and consistent systems-wide biological representation, it is necessary to integrate the chemical view of these entities with the biological view of GO functions and processes. We describe a collaborative effort between the GO and the Chemical Entities of Biological Interest (ChEBI) ontology developers to ensure that the representation of chemicals in the GO is both internally consistent and in alignment with the chemical expertise captured in ChEBI. Results We have examined and integrated the ChEBI structural hierarchy into the GO resource through computationally-assisted manual curation of both GO and ChEBI. Our work has resulted in the creation of computable definitions of GO terms that contain fully defined semantic relationships to corresponding chemical terms in ChEBI. Conclusions The set of logical definitions using both the GO and ChEBI has already been used to automate aspects of GO development and has the potential to allow the integration of data across the domains of biology and chemistry. These logical definitions are available as an extended version of the ontology from http://purl.obolibrary.org/obo/go/extensions/go-plus.owl. PMID:23895341

Age-related differences in recall for words using semantics and prosody.

PubMed

Sober, Jonathan D; VanWormer, Lisa A; Arruda, James E

2016-01-01

The positivity effect is a developmental shift seen in older adults to be increasingly influenced by positive information in areas such as memory, attention, and decision-making. This study is the first to examine the age-related differences of the positivity effect for emotional prosody. Participants heard a factorial combination of words that were semantically positive or negative said with either positive or negative intonation. Results showed a semantic positivity effect for older adults, and a prosody positivity effect for younger adults. Additionally, older adults showed a significant decrease in recall for semantically negative words said in an incongruent prosodically positive tone.

A brain electrical signature of left-lateralized semantic activation from single words.

PubMed

Koppehele-Gossel, Judith; Schnuerch, Robert; Gibbons, Henning

2016-01-01

Lesion and imaging studies consistently indicate a left-lateralization of semantic language processing in human temporo-parietal cortex. Surprisingly, electrocortical measures, which allow a direct assessment of brain activity and the tracking of cognitive functions with millisecond precision, have not yet been used to capture this hemispheric lateralization, at least with respect to posterior portions of this effect. Using event-related potentials, we employed a simple single-word reading paradigm to compare neural activity during three tasks requiring different degrees of semantic processing. As expected, we were able to derive a simple temporo-parietal left-right asymmetry index peaking around 300ms into word processing that neatly tracks the degree of semantic activation. The validity of this measure in specifically capturing verbal semantic activation was further supported by a significant relation to verbal intelligence. We thus posit that it represents a promising tool to monitor verbal semantic processing in the brain with little technological effort and in a minimal experimental setup. Copyright © 2016 Elsevier Inc. All rights reserved.

Semantic processing in native and second language: evidence from hemispheric differences in fine and coarse semantic coding.

PubMed

Faust, Miriam; Ben-Artzi, Elisheva; Vardi, Nili

2012-12-01

Previous studies suggest that whereas the left hemisphere (LH) is involved in fine semantic processing, the right hemisphere (RH) is uniquely engaged in coarse semantic coding including the comprehension of distinct types of language such as figurative language, lexical ambiguity and verbal humor (e.g., Chiarello, 2003; Faust, 2012). The present study examined the patterns of hemispheric involvement in fine/coarse semantic processing in native and non-native languages using a split visual field priming paradigm. Thirty native Hebrew speaking students made lexical decision judgments of Hebrew and English target words preceded by strongly, weakly, or unrelated primes. Results indicated that whereas for Hebrew pairs, priming effect for the weakly-related word pairs was obtained only for RH presented target words, for English pairs, no priming effect for the weakly-related pairs emerged for either LH or RH presented targets, suggesting that coarse semantic coding is much weaker for a non-native than native language. Copyright © 2012 Elsevier Inc. All rights reserved.

The effects of gender and self-insight on early semantic processing.

PubMed

Xu, Xu; Kang, Chunyan; Guo, Taomei

2014-01-01

This event-related potential (ERP) study explored individual differences associated with gender and level of self-insight in early semantic processing. Forty-eight Chinese native speakers completed a semantic judgment task with three different categories of words: abstract neutral words (e.g., logic, effect), concrete neutral words (e.g., teapot, table), and emotion words (e.g., despair, guilt). They then assessed their levels of self-insight. Results showed that women engaged in greater processing than did men. Gender differences also manifested in the relationship between level of self-insight and word processing. For women, level of self-insight was associated with level of semantic activation for emotion words and abstract neutral words, but not for concrete neutral words. For men, level of self-insight was related to processing speed, particularly in response to abstract and concrete neutral words. These findings provide electrophysiological evidence for the effects of gender and self-insight on semantic processing and highlight the need to take into consideration subject variables in related research.

Does "a picture is worth 1000 words" apply to iconic Chinese words? Relationship of Chinese words and pictures.

PubMed

Lo, Shih-Yu; Yeh, Su-Ling

2018-05-29

The meaning of a picture can be extracted rapidly, but the form-to-meaning relationship is less obvious for printed words. In contrast to English words that follow grapheme-to-phoneme correspondence rule, the iconic nature of Chinese words might predispose them to activate their semantic representations more directly from their orthographies. By using the paradigm of repetition blindness (RB) that taps into the early level of word processing, we examined whether Chinese words activate their semantic representations as directly as pictures do. RB refers to the failure to detect the second occurrence of an item when it is presented twice in temporal proximity. Previous studies showed RB for semantically related pictures, suggesting that pictures activate their semantic representations directly from their shapes and thus two semantically related pictures are represented as repeated. However, this does not apply to English words since no RB was found for English synonyms. In this study, we replicated the semantic RB effect for pictures, and further showed the absence of semantic RB for Chinese synonyms. Based on our findings, it is suggested that Chinese words are processed like English words, which do not activate their semantic representations as directly as pictures do.

Evidence of semantic processing impairments in behavioural variant frontotemporal dementia and Parkinson's disease.

PubMed

Cousins, Katheryn A Q; Grossman, Murray

2017-12-01

Category-specific impairments caused by brain damage can provide important insights into how semantic concepts are organized in the brain. Recent research has demonstrated that disease to sensory and motor cortices can impair perceptual feature knowledge important to the representation of semantic concepts. This evidence supports the grounded cognition theory of semantics, the view that lexical knowledge is partially grounded in perceptual experience and that sensory and motor regions support semantic representations. Less well understood, however, is how heteromodal semantic hubs work to integrate and process semantic information. Although the majority of semantic research to date has focused on how sensory cortical areas are important for the representation of semantic features, new research explores how semantic memory is affected by neurodegeneration in regions important for semantic processing. Here, we review studies that demonstrate impairments to abstract noun knowledge in behavioural variant frontotemporal degeneration (bvFTD) and to action verb knowledge in Parkinson's disease, and discuss how these deficits relate to disease of the semantic selection network. Findings demonstrate that semantic selection processes are supported by the left inferior frontal gyrus (LIFG) and basal ganglia, and that disease to these regions in bvFTD and Parkinson's disease can lead to categorical impairments for abstract nouns and action verbs, respectively.

Episodic and Semantic Autobiographical Memory and Everyday Memory during Late Childhood and Early Adolescence

PubMed Central

Willoughby, Karen A.; Desrocher, Mary; Levine, Brian; Rovet, Joanne F.

2012-01-01

Few studies have examined both episodic and semantic autobiographical memory (AM) performance during late childhood and early adolescence. Using the newly developed Children’s Autobiographical Interview (CAI), the present study examined the effects of age and sex on episodic and semantic AM and everyday memory in 182 children and adolescents. Results indicated that episodic and semantic AM both improved between 8 and 16 years of age; however, age-related changes were larger for episodic AM than for semantic AM. In addition, females were found to recall more episodic AM details, but not more semantic AM details, than males. Importantly, this sex difference in episodic AM recall was attenuated under conditions of high retrieval support (i.e., the use of probing questions). The ability to clearly visualize past events at the time of recollection was related to children’s episodic AM recall performance, particularly the retrieval of perceptual details. Finally, similar age and sex effects were found between episodic AM and everyday memory ability (e.g., memory for everyday activities). More specifically, older participants and females exhibited better episodic AM and everyday memory performance than younger participants and males. Overall, the present study provides important new insight into both episodic and semantic AM performance, as well as the relation between episodic AM and everyday memory, during late childhood and adolescence. PMID:22403560

Episodic and Semantic Autobiographical Memory and Everyday Memory during Late Childhood and Early Adolescence.

PubMed

Willoughby, Karen A; Desrocher, Mary; Levine, Brian; Rovet, Joanne F

2012-01-01

Few studies have examined both episodic and semantic autobiographical memory (AM) performance during late childhood and early adolescence. Using the newly developed Children's Autobiographical Interview (CAI), the present study examined the effects of age and sex on episodic and semantic AM and everyday memory in 182 children and adolescents. Results indicated that episodic and semantic AM both improved between 8 and 16 years of age; however, age-related changes were larger for episodic AM than for semantic AM. In addition, females were found to recall more episodic AM details, but not more semantic AM details, than males. Importantly, this sex difference in episodic AM recall was attenuated under conditions of high retrieval support (i.e., the use of probing questions). The ability to clearly visualize past events at the time of recollection was related to children's episodic AM recall performance, particularly the retrieval of perceptual details. Finally, similar age and sex effects were found between episodic AM and everyday memory ability (e.g., memory for everyday activities). More specifically, older participants and females exhibited better episodic AM and everyday memory performance than younger participants and males. Overall, the present study provides important new insight into both episodic and semantic AM performance, as well as the relation between episodic AM and everyday memory, during late childhood and adolescence.

The influence of autonomic arousal and semantic relatedness on memory for emotional words.

PubMed

Buchanan, Tony W; Etzel, Joset A; Adolphs, Ralph; Tranel, Daniel

2006-07-01

Increased memory for emotional stimuli is a well-documented phenomenon. Emotional arousal during the encoding of a stimulus is one mediator of this memory enhancement. Other variables such as semantic relatedness also play a role in the enhanced memory for emotional stimuli, especially for verbal stimuli. Research has not addressed the contributions of emotional arousal, indexed by self-report and autonomic measures, and semantic relatedness on memory performance. Twenty young adults (10 women) were presented neutral-unrelated words, school-related words, moderately arousing emotional words, and highly arousing taboo words while heart rate and skin conductance were measured. Memory was tested with free recall and recognition tests. Results showed that taboo words, which were both semantically related and high arousal were remembered best. School-related words, which were high on semantic relatedness but low on arousal, were remembered better than the moderately arousing emotional words and semantically unrelated neutral words. Psychophysiological responses showed that within the moderately arousing emotional and neutral word groups, those words eliciting greater autonomic activity were better remembered than words that did not elicit such activity. These results demonstrate additive effects of semantic relatedness and emotional arousal on memory. Relatedness confers an advantage to memory (as in the school-words), but the combination of relatedness and arousal (as in the taboo words) results in the best memory performance.

Differential Phonological and Semantic Modulation of Neurophysiological Responses to Visual Word Recognition.

PubMed

Drakesmith, Mark; El-Deredy, Wael; Welbourne, Stephen

2015-01-01

Reading words for meaning relies on orthographic, phonological and semantic processing. The triangle model implicates a direct orthography-to-semantics pathway and a phonologically mediated orthography-to-semantics pathway, which interact with each other. The temporal evolution of processing in these routes is not well understood, although theoretical evidence predicts early phonological processing followed by interactive phonological and semantic processing. This study used electroencephalography-event-related potential (ERP) analysis and magnetoencephalography (MEG) source localisation to identify temporal markers and the corresponding neural generators of these processes in early (∼200 ms) and late (∼400 ms) neurophysiological responses to visual words, pseudowords and consonant strings. ERP showed an effect of phonology but not semantics in both time windows, although at ∼400 ms there was an effect of stimulus familiarity. Phonological processing at ~200 ms was localised to the left occipitotemporal cortex and the inferior frontal gyrus. At 400 ms, there was continued phonological processing in the inferior frontal gyrus and additional semantic processing in the anterior temporal cortex. There was also an area in the left temporoparietal junction which was implicated in both phonological and semantic processing. In ERP, the semantic response at ∼400 ms appeared to be masked by concurrent processes relating to familiarity, while MEG successfully differentiated these processes. The results support the prediction of early phonological processing followed by an interaction of phonological and semantic processing during word recognition. Neuroanatomical loci of these processes are consistent with previous neuropsychological and functional magnetic resonance imaging studies. The results also have implications for the classical interpretation of N400-like responses as markers for semantic processing.

Age-related reduction of adaptive brain response during semantic integration is associated with gray matter reduction.

PubMed

Zhu, Zude; Yang, Fengjun; Li, Dongning; Zhou, Lianjun; Liu, Ying; Zhang, Ying; Chen, Xuezhi

2017-01-01

While aging is associated with increased knowledge, it is also associated with decreased semantic integration. To investigate brain activation changes during semantic integration, a sample of forty-eight 25-75 year-old adults read sentences with high cloze (HC) and low cloze (LC) probability while functional magnetic resonance imaging was conducted. Significant age-related reduction of cloze effect (LC vs. HC) was found in several regions, especially the left middle frontal gyrus (MFG) and right inferior frontal gyrus (IFG), which play an important role in semantic integration. Moreover, when accounting for global gray matter volume reduction, the age-cloze correlation in the left MFG and right IFG was absent. The results suggest that brain structural atrophy may disrupt brain response in aging brains, which then show less brain engagement in semantic integration.

[Picture naming and memory in children: phonological and semantic effects].

PubMed

Scheuer, Claudia Ines; Stivanin, Luciene; Mangilli, Laura Davidson

2004-01-01

[corrected] The relation between picture naming and the short and long term memories. to verify the ability of picture naming based on phonological and semantic queues, relating it to memory. 80 pictures selected from a set of 400 (Cycowicz et al., 1997) were presented to 80 children with ages ranging from 3 to 6 years. Responses were classified in semantic and phonologic errors and number of correct answers. The effect of the articulatory complexity was significant and the effect of the semantic complexity was not significant. Naming is the result of memory activation which is organized in categories, physical properties and function; phonologic effects do interfere in the activity of naming, whereas the semantic effects reflect that the long term memory is organized in categories which are dependant of the context and of the development.

Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

PubMed

Mrabet, Yassine; Kilicoglu, Halil; Roberts, Kirk; Demner-Fushman, Dina

2016-01-01

Determining the main topics in consumer health questions is a crucial step in their processing as it allows narrowing the search space to a specific semantic context. In this paper we propose a topic recognition approach based on biomedical and open-domain knowledge bases. In the first step of our method, we recognize named entities in consumer health questions using an unsupervised method that relies on a biomedical knowledge base, UMLS, and an open-domain knowledge base, DBpedia. In the next step, we cast topic recognition as a binary classification problem of deciding whether a named entity is the question topic or not. We evaluated our approach on a dataset from the National Library of Medicine (NLM), introduced in this paper, and another from the Genetic and Rare Disease Information Center (GARD). The combination of knowledge bases outperformed the results obtained by individual knowledge bases by up to 16.5% F1 and achieved state-of-the-art performance. Our results demonstrate that combining open-domain knowledge bases with biomedical knowledge bases can lead to a substantial improvement in understanding user-generated health content.

Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information

PubMed Central

Wilbur, W. John

2012-01-01

The Comparative Toxicogenomics Database (CTD) contains manually curated literature that describes chemical–gene interactions, chemical–disease relationships and gene–disease relationships. Finding articles containing this information is the first and an important step to assist manual curation efficiency. However, the complex nature of named entities and their relationships make it challenging to choose relevant articles. In this article, we introduce a machine learning framework for prioritizing CTD-relevant articles based on our prior system for the protein–protein interaction article classification task in BioCreative III. To address new challenges in the CTD task, we explore a new entity identification method for genes, chemicals and diseases. In addition, latent topics are analyzed and used as a feature type to overcome the small size of the training set. Applied to the BioCreative 2012 Triage dataset, our method achieved 0.8030 mean average precision (MAP) in the official runs, resulting in the top MAP system among participants. Integrated with PubTator, a Web interface for annotating biomedical literature, the proposed system also received a positive review from the CTD curation team. PMID:23160415

Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information.

PubMed

Kim, Sun; Kim, Won; Wei, Chih-Hsuan; Lu, Zhiyong; Wilbur, W John

2012-01-01

The Comparative Toxicogenomics Database (CTD) contains manually curated literature that describes chemical-gene interactions, chemical-disease relationships and gene-disease relationships. Finding articles containing this information is the first and an important step to assist manual curation efficiency. However, the complex nature of named entities and their relationships make it challenging to choose relevant articles. In this article, we introduce a machine learning framework for prioritizing CTD-relevant articles based on our prior system for the protein-protein interaction article classification task in BioCreative III. To address new challenges in the CTD task, we explore a new entity identification method for genes, chemicals and diseases. In addition, latent topics are analyzed and used as a feature type to overcome the small size of the training set. Applied to the BioCreative 2012 Triage dataset, our method achieved 0.8030 mean average precision (MAP) in the official runs, resulting in the top MAP system among participants. Integrated with PubTator, a Web interface for annotating biomedical literature, the proposed system also received a positive review from the CTD curation team.

Semi-Automated Annotation of Biobank Data Using Standard Medical Terminologies in a Graph Database.

PubMed

Hofer, Philipp; Neururer, Sabrina; Goebel, Georg

2016-01-01

Data describing biobank resources frequently contains unstructured free-text information or insufficient coding standards. (Bio-) medical ontologies like Orphanet Rare Diseases Ontology (ORDO) or the Human Disease Ontology (DOID) provide a high number of concepts, synonyms and entity relationship properties. Such standard terminologies increase quality and granularity of input data by adding comprehensive semantic background knowledge from validated entity relationships. Moreover, cross-references between terminology concepts facilitate data integration across databases using different coding standards. In order to encourage the use of standard terminologies, our aim is to identify and link relevant concepts with free-text diagnosis inputs within a biobank registry. Relevant concepts are selected automatically by lexical matching and SPARQL queries against a RDF triplestore. To ensure correctness of annotations, proposed concepts have to be confirmed by medical data administration experts before they are entered into the registry database. Relevant (bio-) medical terminologies describing diseases and phenotypes were identified and stored in a graph database which was tied to a local biobank registry. Concept recommendations during data input trigger a structured description of medical data and facilitate data linkage between heterogeneous systems.

Chain-Wise Generalization of Road Networks Using Model Selection

NASA Astrophysics Data System (ADS)

Bulatov, D.; Wenzel, S.; Häufel, G.; Meidow, J.

2017-05-01

Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.

Localising semantic and syntactic processing in spoken and written language comprehension: an Activation Likelihood Estimation meta-analysis.

PubMed

Rodd, Jennifer M; Vitello, Sylvia; Woollams, Anna M; Adank, Patti

2015-02-01

We conducted an Activation Likelihood Estimation (ALE) meta-analysis to identify brain regions that are recruited by linguistic stimuli requiring relatively demanding semantic or syntactic processing. We included 54 functional MRI studies that explicitly varied the semantic or syntactic processing load, while holding constant demands on earlier stages of processing. We included studies that introduced a syntactic/semantic ambiguity or anomaly, used a priming manipulation that specifically reduced the load on semantic/syntactic processing, or varied the level of syntactic complexity. The results confirmed the critical role of the posterior left Inferior Frontal Gyrus (LIFG) in semantic and syntactic processing. These results challenge models of sentence comprehension highlighting the role of anterior LIFG for semantic processing. In addition, the results emphasise the posterior (but not anterior) temporal lobe for both semantic and syntactic processing. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.

The semantic distance task: Quantifying semantic distance with semantic network path length.

PubMed

Kenett, Yoed N; Levi, Effi; Anaki, David; Faust, Miriam

2017-09-01

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We propose a novel approach to computing semantic distance, based on network science methodology. Path length in a semantic network represents the amount of steps needed to traverse from 1 word in the network to the other. We examine whether path length can be used as a measure of semantic distance, by investigating how path length affect performance in a semantic relatedness judgment task and recall from memory. Our results show a differential effect on performance: Up to 4 steps separating between word-pairs, participants exhibit an increase in reaction time (RT) and decrease in the percentage of word-pairs judged as related. From 4 steps onward, participants exhibit a significant decrease in RT and the word-pairs are dominantly judged as unrelated. Furthermore, we show that as path length between word-pairs increases, success in free- and cued-recall decreases. Finally, we demonstrate how our measure outperforms computational methods measuring semantic distance (LSA and positive pointwise mutual information) in predicting participants RT and subjective judgments of semantic strength. Thus, we provide a computational alternative to computing semantic distance. Furthermore, this approach addresses key issues in cognitive theory, namely the breadth of the spreading activation process and the effect of semantic distance on memory retrieval. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The word processing deficit in semantic dementia: all categories are equal, but some categories are more equal than others.

PubMed

Pulvermüller, Friedemann; Cooper-Pye, Elisa; Dine, Clare; Hauk, Olaf; Nestor, Peter J; Patterson, Karalyn

2010-09-01

It has been claimed that semantic dementia (SD), the temporal variant of fronto-temporal dementia, is characterized by an across-the-board deficit affecting all types of conceptual knowledge. We here confirm this generalized deficit but also report differential degrees of impairment in processing specific semantic word categories in a case series of SD patients (N = 11). Within the domain of words with strong visually grounded meaning, the patients' lexical decision accuracy was more impaired for color-related than for form-related words. Likewise, within the domain of action verbs, the patients' performance was worse for words referring to face movements and speech acts than for words semantically linked to actions performed with the hand and arm. Psycholinguistic properties were matched between the stimulus groups entering these contrasts; an explanation for the differential degrees of impairment must therefore involve semantic features of the words in the different conditions. Furthermore, this specific pattern of deficits cannot be captured by classic category distinctions such as nouns versus verbs or living versus nonliving things. Evidence from previous neuroimaging research indicates that color- and face/speech-related words, respectively, draw most heavily on anterior-temporal and inferior-frontal areas, the structures most affected in SD. Our account combines (a) the notion of an anterior-temporal amodal semantic "hub" to explain the profound across-the-board deficit in SD word processing, with (b) a semantic topography model of category-specific circuits whose cortical distributions reflect semantic features of the words and concepts represented.

Fine-grained semantic categorization across the abstract and concrete domains.

PubMed

Ghio, Marta; Vaghi, Matilde Maria Serena; Tettamanti, Marco

2013-01-01

A consolidated approach to the study of the mental representation of word meanings has consisted in contrasting different domains of knowledge, broadly reflecting the abstract-concrete dichotomy. More fine-grained semantic distinctions have emerged in neuropsychological and cognitive neuroscience work, reflecting semantic category specificity, but almost exclusively within the concrete domain. Theoretical advances, particularly within the area of embodied cognition, have more recently put forward the idea that distributed neural representations tied to the kinds of experience maintained with the concepts' referents might distinguish conceptual meanings with a high degree of specificity, including those within the abstract domain. Here we report the results of two psycholinguistic rating studies incorporating such theoretical advances with two main objectives: first, to provide empirical evidence of fine-grained distinctions within both the abstract and the concrete semantic domains with respect to relevant psycholinguistic dimensions; second, to develop a carefully controlled linguistic stimulus set that may be used for auditory as well as visual neuroimaging studies focusing on the parametrization of the semantic space beyond the abstract-concrete dichotomy. Ninety-six participants rated a set of 210 sentences across pre-selected concrete (mouth, hand, or leg action-related) and abstract (mental state-, emotion-, mathematics-related) categories, with respect either to different semantic domain-related scales (rating study 1), or to concreteness, familiarity, and context availability (rating study 2). Inferential statistics and correspondence analyses highlighted distinguishing semantic and psycholinguistic traits for each of the pre-selected categories, indicating that a simple abstract-concrete dichotomy is not sufficient to account for the entire semantic variability within either domains.

Fine-Grained Semantic Categorization across the Abstract and Concrete Domains

PubMed Central

Tettamanti, Marco

2013-01-01

A consolidated approach to the study of the mental representation of word meanings has consisted in contrasting different domains of knowledge, broadly reflecting the abstract-concrete dichotomy. More fine-grained semantic distinctions have emerged in neuropsychological and cognitive neuroscience work, reflecting semantic category specificity, but almost exclusively within the concrete domain. Theoretical advances, particularly within the area of embodied cognition, have more recently put forward the idea that distributed neural representations tied to the kinds of experience maintained with the concepts' referents might distinguish conceptual meanings with a high degree of specificity, including those within the abstract domain. Here we report the results of two psycholinguistic rating studies incorporating such theoretical advances with two main objectives: first, to provide empirical evidence of fine-grained distinctions within both the abstract and the concrete semantic domains with respect to relevant psycholinguistic dimensions; second, to develop a carefully controlled linguistic stimulus set that may be used for auditory as well as visual neuroimaging studies focusing on the parametrization of the semantic space beyond the abstract-concrete dichotomy. Ninety-six participants rated a set of 210 sentences across pre-selected concrete (mouth, hand, or leg action-related) and abstract (mental state-, emotion-, mathematics-related) categories, with respect either to different semantic domain-related scales (rating study 1), or to concreteness, familiarity, and context availability (rating study 2). Inferential statistics and correspondence analyses highlighted distinguishing semantic and psycholinguistic traits for each of the pre-selected categories, indicating that a simple abstract-concrete dichotomy is not sufficient to account for the entire semantic variability within either domains. PMID:23825625

SKIMMR: facilitating knowledge discovery in life sciences by machine-aided skim reading

PubMed Central

Burns, Gully A.P.C.

2014-01-01

Background. Unlike full reading, ‘skim-reading’ involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences. Methods. To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vector-space model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used to construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Last but not least, we describe a methodology for automated experimental evaluation of the presented approach. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. Results. We provide a web-based prototype (called ‘SKIMMR’) that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are the most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. To illustrate the practical applicability of SKIMMR, we present examples of its use in the domains of Spinal Muscular Atrophy and Parkinson’s Disease. Finally, we report on the results of experimental evaluation using the two domains and one additional dataset based on the TREC challenge. The results show how the presented method for machine-aided skim reading outperforms tools like PubMed regarding focused browsing and informativeness of the browsing context. PMID:25097821

Using semantic memory to boost 'episodic' recall in a case of developmental amnesia.

PubMed

Brandt, Karen R; Gardiner, John M; Vargha-Khadem, Faraneh; Baddeley, Alan D; Mishkin, Mortimer

2006-07-17

We report two experiments that investigated factors that might boost 'episodic' recall for Jon, a developmental amnesic whose episodic memory is gravely impaired but whose semantic memory seems relatively normal. Experiment 1 showed that Jon's recall improved following a semantic study task compared with a non-semantic study task, as well as following four repeated study trials compared with only one. Experiment 2 additionally revealed that Jon's recall improved after acting compared with reading action phrases at study, but only if the phrases were well integrated semantically. The results provide some support for the hypothesis that Jon's 'episodic' recall depends on the extent to which he is able to retrieve events using semantic memory.

Orthographic effects in spoken word recognition: Evidence from Chinese.

PubMed

Qu, Qingqing; Damian, Markus F

2017-06-01

Extensive evidence from alphabetic languages demonstrates a role of orthography in the processing of spoken words. Because alphabetic systems explicitly code speech sounds, such effects are perhaps not surprising. However, it is less clear whether orthographic codes are involuntarily accessed from spoken words in languages with non-alphabetic systems, in which the sound-spelling correspondence is largely arbitrary. We investigated the role of orthography via a semantic relatedness judgment task: native Mandarin speakers judged whether or not spoken word pairs were related in meaning. Word pairs were either semantically related, orthographically related, or unrelated. Results showed that relatedness judgments were made faster for word pairs that were semantically related than for unrelated word pairs. Critically, orthographic overlap on semantically unrelated word pairs induced a significant increase in response latencies. These findings indicate that orthographic information is involuntarily accessed in spoken-word recognition, even in a non-alphabetic language such as Chinese.

Consumers' Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites.

PubMed

Park, Min Sook; He, Zhe; Chen, Zhiwei; Oh, Sanghee; Bian, Jiang

2016-11-24

The widely known terminology gap between health professionals and health consumers hinders effective information seeking for consumers. The aim of this study was to better understand consumers' usage of medical concepts by evaluating the coverage of concepts and semantic types of the Unified Medical Language System (UMLS) on diabetes-related postings in 2 types of social media: blogs and social question and answer (Q&A). We collected 2 types of social media data: (1) a total of 3711 blogs tagged with "diabetes" on Tumblr posted between February and October 2015; and (2) a total of 58,422 questions and associated answers posted between 2009 and 2014 in the diabetes category of Yahoo! Answers. We analyzed the datasets using a widely adopted biomedical text processing framework Apache cTAKES and its extension YTEX. First, we applied the named entity recognition (NER) method implemented in YTEX to identify UMLS concepts in the datasets. We then analyzed the coverage and the popularity of concepts in the UMLS source vocabularies across the 2 datasets (ie, blogs and social Q&A). Further, we conducted a concept-level comparative coverage analysis between SNOMED Clinical Terms (SNOMED CT) and Open-Access Collaborative Consumer Health Vocabulary (OAC CHV)-the top 2 UMLS source vocabularies that have the most coverage on our datasets. We also analyzed the UMLS semantic types that were frequently observed in our datasets. We identified 2415 UMLS concepts from blog postings, 6452 UMLS concepts from social Q&A questions, and 10,378 UMLS concepts from the answers. The medical concepts identified in the blogs can be covered by 56 source vocabularies in the UMLS, while those in questions and answers can be covered by 58 source vocabularies. SNOMED CT was the dominant vocabulary in terms of coverage across all the datasets, ranging from 84.9% to 95.9%. It was followed by OAC CHV (between 73.5% and 80.0%) and Metathesaurus Names (MTH) (between 55.7% and 73.5%). All of the social media datasets shared frequent semantic types such as "Amino Acid, Peptide, or Protein," "Body Part, Organ, or Organ Component," and "Disease or Syndrome." Although the 3 social media datasets vary greatly in size, they exhibited similar conceptual coverage among UMLS source vocabularies and the identified concepts showed similar semantic type distributions. As such, concepts that are both frequently used by consumers and also found in professional vocabularies such as SNOMED CT can be suggested to OAC CHV to improve its coverage. ©Min Sook Park, Zhe He, Zhiwei Chen, Sanghee Oh, Jiang Bian. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 24.11.2016.

Consumers’ Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites

PubMed Central

Chen, Zhiwei; Oh, Sanghee; Bian, Jiang

2016-01-01

Background The widely known terminology gap between health professionals and health consumers hinders effective information seeking for consumers. Objective The aim of this study was to better understand consumers’ usage of medical concepts by evaluating the coverage of concepts and semantic types of the Unified Medical Language System (UMLS) on diabetes-related postings in 2 types of social media: blogs and social question and answer (Q&A). Methods We collected 2 types of social media data: (1) a total of 3711 blogs tagged with “diabetes” on Tumblr posted between February and October 2015; and (2) a total of 58,422 questions and associated answers posted between 2009 and 2014 in the diabetes category of Yahoo! Answers. We analyzed the datasets using a widely adopted biomedical text processing framework Apache cTAKES and its extension YTEX. First, we applied the named entity recognition (NER) method implemented in YTEX to identify UMLS concepts in the datasets. We then analyzed the coverage and the popularity of concepts in the UMLS source vocabularies across the 2 datasets (ie, blogs and social Q&A). Further, we conducted a concept-level comparative coverage analysis between SNOMED Clinical Terms (SNOMED CT) and Open-Access Collaborative Consumer Health Vocabulary (OAC CHV)—the top 2 UMLS source vocabularies that have the most coverage on our datasets. We also analyzed the UMLS semantic types that were frequently observed in our datasets. Results We identified 2415 UMLS concepts from blog postings, 6452 UMLS concepts from social Q&A questions, and 10,378 UMLS concepts from the answers. The medical concepts identified in the blogs can be covered by 56 source vocabularies in the UMLS, while those in questions and answers can be covered by 58 source vocabularies. SNOMED CT was the dominant vocabulary in terms of coverage across all the datasets, ranging from 84.9% to 95.9%. It was followed by OAC CHV (between 73.5% and 80.0%) and Metathesaurus Names (MTH) (between 55.7% and 73.5%). All of the social media datasets shared frequent semantic types such as “Amino Acid, Peptide, or Protein,” “Body Part, Organ, or Organ Component,” and “Disease or Syndrome.” Conclusions Although the 3 social media datasets vary greatly in size, they exhibited similar conceptual coverage among UMLS source vocabularies and the identified concepts showed similar semantic type distributions. As such, concepts that are both frequently used by consumers and also found in professional vocabularies such as SNOMED CT can be suggested to OAC CHV to improve its coverage. PMID:27884812

A Generic Evaluation Model for Semantic Web Services

NASA Astrophysics Data System (ADS)

Shafiq, Omair

Semantic Web Services research has gained momentum over the last few Years and by now several realizations exist. They are being used in a number of industrial use-cases. Soon software developers will be expected to use this infrastructure to build their B2B applications requiring dynamic integration. However, there is still a lack of guidelines for the evaluation of tools developed to realize Semantic Web Services and applications built on top of them. In normal software engineering practice such guidelines can already be found for traditional component-based systems. Also some efforts are being made to build performance models for servicebased systems. Drawing on these related efforts in component-oriented and servicebased systems, we identified the need for a generic evaluation model for Semantic Web Services applicable to any realization. The generic evaluation model will help users and customers to orient their systems and solutions towards using Semantic Web Services. In this chapter, we have presented the requirements for the generic evaluation model for Semantic Web Services and further discussed the initial steps that we took to sketch such a model. Finally, we discuss related activities for evaluating semantic technologies.

Altered brain response for semantic knowledge in Alzheimer's disease.

PubMed

Wierenga, Christina E; Stricker, Nikki H; McCauley, Ashley; Simmons, Alan; Jak, Amy J; Chang, Yu-Ling; Nation, Daniel A; Bangen, Katherine J; Salmon, David P; Bondi, Mark W

2011-02-01

Word retrieval deficits are common in Alzheimer's disease (AD) and are thought to reflect a degradation of semantic memory. Yet, the nature of semantic deterioration in AD and the underlying neural correlates of these semantic memory changes remain largely unknown. We examined the semantic memory impairment in AD by investigating the neural correlates of category knowledge (e.g., living vs. nonliving) and featural processing (global vs. local visual information). During event-related fMRI, 10 adults diagnosed with mild AD and 22 cognitively normal (CN) older adults named aloud items from three categories for which processing of specific visual features has previously been dissociated from categorical features. Results showed widespread group differences in the categorical representation of semantic knowledge in several language-related brain areas. For example, the right inferior frontal gyrus showed selective brain response for nonliving items in the CN group but living items in the AD group. Additionally, the AD group showed increased brain response for word retrieval irrespective of category in Broca's homologue in the right hemisphere and rostral cingulate cortex bilaterally, which suggests greater recruitment of frontally mediated neural compensatory mechanisms in the face of semantic alteration. Copyright © 2010 Elsevier Ltd. All rights reserved.

The effect of semantic transparency on the processing of morphologically derived words: Evidence from decision latencies and event-related potentials.

PubMed

Jared, Debra; Jouravlev, Olessia; Joanisse, Marc F

2017-03-01

Decomposition theories of morphological processing in visual word recognition posit an early morpho-orthographic parser that is blind to semantic information, whereas parallel distributed processing (PDP) theories assume that the transparency of orthographic-semantic relationships influences processing from the beginning. To test these alternatives, the performance of participants on transparent (foolish), quasi-transparent (bookish), opaque (vanish), and orthographic control words (bucket) was examined in a series of 5 experiments. In Experiments 1-3 variants of a masked priming lexical-decision task were used; Experiment 4 used a masked priming semantic decision task, and Experiment 5 used a single-word (nonpriming) semantic decision task with a color-boundary manipulation. In addition to the behavioral data, event-related potential (ERP) data were collected in Experiments 1, 2, 4, and 5. Across all experiments, we observed a graded effect of semantic transparency in behavioral and ERP data, with the largest effect for semantically transparent words, the next largest for quasi-transparent words, and the smallest for opaque words. The results are discussed in terms of decomposition versus PDP approaches to morphological processing. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Identification of environmental sounds and melodies in syndromes of anterior temporal lobe degeneration.

PubMed

Golden, Hannah L; Downey, Laura E; Fletcher, Philip D; Mahoney, Colin J; Schott, Jonathan M; Mummery, Catherine J; Crutch, Sebastian J; Warren, Jason D

2015-05-15

Recognition of nonverbal sounds in semantic dementia and other syndromes of anterior temporal lobe degeneration may determine clinical symptoms and help to define phenotypic profiles. However, nonverbal auditory semantic function has not been widely studied in these syndromes. Here we investigated semantic processing in two key nonverbal auditory domains - environmental sounds and melodies - in patients with semantic dementia (SD group; n=9) and in patients with anterior temporal lobe atrophy presenting with behavioural decline (TL group; n=7, including four cases with MAPT mutations) in relation to healthy older controls (n=20). We assessed auditory semantic performance in each domain using novel, uniform within-modality neuropsychological procedures that determined sound identification based on semantic classification of sound pairs. Both the SD and TL groups showed comparable overall impairments of environmental sound and melody identification; individual patients generally showed superior identification of environmental sounds than melodies, however relative sparing of melody over environmental sound identification also occurred in both groups. Our findings suggest that nonverbal auditory semantic impairment is a common feature of neurodegenerative syndromes with anterior temporal lobe atrophy. However, the profile of auditory domain involvement varies substantially between individuals. Copyright © 2015. Published by Elsevier B.V.

Hemispheric asymmetries in discourse processing: evidence from false memories for lists and texts.

PubMed

Ben-Artzi, Elisheva; Faust, Miriam; Moeller, Edna

2009-01-01

Previous research suggests that the right hemisphere (RH) may contribute uniquely to discourse and text processing by activating and maintaining a wide range of meanings, including more distantly related meanings. The present study used the word-lists false memory paradigm [Roediger, H. L., III, & McDermott, K. B. (1995). Creating false memories: Remembering words not presented in lists. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 803-814.] to examine the hypothesis that difference between the two cerebral hemispheres in discourse processing may be due, at least partly, to memory representations for implicit text-related semantic information. Specifically, we tested the susceptibility of the left hemisphere (LH) and RH to unpresented target words following the presentation of semantically related words appearing in either word lists or short texts. Findings showed that the RH produced more false alarms than the LH for unpresented target words following either word lists or texts. These findings reveal hemispheric differences in memory for semantically related information and suggest that RH advantage in long-term maintenance of a wide range of text-related word meanings may be one aspect of its unique contribution to the construction of a discourse model. The results support the RH coarse semantic coding theory [Beeman, M. (1998). Coarse semantic coding and discourse comprehension. In M. Beeman & C. Chiarello (Eds.), Right hemisphere language comprehension: Perspectives from cognitive neuroscience (pp. 255-284). Mahwah, NJ: Erlbaum.] and suggest that hemispheric differences in semantic processing during language comprehension extend also to verbal memory.

Preservation of Person-Specific Semantic Knowledge in Semantic Dementia: Does Direct Personal Experience Have a Specific Role?

PubMed Central

Péron, Julie A.; Piolino, Pascale; Moal-Boursiquot, Sandrine Le; Biseul, Isabelle; Leray, Emmanuelle; Bon, Laetitia; Desgranges, Béatrice; Eustache, Francis; Belliard, Serge

2015-01-01

Semantic dementia patients seem to have better knowledge of information linked to the self. More specifically, despite having severe semantic impairment, these patients show that they have more general information about the people they know personally by direct experience than they do about other individuals they know indirectly. However, the role of direct personal experience remains debated because of confounding factors such as frequency, recency of exposure, and affective relevance. We performed an exploratory study comparing the performance of five semantic dementia patients with that of 10 matched healthy controls on the recognition (familiarity judgment) and identification (biographic information recall) of personally familiar names vs. famous names. As expected, intergroup comparisons indicated a semantic breakdown in semantic dementia patients as compared with healthy controls. Moreover, unlike healthy controls, the semantic dementia patients recognized and identified personally familiar names better than they did famous names. This pattern of results suggests that direct personal experience indeed plays a specific role in the relative preservation of person-specific semantic meaning in semantic dementia. We discuss the role of direct personal experience on the preservation of semantic knowledge and the potential neurophysiological mechanisms underlying these processes. PMID:26635578

Episodic and Semantic Aspects of Memory for Prose.

ERIC Educational Resources Information Center

Dooling, D. James

This report describes research on Bartlett's theory of constructive memory. In experiment one, schematic retention is related to Tulving's distinction between episodic and semantic memory. With the passage of time, memory for prose reflects decreasing output from episodic memory and increasing output from semantic memory. In experiment two,…

Levels of Processing and the Cue-Dependent Nature of Recollection

ERIC Educational Resources Information Center

Mulligan, Neil W.; Picklesimer, Milton

2012-01-01

Dual-process models differentiate between two bases of memory, recollection and familiarity. It is routinely claimed that deeper, semantic encoding enhances recollection relative to shallow, non-semantic encoding, and that recollection is largely a product of semantic, elaborative rehearsal. The present experiments show that this is not always the…

Activation of Phonological and Semantic Codes in Toddlers

ERIC Educational Resources Information Center

Mani, Nivedita; Durrant, Samantha; Floccia, Caroline

2012-01-01

What are the processes underlying word recognition in the toddler lexicon? Work with adults suggests that, by 5-years of age, hearing a word leads to cascaded activation of other phonologically, semantically and phono-semantically related words (Huang & Snedeker, 2010; Marslen-Wilson & Zwitserlood, 1989). Given substantial differences in…

Semantic and Phonological Encoding in Adults Who Stutter: Silent Responses to Pictorial Stimuli

ERIC Educational Resources Information Center

Vincent, Irena

2017-01-01

Purpose: Research on language planning in adult stuttering is relatively sparse and offers diverging arguments about a potential causative relationship between semantic and phonological encoding and fluency breakdowns. This study further investigated semantic and phonological encoding efficiency in adults who stutter (AWS) by means of silent…

Role of Importance and Distinctiveness of Semantic Features in People with Aphasia: A Replication Study

ERIC Educational Resources Information Center

Mason-Baughman, Mary Beth; Wallace, Sarah E.

2014-01-01

Previous studies suggest that people with aphasia have incomplete lexical-semantic representations with decreased low-importance distinctive (LID) feature knowledge. In addition, decreased LID feature knowledge correlates with ability to discriminate among semantically related words. The current study seeks to replicate and extend previous…

Hemispheric Differences in the Recruitment of Semantic Processing Mechanisms

ERIC Educational Resources Information Center

Kandhadai, Padmapriya; Federmeier, Kara D.

2010-01-01

This study examined how the two cerebral hemispheres recruit semantic processing mechanisms by combining event-related potential measures and visual half-field methods in a word priming paradigm in which semantic strength and predictability were manipulated using lexically associated word pairs. Activation patterns on the late positive complex…

Semantics Does Not Need a Processing License from Syntax in Reading Chinese

ERIC Educational Resources Information Center

Zhang, Yaxu; Yu, Jing; Boland, Julie E.

2010-01-01

Two event-related brain potential experiments were conducted to investigate whether there is a functional primacy of syntactic structure building over semantic processes during Chinese sentence reading. In both experiments, we found that semantic interpretation proceeded despite the impossibility of a well-formed syntactic analysis. In Experiment…

Towards a Theory of Semantic Communication (Extended Technical Report)

DTIC Science & Technology

2011-03-01

counting models of a sentence, when interpretations have different probabilities, what matters is the total probability of models of the sentence, not...of classic logics still hold in the LP semantics, e.g., De Morgan’s laws. However, modus pollens does hold in the LP semantics 10 F. Relation to

The Influence of Semantic Property and Grammatical Class on Semantic Selection

ERIC Educational Resources Information Center

Yang, Fan-pei Gloria; Khodaparast, Navid; Bradley, Kailyn; Fang, Min-Chieh; Bernstein, Ari; Krawczyk, Daniel C.

2013-01-01

Research to-date has not successfully demonstrated consistent neural distinctions for different types of ambiguity or explored the effect of grammatical class on semantic selection. We conducted a relatedness judgment task using event-related functional magnetic resonance imaging (fMRI) to further explore these topics. Participants judged…

The Role of Semantic Features in Verb Processing

ERIC Educational Resources Information Center

Bonnotte, Isabelle

2008-01-01

The present study examined the general hypothesis that, as for nouns, stable representations of semantic knowledge relative to situations expressed by verbs are available and accessible in long term memory in normal people. Regular associations between verbs and past tenses in French adults allowed to abstract two superordinate semantic features…

The Relevance Aura of Bibliographic Records.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1997-01-01

Analyzes relevance assessments of topical descriptors for bibliographic records for two dimensions: (1) a vertical conceptual hierarchy of broad to narrow descriptors, and (2) a horizontal linkage of related terms. The data were analyzed for a semantic distance and semantic direction effect as postulated by the Semantic Distance Model. (Author/LRW)

Centrality-based Selection of Semantic Resources for Geosciences

NASA Astrophysics Data System (ADS)

Cerba, Otakar; Jedlicka, Karel

2017-04-01

Semantical questions intervene almost in all disciplines dealing with geographic data and information, because relevant semantics is crucial for any way of communication and interaction among humans as well as among machines. But the existence of such a large number of different semantic resources (such as various thesauri, controlled vocabularies, knowledge bases or ontologies) makes the process of semantics implementation much more difficult and complicates the use of the advantages of semantics. This is because in many cases users are not able to find the most suitable resource for their purposes. The research presented in this paper introduces a methodology consisting of an analysis of identical relations in Linked Data space, which covers a majority of semantic resources, to find a suitable resource of semantic information. Identical links interconnect representations of an object or a concept in various semantic resources. Therefore this type of relations is considered to be crucial from the view of Linked Data, because these links provide new additional information, including various views on one concept based on different cultural or regional aspects (so-called social role of Linked Data). For these reasons it is possible to declare that one reasonable criterion for feasible semantic resources for almost all domains, including geosciences, is their position in a network of interconnected semantic resources and level of linking to other knowledge bases and similar products. The presented methodology is based on searching of mutual connections between various instances of one concept using "follow your nose" approach. The extracted data on interconnections between semantic resources are arranged to directed graphs and processed by various metrics patterned on centrality computing (degree, closeness or betweenness centrality). Semantic resources recommended by the research could be used for providing semantically described keywords for metadata records or as names of items in data models. Such an approach enables much more efficient data harmonization, integration, sharing and exploitation. * * * * This publication was supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports. This publication was supported by project Data-Driven Bioeconomy (DataBio) from the ICT-15-2016-2017, Big Data PPP call.

Remote semantic memory is impoverished in hippocampal amnesia

PubMed Central

Klooster, Nathaniel B.; Duff, Melissa C.

2015-01-01

The necessity of the hippocampus for acquiring new semantic concepts is a topic of considerable debate. However, it is generally accepted that any role the hippocampus plays in semantic memory is time limited and that previously acquired information becomes independent of the hippocampus over time. This view, along with intact naming and word-definition matching performance in amnesia, has led to the notion that remote semantic memory is intact in patients with hippocampal amnesia. Motivated by perspectives of word learning as a protracted process where additional features and senses of a word are added over time, and by recent discoveries about the time course of hippocampal contributions to on-line relational processing, reconsolidation, and the flexible integration of information, we revisit the notion that remote semantic memory is intact in amnesia. Using measures of semantic richness and vocabulary depth from psycholinguistics and first and second language-learning studies, we examined how much information is associated with previously acquired, highly familiar words in a group of patients with bilateral hippocampal damage and amnesia. Relative to healthy demographically matched comparison participants and a group of brain-damaged comparison participants, the patients with hippocampal amnesia performed significantly worse on both productive and receptive measures of vocabulary depth and semantic richness. These findings suggest that remote semantic memory is impoverished in patients with hippocampal amnesia and that the hippocampus may play a role in the maintenance and updating of semantic memory beyond its initial acquisition. PMID:26474741