AlzPharm: integration of neurodegeneration data using RDF

PubMed Central

Lam, Hugo YK; Marenco, Luis; Clark, Tim; Gao, Yong; Kinoshita, June; Shepherd, Gordon; Miller, Perry; Wu, Elizabeth; Wong, Gwendolyn T; Liu, Nian; Crasto, Chiquito; Morse, Thomas; Stephens, Susie; Cheung, Kei-Hoi

2007-01-01

Background Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields. PMID:17493287

The Question Shapes the Answer: The Neural Correlates of Task Differences Reveal Dynamic Semantic Processing

ERIC Educational Resources Information Center

Hargreaves, Ian S.; White, Michelle; Pexman, Penny M.; Pittman, Dan; Goodyear, Brad G.

2012-01-01

Task effects in semantic processing were investigated by contrasting the neural activation associated with two semantic categorization tasks (SCT) using event-related fMRI. The two SCTs involved different decision categories: "is it an animal?" vs. "is it a concrete thing?" Participants completed both tasks and, across participants, the same core…

e-Science and biological pathway semantics

PubMed Central

Luciano, Joanne S; Stevens, Robert D

2007-01-01

Background The development of e-Science presents a major set of opportunities and challenges for the future progress of biological and life scientific research. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. Nowhere is the demand greater than in the semantic integration of these data. Semantic Web tools and technologies afford the chance to achieve this semantic integration. Since pathway knowledge is central to much of the scientific research today it is a good test-bed for semantic integration. Within the context of biological pathways, the BioPAX initiative, part of a broader movement towards the standardization and integration of life science databases, forms a necessary prerequisite for its successful application of e-Science in health care and life science research. This paper examines whether BioPAX, an effort to overcome the barrier of disparate and heterogeneous pathway data sources, addresses the needs of e-Science. Results We demonstrate how BioPAX pathway data can be used to ask and answer some useful biological questions. We find that BioPAX comes close to meeting a broad range of e-Science needs, but certain semantic weaknesses mean that these goals are missed. We make a series of recommendations for re-modeling some aspects of BioPAX to better meet these needs. Conclusion Once these semantic weaknesses are addressed, it will be possible to integrate pathway information in a manner that would be useful in e-Science. PMID:17493286

Web information retrieval based on ontology

NASA Astrophysics Data System (ADS)

Zhang, Jian

2013-03-01

The purpose of the Information Retrieval (IR) is to find a set of documents that are relevant for a specific information need of a user. Traditional Information Retrieval model commonly used in commercial search engine is based on keyword indexing system and Boolean logic queries. One big drawback of traditional information retrieval is that they typically retrieve information without an explicitly defined domain of interest to the users so that a lot of no relevance information returns to users, which burden the user to pick up useful answer from these no relevance results. In order to tackle this issue, many semantic web information retrieval models have been proposed recently. The main advantage of Semantic Web is to enhance search mechanisms with the use of Ontology's mechanisms. In this paper, we present our approach to personalize web search engine based on ontology. In addition, key techniques are also discussed in our paper. Compared to previous research, our works concentrate on the semantic similarity and the whole process including query submission and information annotation.

A deep learning approach for predicting the quality of online health expert question-answering services.

PubMed

Hu, Ze; Zhang, Zhan; Yang, Haiqin; Chen, Qing; Zuo, Decheng

2017-07-01

Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd. The important information, such as the best answer and the number of users' votes, is missing. To tackle these issues, we prepare the first HQA research data set labeled by three medical experts in 90days and formulate the problem of predicting the quality of answers in the system as a classification task. We not only incorporate the standard textual feature of answers, but also introduce a set of unique non-textual features, i.e., the popular used surface linguistic features and the novel social features, from other modalities. A multimodal deep belief network (DBN)-based learning framework is then proposed to learn the high-level hidden semantic representations of answers from both textual features and non-textual features while the learned joint representation is fed into popular classifiers to determine the quality of answers. Finally, we conduct extensive experiments to demonstrate the effectiveness of including the non-textual features and the proposed multimodal deep learning framework. Copyright © 2017 Elsevier Inc. All rights reserved.

Incremental Query Rewriting with Resolution

NASA Astrophysics Data System (ADS)

Riazanov, Alexandre; Aragão, Marcelo A. T.

We address the problem of semantic querying of relational databases (RDB) modulo knowledge bases using very expressive knowledge representation formalisms, such as full first-order logic or its various fragments. We propose to use a resolution-based first-order logic (FOL) reasoner for computing schematic answers to deductive queries, with the subsequent translation of these schematic answers to SQL queries which are evaluated using a conventional relational DBMS. We call our method incremental query rewriting, because an original semantic query is rewritten into a (potentially infinite) series of SQL queries. In this chapter, we outline the main idea of our technique - using abstractions of databases and constrained clauses for deriving schematic answers, and provide completeness and soundness proofs to justify the applicability of this technique to the case of resolution for FOL without equality. The proposed method can be directly used with regular RDBs, including legacy databases. Moreover, we propose it as a potential basis for an efficient Web-scale semantic search technology.

New Semantic Learning in Patients With Large Medial Temporal Lobe Lesions

PubMed Central

Bayley, P.J.; O'Reilly, R.C.; Curran, T.; Squire, L.R.

2008-01-01

Two patients with large lesions of the medial temporal lobe were given four tests of semantic knowledge that could only have been acquired after the onset of their amnesia. In contrast to previous studies of postmorbid semantic learning, correct answers could be based on a simple, nonspecific sense of familiarity about single words, faces, or objects. According to recent computational models (for example, Norman and O'Reilly (2003) Psychol Rev 110:611–646), this characteristic should be optimal for detecting the kind of semantic learning that might be supported directly by the neocortex. Both patients exhibited some capacity for new learning, albeit at a level substantially below control performances. Notably, the correct answers appeared to reflect declarative memory. It was not the case that the correct answers simply popped out in some automatic way in the absence of any additional knowledge about the items. Rather, the few correct choices made by the patients tended to be accompanied by additional information about the chosen items, and the available knowledge appeared to be similar qualitatively to the kind of factual knowledge that healthy individuals gradually acquire over the years. The results are consistent with the idea that neocortical structures outside the medial temporal lobe are able to support some semantic learning, albeit to a very limited extent. Alternatively, the small amount of learning detected in the present study could depend on tissue within the posterior medial temporal lobe that remains intact in both patients. PMID:18306299

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.

PubMed

El-Sappagh, Shaker; Elmogy, Mohammed; Riad, A M

2015-11-01

Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. Copyright © 2015 Elsevier B.V. All rights reserved.

Not Fade Away? Commentary to Paper "Education and The Semantic Web" ("IJAIED" Vol.14, 2004)

ERIC Educational Resources Information Center

Devedzic, Vladan

2016-01-01

If you ask me "Will Semantic Web 'ever' happen, in general, and specifically in education?", the best answer I can give you is "I don't know," but I know that today we are still far away from the hopes that I had when I wrote my paper "Education and The Semantic Web" (Devedzic 2004) more than 10 years ago. Much of the…

Management Effectiveness of a Secondary Coniferous Forest for Landscape Appreciation and Psychological Restoration

PubMed Central

Fujiwara, Akio; Saito, Haruo; Horiuchi, Masahiro

2017-01-01

We investigated the influence of forest management on landscape appreciation and psychological restoration in on-site settings by exposing respondents to an unmanaged, dense coniferous (crowding) forest and a managed (thinned) coniferous forest; we set the two experimental settings in the forests of the Fuji Iyashinomoroi Woodland Study Center. The respondents were individually exposed to both settings while sitting for 15 min and were required to answer three questionnaires to analyze the psychological restorative effects before and after the experiment (feeling (the Profile of Mood States), affect (the Positive and Negative Affect Schedule), and subjective restorativeness (the Restorative Outcome Scale). To compare landscape appreciation, they were required to answer another two questionnaires only after the experiment, for scene appreciation (the semantic differential scale) and for the restorative properties of each environment (the Perceived Restorativeness Scale). Finally, we obtained these findings: (1) the respondents evaluated each forest environment highly differently and evaluated the thinned forest setting more positively; (2) the respondents’ impressions of the two physical environments did not appear to be accurately reflected in their evaluations; (3) forest environments have potential restorative effects whether or not they are managed, but these effects can be partially enhanced by managing the forests. PMID:28718831

Semantic Services in e-Learning: An Argumentation Case Study

ERIC Educational Resources Information Center

Moreale, Emanuela; Vargas-Vera, Maria

2004-01-01

This paper outlines an e-Learning services architecture offering semantic-based services to students and tutors, in particular ways to browse and obtain information through web services. Services could include registration, authentication, tutoring systems, smart question answering for students' queries, automated marking systems and a student…

A systematic approach to advanced cockpit warning systems for air transport operations: Line pilot preferences

NASA Technical Reports Server (NTRS)

Williams, D. H.; Simpson, C. A.

1976-01-01

Line pilots (fifty captains, first officers, and flight engineers) from 8 different airlines were administered a structured questionnaire relating to future warning system design and solutions to current warning system problems. This was followed by a semantic differential to obtain a factor analysis of 18 different cockpit warning signals on scales such as informative/distracting, annoying/soothing. Half the pilots received a demonstration of the experimental text and voice synthesizer warning systems before answering the questionnaire and the semantic differential. A control group answered the questionnaire and the semantic differential first, thus providing a check for the stability of pilot preferences with and without actual exposure to experimental systems. Generally, the preference data obtained revealed much consistency and strong agreement among line pilots concerning advance cockpit warning system design.

The Development of Semantic Knowledge Systems for Realistic Goals.

ERIC Educational Resources Information Center

Goldman, Susan R.

This study investigates age differences in children's semantic expectations regarding causal relations in stories about three realistic goal situations (being friendly, getting a dog, and doing chores). Twenty children at each of three age levels (ages 6, 9, and 12) were asked to produce stories and answer probe questions about wanting and not…

Here's Looking at You: Visual Similarity Exacerbates the Moses Illusion for Semantically Similar Celebrities

ERIC Educational Resources Information Center

Davis, Danielle K.; Abrams, Lise

2016-01-01

When people read questions like "How many animals of each kind did Moses take on the ark?", many mistakenly answer "2" despite knowing that Noah sailed the ark. This "Moses illusion" occurs when names share semantic features. Two experiments examined whether shared "visual" concepts (facial features)…

Counting Strategies and Semantic Analysis as Applied to Class Inclusion. Report No. 61.

ERIC Educational Resources Information Center

Wilkinson, Alexander

This investigation examined strategic and semantic aspects of the answers given by preschool children to class inclusion problems. The Piagetian logical formalism for class inclusion was contrasted with a new, problem processing formalism in three experiments. In experiment 1, it was found that 48 nursery school subjects nearly always performed…

Crossword expertise as recognitional decision making: an artificial intelligence approach

PubMed Central

Thanasuan, Kejkaew; Mueller, Shane T.

2014-01-01

The skills required to solve crossword puzzles involve two important aspects of lexical memory: semantic information in the form of clues that indicate the meaning of the answer, and orthographic patterns that constrain the possibilities but may also provide hints to possible answers. Mueller and Thanasuan (2013) proposed a model accounting for the simple memory access processes involved in solving individual crossword clues, but expert solvers also bring additional skills and strategies to bear on solving complete puzzles. In this paper, we developed an computational model of crossword solving that incorporates strategic and other factors, and is capable of solving crossword puzzles in a human-like fashion, in order to understand the complete set of skills needed to solve a crossword puzzle. We compare our models to human expert and novice solvers to investigate how different strategic and structural factors in crossword play impact overall performance. Results reveal that expert crossword solving relies heavily on fluent semantic memory search and retrieval, which appear to allow experts to take better advantage of orthographic-route solutions, and experts employ strategies that enable them to use orthographic information. Furthermore, other processes central to traditional AI models (error correction and backtracking) appear to be of less importance for human players. PMID:25309483

Crossword expertise as recognitional decision making: an artificial intelligence approach.

PubMed

Thanasuan, Kejkaew; Mueller, Shane T

2014-01-01

THE SKILLS REQUIRED TO SOLVE CROSSWORD PUZZLES INVOLVE TWO IMPORTANT ASPECTS OF LEXICAL MEMORY: semantic information in the form of clues that indicate the meaning of the answer, and orthographic patterns that constrain the possibilities but may also provide hints to possible answers. Mueller and Thanasuan (2013) proposed a model accounting for the simple memory access processes involved in solving individual crossword clues, but expert solvers also bring additional skills and strategies to bear on solving complete puzzles. In this paper, we developed an computational model of crossword solving that incorporates strategic and other factors, and is capable of solving crossword puzzles in a human-like fashion, in order to understand the complete set of skills needed to solve a crossword puzzle. We compare our models to human expert and novice solvers to investigate how different strategic and structural factors in crossword play impact overall performance. Results reveal that expert crossword solving relies heavily on fluent semantic memory search and retrieval, which appear to allow experts to take better advantage of orthographic-route solutions, and experts employ strategies that enable them to use orthographic information. Furthermore, other processes central to traditional AI models (error correction and backtracking) appear to be of less importance for human players.

Use of Co-occurrences for Temporal Expressions Annotation

NASA Astrophysics Data System (ADS)

Craveiro, Olga; Macedo, Joaquim; Madeira, Henrique

The annotation or extraction of temporal information from text documents is becoming increasingly important in many natural language processing applications such as text summarization, information retrieval, question answering, etc.. This paper presents an original method for easy recognition of temporal expressions in text documents. The method creates semantically classified temporal patterns, using word co-occurrences obtained from training corpora and a pre-defined seed keywords set, derived from the used language temporal references. A participation on a Portuguese named entity evaluation contest showed promising effectiveness and efficiency results. This approach can be adapted to recognize other type of expressions or languages, within other contexts, by defining the suitable word sets and training corpora.

An Approach to Semantic Interoperability for Improved Capability Exchanges in Federations of Systems

ERIC Educational Resources Information Center

Moschoglou, Georgios

2013-01-01

This study seeks an affirmative answer to the question whether a knowledge-based approach to system of systems interoperation using semantic web standards and technologies can provide the centralized control of the capability for exchanging data and services lacking in a federation of systems. Given the need to collect and share real-time…

Toward a Geoscientific Semantic Web Based on How Geoscientists Talk Across Disciplines

NASA Astrophysics Data System (ADS)

Peckham, S. D.

2015-12-01

Are there terms and scientific concepts from math and science that almost all geoscientists understand? Is there a limited set of terms, patterns and language elements that geoscientists use for efficient, unambiguous communication that could be used to describe the variables that they measure, store in data sets and use as model inputs and outputs? In this talk it will be argued that the answer to both questions is "yes" by drawing attention to many such patterns and then showing how they have been used to create a rich set of naming conventions for variables called the CSDMS Standard Names. Variables, which store numerical quantities associated with specific objects, are the fundamental currency of science. They are the items that are measured and saved in data sets, which may then be read into models. They are the inputs and outputs of models and the items exchanged between coupled models. They also star in the equations that summarize our scientific knowledge. Carefully constructed, unambiguous and unique labels for commonly used variables therefore provide an attractive mechanism for automatic semantic mediation when variables are to be shared between heterogeous resources. They provide a means to automatically check for semantic equivalence so that variables can be safely shared in resource compositions. A good set of standardized variable names can serve as the hub in a hub-and-spoke solution to semantic mediation, where the "internal vocabularies" of geoscience resources (i.e. data sets and models) are mapped to and from the hub to facilitate interoperability and data sharing. When built from patterns and terms that most geoscientists are already familiar with, these standardized variable names are then "readable" by both humans and machines. Despite the importance of variables in scientific work, most of the ontological work in the geosciences is focused at a higher level that supports finding resources (e.g data sets) but not on describing the contents of those resources. The CSDMS Standard Names have matured continuously since they were first introduced over three years ago. Many recent extensions and applications of them (e.g. different science domains, different projects, new rules, ontological work) as well as their compatibility with the International System of Quantities (ISO 80000) will be discussed.

Context-Aware Adaptive Hybrid Semantic Relatedness in Biomedical Science

NASA Astrophysics Data System (ADS)

Emadzadeh, Ehsan

Text mining of biomedical literature and clinical notes is a very active field of research in biomedical science. Semantic analysis is one of the core modules for different Natural Language Processing (NLP) solutions. Methods for calculating semantic relatedness of two concepts can be very useful in solutions solving different problems such as relationship extraction, ontology creation and question / answering [1--6]. Several techniques exist in calculating semantic relatedness of two concepts. These techniques utilize different knowledge sources and corpora. So far, researchers attempted to find the best hybrid method for each domain by combining semantic relatedness techniques and data sources manually. In this work, attempts were made to eliminate the needs for manually combining semantic relatedness methods targeting any new contexts or resources through proposing an automated method, which attempted to find the best combination of semantic relatedness techniques and resources to achieve the best semantic relatedness score in every context. This may help the research community find the best hybrid method for each context considering the available algorithms and resources.

Distribution functions of probabilistic automata

NASA Technical Reports Server (NTRS)

Vatan, F.

2001-01-01

Each probabilistic automaton M over an alphabet A defines a probability measure Prob sub(M) on the set of all finite and infinite words over A. We can identify a k letter alphabet A with the set {0, 1,..., k-1}, and, hence, we can consider every finite or infinite word w over A as a radix k expansion of a real number X(w) in the interval [0, 1]. This makes X(w) a random variable and the distribution function of M is defined as usual: F(x) := Prob sub(M) { w: X(w) < x }. Utilizing the fixed-point semantics (denotational semantics), extended to probabilistic computations, we investigate the distribution functions of probabilistic automata in detail. Automata with continuous distribution functions are characterized. By a new, and much more easier method, it is shown that the distribution function F(x) is an analytic function if it is a polynomial. Finally, answering a question posed by D. Knuth and A. Yao, we show that a polynomial distribution function F(x) on [0, 1] can be generated by a prob abilistic automaton iff all the roots of F'(x) = 0 in this interval, if any, are rational numbers. For this, we define two dynamical systems on the set of polynomial distributions and study attracting fixed points of random composition of these two systems.

Survey on Sentence Similarity Evaluation using Deep Learning

NASA Astrophysics Data System (ADS)

Ramaprabha, J.; Das, Sayan; Mukerjee, Pronay

2018-04-01

Two questions asking the same thing can have di erent set of vocabulary set and syntactic structure. Which makes detecting the semantics equivalence between the sentences challenging. In online user forums like Quora, Stack Over ow, Stack Exchange, etc. its important to maintain high quality knowledge base by ensuring each unique question exists only once. Writers shouldn't have to write the same answer to each of the similar question and the reader must get a single page of the question they are looking for. For example, consider questions like What are the best ways to lose weight?, How can a person reduce weight?, and What are elective weight loss plans? to be duplicate questions because they all have the same intent.

Auspice: Automatic Service Planning in Cloud/Grid Environments

NASA Astrophysics Data System (ADS)

Chiu, David; Agrawal, Gagan

Recent scientific advances have fostered a mounting number of services and data sets available for utilization. These resources, though scattered across disparate locations, are often loosely coupled both semantically and operationally. This loosely coupled relationship implies the possibility of linking together operations and data sets to answer queries. This task, generally known as automatic service composition, therefore abstracts the process of complex scientific workflow planning from the user. We have been exploring a metadata-driven approach toward automatic service workflow composition, among other enabling mechanisms, in our system, Auspice: Automatic Service Planning in Cloud/Grid Environments. In this paper, we present a complete overview of our system's unique features and outlooks for future deployment as the Cloud computing paradigm becomes increasingly eminent in enabling scientific computing.

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.

Testing the attentional boundary conditions of subliminal semantic priming: the influence of semantic and phonological task sets

PubMed Central

Adams, Sarah C.; Kiefer, Markus

2012-01-01

Recent studies challenged the classical notion of automaticity and indicated that even unconscious automatic semantic processing is under attentional control to some extent. In line with our attentional sensitization model, these data suggest that a sensitization of semantic pathways by a semantic task set is necessary for subliminal semantic priming to occur while non-semantic task sets attenuate priming. In the present study, we tested whether masked semantic priming is also reduced by phonological task sets using the previously developed induction task paradigm. This would substantiate the notion that attention to semantics is necessary for eliciting unconscious semantic priming. Participants first performed semantic and phonological induction tasks that should either activate a semantic or a phonological task set. Subsequent to the induction task, a masked prime word, either associated or non-associated with the following lexical decision target word, was presented. Across two experiments, we varied the nature of the phonological induction task (word phonology vs. letter phonology) to assess whether the attentional focus on the entire word vs. single letters modulates subsequent masked semantic priming. In both experiments, subliminal semantic priming was only found subsequent to the semantic induction task, but was attenuated following either phonological induction task. These results indicate that attention to phonology attenuates subsequent semantic processing of unconsciously presented primes whether or not attention is directed to the entire word or to single letters. The present findings therefore substantiate earlier evidence that an attentional orientation toward semantics is necessary for subliminal semantic priming to be elicited. PMID:22952461

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.

The Semantic Reactivity of Red, Blue, and Purple: A Linguistic Analysis of Post-Election Statements Made by Executive Leadership of Three Public Flagship Universities

ERIC Educational Resources Information Center

Taylor, Zachary Wayne

2017-01-01

Examining post-election statements made by UC System, UT-Austin, and UW-Madison executive leadership, this study employs word frequency, collocation, and a three-pronged latent semantic analysis to explicate the associative diction, major concepts, and institutional priorities expressed by said leadership to answer the research question,…

Raising the IQ in full-text searching via intelligent querying

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

Kero, R.; Russell, L.; Swietlik, C.

1994-11-01

Current Information Retrieval (IR) technologies allow for efficient access to relevant information, provided that user selected query terms coincide with the specific linguistical choices made by the authors whose works constitute the text-base. Therefore, the challenge is to enhance the limited searching capability of state-of-the-practice IR. This can be done either with augmented clients that overcome current server searching deficiencies, or with added capabilities that can augment searching algorithms on the servers. The technology being investigated is that of deductive databases, with a set of new techniques called cooperative answering. This technology utilizes semantic networks to allow for navigation betweenmore » possible query search term alternatives. The augmented search terms are passed to an IR engine and the results can be compared. The project utilizes the OSTI Environment, Safety and Health Thesaurus to populate the domain specific semantic network and the text base of ES&H related documents from the Facility Profile Information Management System as the domain specific search space.« less

The Influence of Information Structure on the Depth of Semantic Processing: How Focus and Pitch Accent Determine the Size of the N400 Effect

ERIC Educational Resources Information Center

Wang, Lin; Bastiaansen, Marcel; Yang, Yufang; Hagoort, Peter

2011-01-01

To highlight relevant information in dialogues, both wh-question context and pitch accent in answers can be used, such that focused information gains more attention and is processed more elaborately. To evaluate the relative influence of context and pitch accent on the depth of semantic processing, we measured event-related potentials (ERPs) to…

[Tool for measuring occupational stress: a nurses' stress inventory].

PubMed

Stacciarini, J M; Tróccoli, B T

2000-12-01

We present an exploratory study aiming at constructing an inventory to measure occupational stress in nurses ("Inventário de Estresse em Enfermeiros"--IEE). A set of items was initially constructed from previously defined categories based on interviews with nurses and then improved through semantic analysis by referees and a pilot-test with nursing students. A sample of 461 nurses--workers from the public services of the Federal District--who answered the IEE was used in the study. Factorial analysis indicated the presence of a second-order global factor and three first-order factors: Interpersonal Relationships, Stressful Career Roles and Intrinsic Job Factors.

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

SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases.

PubMed

Schweiger, Dominik; Trajanoski, Zlatko; Pabinger, Stephan

2014-08-15

Semantic Web has established itself as a framework for using and sharing data across applications and database boundaries. Here, we present a web-based platform for querying biological Semantic Web databases in a graphical way. SPARQLGraph offers an intuitive drag & drop query builder, which converts the visual graph into a query and executes it on a public endpoint. The tool integrates several publicly available Semantic Web databases, including the databases of the just recently released EBI RDF platform. Furthermore, it provides several predefined template queries for answering biological questions. Users can easily create and save new query graphs, which can also be shared with other researchers. This new graphical way of creating queries for biological Semantic Web databases considerably facilitates usability as it removes the requirement of knowing specific query languages and database structures. The system is freely available at http://sparqlgraph.i-med.ac.at.

Assessing Classroom Learning: How Students Use Their Knowledge and Experience to Answer Classroom Achievement Test Questions in Science and Social Studies.

ERIC Educational Resources Information Center

Nuthall, Graham; Alton-Lee, Adrienne

1995-01-01

Observational studies of student learning from classroom experience in science and social studies in elementary and middle school classrooms were carried out with 14 students. A model is described that explains how students use multilayered episodic and semantic memory for learning experience and related knowledge to answer achievement test items.…

Large-scale online semantic indexing of biomedical articles via an ensemble of multi-label classification models.

PubMed

Papanikolaou, Yannis; Tsoumakas, Grigorios; Laliotis, Manos; Markantonatos, Nikos; Vlahavas, Ioannis

2017-09-22

In this paper we present the approach that we employed to deal with large scale multi-label semantic indexing of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge (2013-2017), a challenge concerned with biomedical semantic indexing and question answering. Our main contribution is a MUlti-Label Ensemble method (MULE) that incorporates a McNemar statistical significance test in order to validate the combination of the constituent machine learning algorithms. Some secondary contributions include a study on the temporal aspects of the BioASQ corpus (observations apply also to the BioASQ's super-set, the PubMed articles collection) and the proper parametrization of the algorithms used to deal with this challenging classification task. The ensemble method that we developed is compared to other approaches in experimental scenarios with subsets of the BioASQ corpus giving positive results. In our participation in the BioASQ challenge we obtained the first place in 2013 and the second place in the four following years, steadily outperforming MTI, the indexing system of the National Library of Medicine (NLM). The results of our experimental comparisons, suggest that employing a statistical significance test to validate the ensemble method's choices, is the optimal approach for ensembling multi-label classifiers, especially in contexts with many rare labels.

A Knowledge-Based Representation Scheme for Environmental Science Models

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Dungan, Jennifer L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

Focused search of semantic cases: the effects of question form and case status.

PubMed

Singer, M; Jakobson, L S

1989-05-01

The present study was designed to identify and examine some of the variables that influence the focused search of semantic cases in question answering. Singer, Parbery, and Jakobson (1988) have previously reported that people can focus on the case interrogated by a question and can largely disregard irrelevant cases. In the present study, people learned facts, such as the pilot painted the garage with the roller, the spraygun, and the brush. One day later, they answered questions that focused on a particular case. For example, the question did the pilot paint with a spraygun? focuses on the instrument case. Experiment 1 revealed that people can focus on a particular case in response both to complete questions and to comparable word probes, such as "pilot spraygun." Therefore, the given-new structure of questions is not essential to focused search. Experiment 2 revealed that people have a difficult time ignoring the agent case, even when it is irrelevant to the question. This corroborates proposals that agent and action information are closely interrelated in the representation of a fact. These results help to delineate the phenomenon of the focused search of semantic cases.

Dynamic memory searches: Selective output interference for the memory of facts.

PubMed

Aue, William R; Criss, Amy H; Prince, Melissa A

2015-12-01

The benefits of testing on later memory performance are well documented; however, the manner in which testing harms memory performance is less well understood. This research is concerned with the finding that accuracy decreases over the course of testing, a phenomena termed "output interference" (OI). OI has primarily been investigated with episodic memory, but there is limited research investigating OI in measures of semantic memory (i.e., knowledge). In the current study, participants were twice tested for their knowledge of factual questions; they received corrective feedback during the first test. No OI was observed during the first test, when participants presumably searched semantic memory to answer the general-knowledge questions. During the second test, OI was observed. Conditional analyses of Test 2 performance revealed that OI was largely isolated to questions answered incorrectly during Test 1. These were questions for which participants needed to rely on recent experience (i.e., the feedback in episodic memory) to respond correctly. One possible explanation is that episodic memory is more susceptible to the sort of interference generated during testing (e.g., gradual changes in context, encoding/updating of items) relative to semantic memory. Alternative explanations are considered.

A figurative proverb test for dementia: rapid detection of disinhibition, excuse and confabulation, causing discommunication.

PubMed

Yamaguchi, Haruyasu; Maki, Yohko; Yamaguchi, Tomoharu

2011-12-01

Communicative disability is regarded as a prominent symptom of demented patients, and many studies have been devoted to analyze deficits of lexical-semantic operations in demented patients. However, it is often observed that even patients with preserved lexical-semantic skills might fail in interactive social communication. Whereas social interaction requires pragmatic language skills, pragmatic language competencies in demented subjects have not been well understood. We propose here a brief stress-free test to detect pragmatic language deficits, focusing on non-literal understanding of figurative expression. We hypothesized that suppression of the literal interpretation was required for figurative language interpretation.  We examined 69 demented subjects, 13 subjects with mild cognitive impairment and 61 healthy controls aged 65 years or more. The subjects were asked the meaning of a familiar proverb categorized as a figurative expression. The answers were analyzed based on five factors, and scored from 0 to 5. To consider the influence of cognitive inhibition on proverb comprehension, the scores of the Stroop Colour-Word Test were compared concerning correct and incorrect answers for each factor, respectively. Furthermore, the characteristics of answers were considered in the light of excuse and confabulation qualitatively. The proverb comprehension scores gradually decreased significantly as dementia progressed. The literal interpretation of the proverb, which showed difficulties in figurative language comprehension, was related to disinhibition. The qualitative analysis showed that excuse and confabulation increased as the dementia stage progressed. Deficits in cognitive inhibition partly explains the difficulties in interactive social communication in dementia. With qualitative analysis, asking the meaning of a proverb can be a brief test applied in a clinical setting to evaluate the stage of dementia, and to illustrate disinhibition, confabulation and excuse, which might cause discommunication and psychosocial maladjustment in demented patients. © 2011 The Authors. Psychogeriatrics © 2011 Japanese Psychogeriatric Society.

An ontology-driven semantic mash-up of gene and biological pathway information: Application to the domain of nicotine dependence

PubMed Central

Sahoo, Satya S.; Bodenreider, Olivier; Rutter, Joni L.; Skinner, Karen J.; Sheth, Amit P.

2008-01-01

Objectives This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. Methods We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Results Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Conclusion Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. Resource page http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/ PMID:18395495

An ontology-driven semantic mashup of gene and biological pathway information: application to the domain of nicotine dependence.

PubMed

Sahoo, Satya S; Bodenreider, Olivier; Rutter, Joni L; Skinner, Karen J; Sheth, Amit P

2008-10-01

This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. RESOURCE PAGE: http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/

The ontology-based answers (OBA) service: a connector for embedded usage of ontologies in applications.

PubMed

Dönitz, Jürgen; Wingender, Edgar

2012-01-01

The semantic web depends on the use of ontologies to let electronic systems interpret contextual information. Optimally, the handling and access of ontologies should be completely transparent to the user. As a means to this end, we have developed a service that attempts to bridge the gap between experts in a certain knowledge domain, ontologists, and application developers. The ontology-based answers (OBA) service introduced here can be embedded into custom applications to grant access to the classes of ontologies and their relations as most important structural features as well as to information encoded in the relations between ontology classes. Thus computational biologists can benefit from ontologies without detailed knowledge about the respective ontology. The content of ontologies is mapped to a graph of connected objects which is compatible to the object-oriented programming style in Java. Semantic functions implement knowledge about the complex semantics of an ontology beyond the class hierarchy and "partOf" relations. By using these OBA functions an application can, for example, provide a semantic search function, or (in the examples outlined) map an anatomical structure to the organs it belongs to. The semantic functions relieve the application developer from the necessity of acquiring in-depth knowledge about the semantics and curation guidelines of the used ontologies by implementing the required knowledge. The architecture of the OBA service encapsulates the logic to process ontologies in order to achieve a separation from the application logic. A public server with the current plugins is available and can be used with the provided connector in a custom application in scenarios analogous to the presented use cases. The server and the client are freely available if a project requires the use of custom plugins or non-public ontologies. The OBA service and further documentation is available at http://www.bioinf.med.uni-goettingen.de/projects/oba.

The ontology-based answers (OBA) service: a connector for embedded usage of ontologies in applications

PubMed Central

Dönitz, Jürgen; Wingender, Edgar

2012-01-01

The semantic web depends on the use of ontologies to let electronic systems interpret contextual information. Optimally, the handling and access of ontologies should be completely transparent to the user. As a means to this end, we have developed a service that attempts to bridge the gap between experts in a certain knowledge domain, ontologists, and application developers. The ontology-based answers (OBA) service introduced here can be embedded into custom applications to grant access to the classes of ontologies and their relations as most important structural features as well as to information encoded in the relations between ontology classes. Thus computational biologists can benefit from ontologies without detailed knowledge about the respective ontology. The content of ontologies is mapped to a graph of connected objects which is compatible to the object-oriented programming style in Java. Semantic functions implement knowledge about the complex semantics of an ontology beyond the class hierarchy and “partOf” relations. By using these OBA functions an application can, for example, provide a semantic search function, or (in the examples outlined) map an anatomical structure to the organs it belongs to. The semantic functions relieve the application developer from the necessity of acquiring in-depth knowledge about the semantics and curation guidelines of the used ontologies by implementing the required knowledge. The architecture of the OBA service encapsulates the logic to process ontologies in order to achieve a separation from the application logic. A public server with the current plugins is available and can be used with the provided connector in a custom application in scenarios analogous to the presented use cases. The server and the client are freely available if a project requires the use of custom plugins or non-public ontologies. The OBA service and further documentation is available at http://www.bioinf.med.uni-goettingen.de/projects/oba PMID:23060901

Effects on Vocabulary Acquisition of Presenting New Words in Semantic Sets versus Semantically Unrelated Sets

ERIC Educational Resources Information Center

Erten, Ismail Hakki; Tekin, Mustafa

2008-01-01

This paper reports on a study which investigated the effect on vocabulary recall of introducing new words via two different methods. A one-group quasi-experimental research design with alternating time series measures was employed. A group of 60 fourth graders were taught 80 carefully selected words either in semantically related sets or…

Assessing Semantic Knowledge Using Computer-Based and Paper-Based Media

DTIC Science & Technology

1992-01-01

capitalized upon in this research. Computer-Based Assessment A computer-based game or test, FlashCards (Liggett & Federico, 1986), was adopt- ed and adapted...alterative forms did not have to be specifically or previously programmed as such. FlashCards is analogous to using real flash cards. That is, a...reflects their degree of confidence in their answer. Also, for each answer the student’s response latency is recorded and displayed. FlashCards quizzed

Analyzing structural changes in SNOMED CT's Bacterial infectious diseases using a visual semantic delta.

PubMed

Ochs, Christopher; Case, James T; Perl, Yehoshua

2017-03-01

Thousands of changes are applied to SNOMED CT's concepts during each release cycle. These changes are the result of efforts to improve or expand the coverage of health domains in the terminology. Understanding which concepts changed, how they changed, and the overall impact of a set of changes is important for editors and end users. Each SNOMED CT release comes with delta files, which identify all of the individual additions and removals of concepts and relationships. These files typically contain tens of thousands of individual entries, overwhelming users. They also do not identify the editorial processes that were applied to individual concepts and they do not capture the overall impact of a set of changes on a subhierarchy of concepts. In this paper we introduce a methodology and accompanying software tool called a SNOMED CT Visual Semantic Delta ("semantic delta" for short) to enable a comprehensive review of changes in SNOMED CT. The semantic delta displays a graphical list of editing operations that provides semantics and context to the additions and removals in the delta files. However, there may still be thousands of editing operations applied to a set of concepts. To address this issue, a semantic delta includes a visual summary of changes that affected sets of structurally and semantically similar concepts. The software tool for creating semantic deltas offers views of various granularities, allowing a user to control how much change information they view. In this tool a user can select a set of structurally and semantically similar concepts and review the editing operations that affected their modeling. The semantic delta methodology is demonstrated on SNOMED CT's Bacterial infectious disease subhierarchy, which has undergone a significant remodeling effort over the last two years. Copyright © 2017 Elsevier Inc. All rights reserved.

Analyzing Structural Changes in SNOMED CT’s Bacterial Infectious Diseases Using a Visual Semantic Delta

PubMed Central

Ochs, Christopher; Case, James T.; Perl, Yehoshua

2017-01-01

Thousands of changes are applied to SNOMED CT’s concepts during each release cycle. These changes are the result of efforts to improve or expand the coverage of health domains in the terminology. Understanding which concepts changed, how they changed, and the overall impact of a set of changes is important for editors and end users. Each SNOMED CT release comes with delta files, which identify all of the individual additions and removals of concepts and relationships. These files typically contain tens of thousands of individual entries, overwhelming users. They also do not identify the editorial processes that were applied to individual concepts and they do not capture the overall impact of a set of changes on a subhierarchy of concepts. In this paper we introduce a methodology and accompanying software tool called a SNOMED CT Visual Semantic Delta (“semantic delta” for short) to enable a comprehensive review of changes in SNOMED CT. The semantic delta displays a graphical list of editing operations that provides semantics and context to the additions and removals in the delta files. However, there may still be thousands of editing operations applied to a set of concepts. To address this issue, a semantic delta includes a visual summary of changes that affected sets of structurally and semantically similar concepts. The software tool for creating semantic deltas offers views of various granularities, allowing a user to control how much change information they view. In this tool a user can select a set of structurally and semantically similar concepts and review the editing operations that affected their modeling. The semantic delta methodology is demonstrated on SNOMED CT’s Bacterial infectious disease subhierarchy, which has undergone a significant remodeling effort over the last two years. PMID:28215561

Can social semantic web techniques foster collaborative curriculum mapping in medicine?

PubMed

Spreckelsen, Cord; Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-08-15

Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives ("LOs"). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 ("good") in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system's ability to support curriculum revision. The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems.

Lexical and sublexical semantic preview benefits in Chinese reading.

PubMed

Yan, Ming; Zhou, Wei; Shu, Hua; Kliegl, Reinhold

2012-07-01

Semantic processing from parafoveal words is an elusive phenomenon in alphabetic languages, but it has been demonstrated only for a restricted set of noncompound Chinese characters. Using the gaze-contingent boundary paradigm, this experiment examined whether parafoveal lexical and sublexical semantic information was extracted from compound preview characters. Results generalized parafoveal semantic processing to this representative set of Chinese characters and extended the parafoveal processing to radical (sublexical) level semantic information extraction. Implications for notions of parafoveal information extraction during Chinese reading are discussed. 2012 APA, all rights reserved

Semantic memory: a feature-based analysis and new norms for Italian.

PubMed

Montefinese, Maria; Ambrosini, Ettore; Fairfield, Beth; Mammarella, Nicola

2013-06-01

Semantic norms for properties produced by native speakers are valuable tools for researchers interested in the structure of semantic memory and in category-specific semantic deficits in individuals following brain damage. The aims of this study were threefold. First, we sought to extend existing semantic norms by adopting an empirical approach to category (Exp. 1) and concept (Exp. 2) selection, in order to obtain a more representative set of semantic memory features. Second, we extensively outlined a new set of semantic production norms collected from Italian native speakers for 120 artifactual and natural basic-level concepts, using numerous measures and statistics following a feature-listing task (Exp. 3b). Finally, we aimed to create a new publicly accessible database, since only a few existing databases are publicly available online.

Classification of clinically useful sentences in clinical evidence resources.

PubMed

Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana; Jonnalagadda, Siddhartha R; Del Fiol, Guilherme

2016-04-01

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs. We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62). The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Practical Experiences for the Development of Educational Systems in the Semantic Web

ERIC Educational Resources Information Center

Sánchez Vera, Ma. del Mar; Tomás Fernández Breis, Jesualdo; Serrano Sánchez, José Luis; Prendes Espinosa, Ma. Paz

2013-01-01

Semantic Web technologies have been applied in educational settings for different purposes in recent years, with the type of application being mainly defined by the way in which knowledge is represented and exploited. The basic technology for knowledge representation in Semantic Web settings is the ontology, which represents a common, shareable…

Part-set cueing impairment & facilitation in semantic memory.

PubMed

Kelley, Matthew R; Parihar, Sushmeena A

2018-01-19

The present study explored the influence of part-set cues in semantic memory using tests of "free" recall, reconstruction of order, and serial recall. Nine distinct categories of information were used (e.g., Zodiac signs, Harry Potter books, Star Wars films, planets). The results showed part-set cueing impairment for all three "free" recall sets, whereas part-set cueing facilitation was evident for five of the six ordered sets. Generally, the present results parallel those often observed across episodic tasks, which could indicate that similar mechanisms contribute to part-set cueing effects in both episodic and semantic memory. A novel anchoring explanation of part-set cueing facilitation in order and spatial tasks is provided.

Incremental generation of answers during the comprehension of questions with quantifiers.

PubMed

Bott, Oliver; Augurzky, Petra; Sternefeld, Wolfgang; Ulrich, Rolf

2017-09-01

The paper presents a study on the online interpretation of quantified questions involving complex domain restriction, for instance, are all triangles blue that are in the circle. Two probe reaction time (RT) task experiments were conducted to study the incremental nature of answer generation while manipulating visual contexts and response hand overlap between tasks. We manipulated the contexts in such a way that the incremental answer to the question changed from 'yes' to 'no' or remained the same before and after encountering the extraposed relative clause. The findings of both experiments provide evidence for incremental answer preparation but only if the context did not involve the risk of answer revision. Our results show that preliminary output from incremental semantic interpretation results in response priming that facilitates congruent responses in the probe RT task. Copyright © 2017 Elsevier B.V. All rights reserved.

Thai Language Sentence Similarity Computation Based on Syntactic Structure and Semantic Vector

NASA Astrophysics Data System (ADS)

Wang, Hongbin; Feng, Yinhan; Cheng, Liang

2018-03-01

Sentence similarity computation plays an increasingly important role in text mining, Web page retrieval, machine translation, speech recognition and question answering systems. Thai language as a kind of resources scarce language, it is not like Chinese language with HowNet and CiLin resources. So the Thai sentence similarity research faces some challenges. In order to solve this problem of the Thai language sentence similarity computation. This paper proposes a novel method to compute the similarity of Thai language sentence based on syntactic structure and semantic vector. This method firstly uses the Part-of-Speech (POS) dependency to calculate two sentences syntactic structure similarity, and then through the word vector to calculate two sentences semantic similarity. Finally, we combine the two methods to calculate two Thai language sentences similarity. The proposed method not only considers semantic, but also considers the sentence syntactic structure. The experiment result shows that this method in Thai language sentence similarity computation is feasible.

Developing a semantically rich ontology for the biobank-administration domain

PubMed Central

2013-01-01

Background Biobanks are a critical resource for translational science. Recently, semantic web technologies such as ontologies have been found useful in retrieving research data from biobanks. However, recent research has also shown that there is a lack of data about the administrative aspects of biobanks. These data would be helpful to answer research-relevant questions such as what is the scope of specimens collected in a biobank, what is the curation status of the specimens, and what is the contact information for curators of biobanks. Our use cases include giving researchers the ability to retrieve key administrative data (e.g. contact information, contact's affiliation, etc.) about the biobanks where specific specimens of interest are stored. Thus, our goal is to provide an ontology that represents the administrative entities in biobanking and their relations. We base our ontology development on a set of 53 data attributes called MIABIS, which were in part the result of semantic integration efforts of the European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). The previous work on MIABIS provided the domain analysis for our ontology. We report on a test of our ontology against competency questions that we derived from the initial BBMRI use cases. Future work includes additional ontology development to answer additional competency questions from these use cases. Results We created an open-source ontology of biobank administration called Ontologized MIABIS (OMIABIS) coded in OWL 2.0 and developed according to the principles of the OBO Foundry. It re-uses pre-existing ontologies when possible in cooperation with developers of other ontologies in related domains, such as the Ontology of Biomedical Investigation. OMIABIS provides a formalized representation of biobanks and their administration. Using the ontology and a set of Description Logic queries derived from the competency questions that we identified, we were able to retrieve test data with perfect accuracy. In addition, we began development of a mapping from the ontology to pre-existing biobank data structures commonly used in the U.S. Conclusions In conclusion, we created OMIABIS, an ontology of biobank administration. We found that basing its development on pre-existing resources to meet the BBMRI use cases resulted in a biobanking ontology that is re-useable in environments other than BBMRI. Our ontology retrieved all true positives and no false positives when queried according to the competency questions we derived from the BBMRI use cases. Mapping OMIABIS to a data structure used for biospecimen collections in a medical center in Little Rock, AR showed adequate coverage of our ontology. PMID:24103726

Strategic Origins of Early Semantic Facilitation in the Blocked-Cyclic Naming Paradigm

ERIC Educational Resources Information Center

Belke, Eva; Shao, Zeshu; Meyer, Antje S.

2017-01-01

In the blocked-cyclic naming paradigm, participants repeatedly name small sets of objects that do or do not belong to the same semantic category. A standard finding is that, after a first presentation cycle where one might find semantic facilitation, naming is slower in related (homogeneous) than in unrelated (heterogeneous) sets. According to…

Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups

PubMed Central

Solbrig, Harold R; Chute, Christopher G

2012-01-01

Objective The objective of this study is to develop an approach to evaluate the quality of terminological annotations on the value set (ie, enumerated value domain) components of the common data elements (CDEs) in the context of clinical research using both unified medical language system (UMLS) semantic types and groups. Materials and methods The CDEs of the National Cancer Institute (NCI) Cancer Data Standards Repository, the NCI Thesaurus (NCIt) concepts and the UMLS semantic network were integrated using a semantic web-based framework for a SPARQL-enabled evaluation. First, the set of CDE-permissible values with corresponding meanings in external controlled terminologies were isolated. The corresponding value meanings were then evaluated against their NCI- or UMLS-generated semantic network mapping to determine whether all of the meanings fell within the same semantic group. Results Of the enumerated CDEs in the Cancer Data Standards Repository, 3093 (26.2%) had elements drawn from more than one UMLS semantic group. A random sample (n=100) of this set of elements indicated that 17% of them were likely to have been misclassified. Discussion The use of existing semantic web tools can support a high-throughput mechanism for evaluating the quality of large CDE collections. This study demonstrates that the involvement of multiple semantic groups in an enumerated value domain of a CDE is an effective anchor to trigger an auditing point for quality evaluation activities. Conclusion This approach produces a useful quality assurance mechanism for a clinical study CDE repository. PMID:22511016

Semantic Annotations and Querying of Web Data Sources

NASA Astrophysics Data System (ADS)

Hornung, Thomas; May, Wolfgang

A large part of the Web, actually holding a significant portion of the useful information throughout the Web, consists of views on hidden databases, provided by numerous heterogeneous interfaces that are partly human-oriented via Web forms ("Deep Web"), and partly based on Web Services (only machine accessible). In this paper we present an approach for annotating these sources in a way that makes them citizens of the Semantic Web. We illustrate how queries can be stated in terms of the ontology, and how the annotations are used to selected and access appropriate sources and to answer the queries.

d-Neighborhood system and generalized F-contraction in dislocated metric space.

PubMed

Kumari, P Sumati; Zoto, Kastriot; Panthi, Dinesh

2015-01-01

This paper, gives an answer for the Question 1.1 posed by Hitzler (Generalized metrics and topology in logic programming semantics, 2001) by means of "Topological aspects of d-metric space with d-neighborhood system". We have investigated the topological aspects of a d-neighborhood system obtained from dislocated metric space (simply d-metric space) which has got useful applications in the semantic analysis of logic programming. Further more we have generalized the notion of F-contraction in the view of d-metric spaces and investigated the uniqueness of fixed point and coincidence point of such mappings.

The Development of Clinical Document Standards for Semantic Interoperability in China

PubMed Central

Yang, Peng; Pan, Feng; Wan, Yi; Tu, Haibo; Tang, Xuejun; Hu, Jianping

2011-01-01

Objectives This study is aimed at developing a set of data groups (DGs) to be employed as reusable building blocks for the construction of the eight most common clinical documents used in China's general hospitals in order to achieve their structural and semantic standardization. Methods The Diagnostics knowledge framework, the related approaches taken from the Health Level Seven (HL7), the Integrating the Healthcare Enterprise (IHE), and the Healthcare Information Technology Standards Panel (HITSP) and 1,487 original clinical records were considered together to form the DG architecture and data sets. The internal structure, content, and semantics of each DG were then defined by mapping each DG data set to a corresponding Clinical Document Architecture data element and matching each DG data set to the metadata in the Chinese National Health Data Dictionary. By using the DGs as reusable building blocks, standardized structures and semantics regarding the clinical documents for semantic interoperability were able to be constructed. Results Altogether, 5 header DGs, 48 section DGs, and 17 entry DGs were developed. Several issues regarding the DGs, including their internal structure, identifiers, data set names, definitions, length and format, data types, and value sets, were further defined. Standardized structures and semantics regarding the eight clinical documents were structured by the DGs. Conclusions This approach of constructing clinical document standards using DGs is a feasible standard-driven solution useful in preparing documents possessing semantic interoperability among the disparate information systems in China. These standards need to be validated and refined through further study. PMID:22259722

Semantic distance as a critical factor in icon design for in-car infotainment systems.

PubMed

Silvennoinen, Johanna M; Kujala, Tuomo; Jokinen, Jussi P P

2017-11-01

In-car infotainment systems require icons that enable fluent cognitive information processing and safe interaction while driving. An important issue is how to find an optimised set of icons for different functions in terms of semantic distance. In an optimised icon set, every icon needs to be semantically as close as possible to the function it visually represents and semantically as far as possible from the other functions represented concurrently. In three experiments (N = 21 each), semantic distances of 19 icons to four menu functions were studied with preference rankings, verbal protocols, and the primed product comparisons method. The results show that the primed product comparisons method can be efficiently utilised for finding an optimised set of icons for time-critical applications out of a larger set of icons. The findings indicate the benefits of the novel methodological perspective into the icon design for safety-critical contexts in general. Copyright © 2017 Elsevier Ltd. All rights reserved.

Finding Meaning: Sense Inventories for Improved Word Sense Disambiguation

ERIC Educational Resources Information Center

Brown, Susan Windisch

2010-01-01

The deep semantic understanding necessary for complex natural language processing tasks, such as automatic question-answering or text summarization, would benefit from highly accurate word sense disambiguation (WSD). This dissertation investigates what makes an appropriate and effective sense inventory for WSD. Drawing on theories and…

Can Social Semantic Web Techniques Foster Collaborative Curriculum Mapping In Medicine?

PubMed Central

Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-01-01

Background Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. Objective The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. Methods A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives (“LOs”). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. Results At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 (“good”) in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system’s ability to support curriculum revision. Conclusions The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems. PMID:23948519

Auditing the Assignments of Top-Level Semantic Types in the UMLS Semantic Network to UMLS Concepts

PubMed Central

He, Zhe; Perl, Yehoshua; Elhanan, Gai; Chen, Yan; Geller, James; Bian, Jiang

2018-01-01

The Unified Medical Language System (UMLS) is an important terminological system. By the policy of its curators, each concept of the UMLS should be assigned the most specific Semantic Types (STs) in the UMLS Semantic Network (SN). Hence, the Semantic Types of most UMLS concepts are assigned at or near the bottom (leaves) of the UMLS Semantic Network. While most ST assignments are correct, some errors do occur. Therefore, Quality Assurance efforts of UMLS curators for ST assignments should concentrate on automatically detected sets of UMLS concepts with higher error rates than random sets. In this paper, we investigate the assignments of top-level semantic types in the UMLS semantic network to concepts, identify potential erroneous assignments, define four categories of errors, and thus provide assistance to curators of the UMLS to avoid these assignments errors. Human experts analyzed samples of concepts assigned 10 of the top-level semantic types and categorized the erroneous ST assignments into these four logical categories. Two thirds of the concepts assigned these 10 top-level semantic types are erroneous. Our results demonstrate that reviewing top-level semantic type assignments to concepts provides an effective way for UMLS quality assurance, comparing to reviewing a random selection of semantic type assignments. PMID:29375930

Auditing the Assignments of Top-Level Semantic Types in the UMLS Semantic Network to UMLS Concepts.

PubMed

He, Zhe; Perl, Yehoshua; Elhanan, Gai; Chen, Yan; Geller, James; Bian, Jiang

2017-11-01

The Unified Medical Language System (UMLS) is an important terminological system. By the policy of its curators, each concept of the UMLS should be assigned the most specific Semantic Types (STs) in the UMLS Semantic Network (SN). Hence, the Semantic Types of most UMLS concepts are assigned at or near the bottom (leaves) of the UMLS Semantic Network. While most ST assignments are correct, some errors do occur. Therefore, Quality Assurance efforts of UMLS curators for ST assignments should concentrate on automatically detected sets of UMLS concepts with higher error rates than random sets. In this paper, we investigate the assignments of top-level semantic types in the UMLS semantic network to concepts, identify potential erroneous assignments, define four categories of errors, and thus provide assistance to curators of the UMLS to avoid these assignments errors. Human experts analyzed samples of concepts assigned 10 of the top-level semantic types and categorized the erroneous ST assignments into these four logical categories. Two thirds of the concepts assigned these 10 top-level semantic types are erroneous. Our results demonstrate that reviewing top-level semantic type assignments to concepts provides an effective way for UMLS quality assurance, comparing to reviewing a random selection of semantic type assignments.

Generating and Executing Complex Natural Language Queries across Linked Data.

PubMed

Hamon, Thierry; Mougin, Fleur; Grabar, Natalia

2015-01-01

With the recent and intensive research in the biomedical area, the knowledge accumulated is disseminated through various knowledge bases. Links between these knowledge bases are needed in order to use them jointly. Linked Data, SPARQL language, and interfaces in Natural Language question-answering provide interesting solutions for querying such knowledge bases. We propose a method for translating natural language questions in SPARQL queries. We use Natural Language Processing tools, semantic resources, and the RDF triples description. The method is designed on 50 questions over 3 biomedical knowledge bases, and evaluated on 27 questions. It achieves 0.78 F-measure on the test set. The method for translating natural language questions into SPARQL queries is implemented as Perl module available at http://search.cpan.org/ thhamon/RDF-NLP-SPARQLQuery.

Regional Brain Dysfunction Associated with Semantic Errors in Comprehension.

PubMed

Shahid, Hinna; Sebastian, Rajani; Tippett, Donna C; Saxena, Sadhvi; Wright, Amy; Hanayik, Taylor; Breining, Bonnie; Bonilha, Leonardo; Fridriksson, Julius; Rorden, Chris; Hillis, Argye E

2018-02-01

Here we illustrate how investigation of individuals acutely after stroke, before structure/function reorganization through recovery or rehabilitation, can be helpful in answering questions about the role of specific brain regions in language functions. Although there is converging evidence from a variety of sources that the left posterior-superior temporal gyrus plays some role in spoken word comprehension, its precise role in this function has not been established. We hypothesized that this region is essential for distinguishing between semantically related words, because it is critical for linking the spoken word to the complete semantic representation. We tested this hypothesis in 127 individuals with 48 hours of acute ischemic stroke, before the opportunity for reorganization or recovery. We identified tissue dysfunction (acute infarct and/or hypoperfusion) in gray and white matter parcels of the left hemisphere, and we evaluated the association between rate of semantic errors in a word-picture verification tasks and extent of tissue dysfunction in each region. We found that after correcting for lesion volume and multiple comparisons, the rate of semantic errors correlated with the extent of tissue dysfunction in left posterior-superior temporal gyrus and retrolenticular white matter. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Developmental changes in the inferior frontal cortex for selecting semantic representations

PubMed Central

Lee, Shu-Hui; Booth, James R.; Chen, Shiou-Yuan; Chou, Tai-Li

2012-01-01

Functional magnetic resonance imaging (fMRI) was used to examine the neural correlates of semantic judgments to Chinese words in a group of 10–15 year old Chinese children. Two semantic tasks were used: visual–visual versus visual–auditory presentation. The first word was visually presented (i.e. character) and the second word was either visually or auditorily presented, and the participant had to determine if these two words were related in meaning. Different from English, Chinese has many homophones in which each spoken word corresponds to many characters. The visual–auditory task, therefore, required greater engagement of cognitive control for the participants to select a semantically appropriate answer for the second homophonic word. Weaker association pairs produced greater activation in the mid-ventral region of left inferior frontal gyrus (BA 45) for both tasks. However, this effect was stronger for the visual–auditory task than for the visual–visual task and this difference was stronger for older compared to younger children. The findings suggest greater involvement of semantic selection mechanisms in the cross-modal task requiring the access of the appropriate meaning of homophonic spoken words, especially for older children. PMID:22337757

A Semi-Automatic Approach to Construct Vietnamese Ontology from Online Text

ERIC Educational Resources Information Center

Nguyen, Bao-An; Yang, Don-Lin

2012-01-01

An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with…

Concealed Questions. In Search of Answers

ERIC Educational Resources Information Center

Frana, Ilaria

2010-01-01

This dissertation examines the semantic interpretation of various types of DPs in so-called concealed-question (CQ) constructions, as "Bill's phone number" in the sentence "John knows Bill's phone number". The peculiar characteristic of DP-CQs is that they are interpreted as having the meaning of an embedded question. So, for instance, the…

ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

PubMed

Ben Abdallah, Emna; Folschette, Maxime; Roux, Olivier; Magnin, Morgan

2017-01-01

This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.

Automated identification of molecular effects of drugs (AIMED)

PubMed Central

Fathiamini, Safa; Johnson, Amber M; Zeng, Jia; Araya, Alejandro; Holla, Vijaykumar; Bailey, Ann M; Litzenburger, Beate C; Sanchez, Nora S; Khotskaya, Yekaterina; Xu, Hua; Meric-Bernstam, Funda; Bernstam, Elmer V

2016-01-01

Introduction Genomic profiling information is frequently available to oncologists, enabling targeted cancer therapy. Because clinically relevant information is rapidly emerging in the literature and elsewhere, there is a need for informatics technologies to support targeted therapies. To this end, we have developed a system for Automated Identification of Molecular Effects of Drugs, to help biomedical scientists curate this literature to facilitate decision support. Objectives To create an automated system to identify assertions in the literature concerning drugs targeting genes with therapeutic implications and characterize the challenges inherent in automating this process in rapidly evolving domains. Methods We used subject-predicate-object triples (semantic predications) and co-occurrence relations generated by applying the SemRep Natural Language Processing system to MEDLINE abstracts and ClinicalTrials.gov descriptions. We applied customized semantic queries to find drugs targeting genes of interest. The results were manually reviewed by a team of experts. Results Compared to a manually curated set of relationships, recall, precision, and F2 were 0.39, 0.21, and 0.33, respectively, which represents a 3- to 4-fold improvement over a publically available set of predications (SemMedDB) alone. Upon review of ostensibly false positive results, 26% were considered relevant additions to the reference set, and an additional 61% were considered to be relevant for review. Adding co-occurrence data improved results for drugs in early development, but not their better-established counterparts. Conclusions Precision medicine poses unique challenges for biomedical informatics systems that help domain experts find answers to their research questions. Further research is required to improve the performance of such systems, particularly for drugs in development. PMID:27107438

A topic clustering approach to finding similar questions from large question and answer archives.

PubMed

Zhang, Wei-Nan; Liu, Ting; Yang, Yang; Cao, Liujuan; Zhang, Yu; Ji, Rongrong

2014-01-01

With the blooming of Web 2.0, Community Question Answering (CQA) services such as Yahoo! Answers (http://answers.yahoo.com), WikiAnswer (http://wiki.answers.com), and Baidu Zhidao (http://zhidao.baidu.com), etc., have emerged as alternatives for knowledge and information acquisition. Over time, a large number of question and answer (Q&A) pairs with high quality devoted by human intelligence have been accumulated as a comprehensive knowledge base. Unlike the search engines, which return long lists of results, searching in the CQA services can obtain the correct answers to the question queries by automatically finding similar questions that have already been answered by other users. Hence, it greatly improves the efficiency of the online information retrieval. However, given a question query, finding the similar and well-answered questions is a non-trivial task. The main challenge is the word mismatch between question query (query) and candidate question for retrieval (question). To investigate this problem, in this study, we capture the word semantic similarity between query and question by introducing the topic modeling approach. We then propose an unsupervised machine-learning approach to finding similar questions on CQA Q&A archives. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods.

Adding question answering to an e-tutor for programming languages

NASA Astrophysics Data System (ADS)

Taylor, Kate; Moore, Simon

Control over a closed domain of textual material removes many question answering issues, as does an ontology that is closely intertwined with its sources. This pragmatic, shallow approach to many challenging areas of research in adaptive hypermedia, question answering, intelligent tutoring and humancomputer interaction has been put into practice at Cambridge in the Computer Science undergraduate course to teach the hardware description language Veri/og. This language itself poses many challenges as it crosses the interdisciplinary boundary between hardware and software engineers, giving rise to severalhuman ontologies as well as theprogramming language itself We present further results from ourformal and informal surveys. We look at further work to increase the dialogue between studentand tutor and export our knowledge to the Semantic Web.

The influence of speech rate and accent on access and use of semantic information.

PubMed

Sajin, Stanislav M; Connine, Cynthia M

2017-04-01

Circumstances in which the speech input is presented in sub-optimal conditions generally lead to processing costs affecting spoken word recognition. The current study indicates that some processing demands imposed by listening to difficult speech can be mitigated by feedback from semantic knowledge. A set of lexical decision experiments examined how foreign accented speech and word duration impact access to semantic knowledge in spoken word recognition. Results indicate that when listeners process accented speech, the reliance on semantic information increases. Speech rate was not observed to influence semantic access, except in the setting in which unusually slow accented speech was presented. These findings support interactive activation models of spoken word recognition in which attention is modulated based on speech demands.

How to switch on and switch off semantic priming effects for natural and artifactual categories: activation processes in category memory depend on focusing specific feature dimensions.

PubMed

Bermeitinger, Christina; Wentura, Dirk; Frings, Christian

2011-06-01

"Semantic priming" refers to the phenomenon that people react faster to target words preceded by semantically related rather than semantically unrelated words. We wondered whether momentary mind sets modulate semantic priming for natural versus artifactual categories. We interspersed a category priming task with a second task that required participants to react to either the perceptual or action features of simple geometric shapes. Focusing on perceptual features enhanced semantic priming effects for natural categories, whereas focusing on action features enhanced semantic priming effects for artifactual categories. In fact, significant priming effects emerged only for those categories thought to rely on the features activated by the second task. This result suggests that (a) priming effects depend on momentary mind set and (b) features can be weighted flexibly in concept representations; it is also further evidence for sensory-functional accounts of concept and category representation.

Strategic origins of early semantic facilitation in the blocked-cyclic naming paradigm.

PubMed

Belke, Eva; Shao, Zeshu; Meyer, Antje S

2017-10-01

In the blocked-cyclic naming paradigm, participants repeatedly name small sets of objects that do or do not belong to the same semantic category. A standard finding is that, after a first presentation cycle where one might find semantic facilitation, naming is slower in related (homogeneous) than in unrelated (heterogeneous) sets. According to competitive theories of lexical selection, this is because the lexical representations of the object names compete more vigorously in homogeneous than in heterogeneous sets. However, Navarrete, del Prato, Peressotti, and Mahon (2014) argued that this pattern of results was not due to increased lexical competition but to weaker repetition priming in homogeneous compared to heterogeneous sets. They demonstrated that when homogeneous sets were not repeated immediately but interleaved with unrelated sets, semantic relatedness induced facilitation rather than interference. We replicate this finding but also show that the facilitation effect has a strategic origin: It is substantial when sets are separated by pauses, making it easy for participants to notice the relatedness within some sets and use it to predict upcoming items. However, the effect is much reduced when these pauses are eliminated. In our view, the semantic facilitation effect does not constitute evidence against competitive theories of lexical selection. It can be accounted for within any framework that acknowledges strategic influences on the speed of object naming in the blocked-cyclic naming paradigm. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The Weight of Iron and Feathers

ERIC Educational Resources Information Center

Zendri, G.; Gratton, L. M.; Oss, S.

2014-01-01

We discuss the popular question concerning the difference in weight between 1 kg of iron and 1 kg of feathers, by taking into account the non-trivial aspect of the semantic interpretation of "weight" and the weighting procedure. The inclusion of air buoyancy makes the correct answer an interesting one. We describe and comment on the…

CONSTRUCT: In Search of a Theory of Meaning. Technical Report No. 238.

ERIC Educational Resources Information Center

Smith, R. L.; And Others

A new language-processing system, CONSTRUCT, is described and defined as a question-answering system for elementary mathematical language using natural language input. The primary goal is said to be an attempt to reach a better understanding of the relationship between syntactic and semantic components of natural language. The "meaning…

Ontology-Based e-Assessment for Accounting Education

ERIC Educational Resources Information Center

Litherland, Kate; Carmichael, Patrick; Martínez-García, Agustina

2013-01-01

This summary reports on a pilot of a novel, ontology-based e-assessment system in accounting. The system, OeLe, uses emerging semantic technologies to offer an online assessment environment capable of marking students' free text answers to questions of a conceptual nature. It does this by matching their response with a "concept map" or…

Can Thematic Roles Leave Traces of Their Places?

ERIC Educational Resources Information Center

Chang, Franklin; Bock, Kathryn; Goldberg, Adele E.

2003-01-01

An important question in the study of language production is the nature of the semantic information that speakers use to create syntactic structures. A common answer to this question assumes that thematic roles help to mediate the mapping from messages to syntax. However, research using structural priming has suggested that the construction of…

Using Life Histories to Individualize Nursing Home Staff Attitudes toward Residents.

ERIC Educational Resources Information Center

Pietrukowicz, Mary E.; Johnson, Mitzi M. S.

1991-01-01

Nurse's aides (n=43) at two institutions answered questions about their experience and knowledge of aging and rated nursing home residents on a semantic differential (attitude) scale. Results indicated including a brief life history in a routine medical chart caused aides to perceive residents in a more positive manner. (Author/ABL)

Rapid Parallel Semantic Processing of Numbers without Awareness

ERIC Educational Resources Information Center

Van Opstal, Filip; de Lange, Floris P.; Dehaene, Stanislas

2011-01-01

In this study, we investigate whether multiple digits can be processed at a semantic level without awareness, either serially or in parallel. In two experiments, we presented participants with two successive sets of four simultaneous Arabic digits. The first set was masked and served as a subliminal prime for the second, visible target set.…

Semantic enrichment of clinical models towards semantic interoperability. The heart failure summary use case.

PubMed

Martínez-Costa, Catalina; Cornet, Ronald; Karlsson, Daniel; Schulz, Stefan; Kalra, Dipak

2015-05-01

To improve semantic interoperability of electronic health records (EHRs) by ontology-based mediation across syntactically heterogeneous representations of the same or similar clinical information. Our approach is based on a semantic layer that consists of: (1) a set of ontologies supported by (2) a set of semantic patterns. The first aspect of the semantic layer helps standardize the clinical information modeling task and the second shields modelers from the complexity of ontology modeling. We applied this approach to heterogeneous representations of an excerpt of a heart failure summary. Using a set of finite top-level patterns to derive semantic patterns, we demonstrate that those patterns, or compositions thereof, can be used to represent information from clinical models. Homogeneous querying of the same or similar information, when represented according to heterogeneous clinical models, is feasible. Our approach focuses on the meaning embedded in EHRs, regardless of their structure. This complex task requires a clear ontological commitment (ie, agreement to consistently use the shared vocabulary within some context), together with formalization rules. These requirements are supported by semantic patterns. Other potential uses of this approach, such as clinical models validation, require further investigation. We show how an ontology-based representation of a clinical summary, guided by semantic patterns, allows homogeneous querying of heterogeneous information structures. Whether there are a finite number of top-level patterns is an open question. © 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.

Mechanisms of masked evaluative priming: task sets modulate behavioral and electrophysiological priming for picture and words differentially

PubMed Central

Liegel, Nathalie; Zovko, Monika; Wentura, Dirk

2017-01-01

Abstract Research with the evaluative priming paradigm has shown that affective evaluation processes reliably influence cognition and behavior, even when triggered outside awareness. However, the precise mechanisms underlying such subliminal evaluative priming effects, response activation vs semantic processing, are matter of a debate. In this study, we determined the relative contribution of semantic processing and response activation to masked evaluative priming with pictures and words. To this end, we investigated the modulation of masked pictorial vs verbal priming by previously activated perceptual vs semantic task sets and assessed the electrophysiological correlates of priming using event-related potential (ERP) recordings. Behavioral and electrophysiological effects showed a differential modulation of pictorial and verbal subliminal priming by previously activated task sets: Pictorial priming was only observed during the perceptual but not during the semantic task set. Verbal priming, in contrast, was found when either task set was activated. Furthermore, only verbal priming was associated with a modulation of the N400 ERP component, an index of semantic processing, whereas a priming-related modulation of earlier ERPs, indexing visuo-motor S-R activation, was found for both picture and words. The results thus demonstrate that different neuro-cognitive processes contribute to unconscious evaluative priming depending on the stimulus format. PMID:27998994

What we talk about when we talk about access deficits

PubMed Central

Mirman, Daniel; Britt, Allison E.

2014-01-01

Semantic impairments have been divided into storage deficits, in which the semantic representations themselves are damaged, and access deficits, in which the representations are intact but access to them is impaired. The behavioural phenomena that have been associated with access deficits include sensitivity to cueing, sensitivity to presentation rate, performance inconsistency, negative serial position effects, sensitivity to number and strength of competitors, semantic blocking effects, disordered selection between strong and weak competitors, correlation between semantic deficits and executive function deficits and reduced word frequency effects. Four general accounts have been proposed for different subsets of these phenomena: abnormal refractoriness, too much activation, impaired competitive selection and deficits of semantic control. A combination of abnormal refractoriness and impaired competitive selection can account for most of the behavioural phenomena, but there remain several open questions. In particular, it remains unclear whether access deficits represent a single syndrome, a syndrome with multiple subtypes or a variable collection of phenomena, whether the underlying deficit is domain-general or domain-specific, whether it is owing to disorders of inhibition, activation or selection, and the nature of the connection (if any) between access phenomena in aphasia and in neurologically intact controls. Computational models offer a promising approach to answering these questions. PMID:24324232

E-Government Goes Semantic Web: How Administrations Can Transform Their Information Processes

NASA Astrophysics Data System (ADS)

Klischewski, Ralf; Ukena, Stefan

E-government applications and services are built mainly on access to, retrieval of, integration of, and delivery of relevant information to citizens, businesses, and administrative users. In order to perform such information processing automatically through the Semantic Web,1 machine-readable2 enhancements of web resources are needed, based on the understanding of the content and context of the information in focus. While these enhancements are far from trivial to produce, administrations in their role of information and service providers so far find little guidance on how to migrate their web resources and enable a new quality of information processing; even research is still seeking best practices. Therefore, the underlying research question of this chapter is: what are the appropriate approaches which guide administrations in transforming their information processes toward the Semantic Web? In search for answers, this chapter analyzes the challenges and possible solutions from the perspective of administrations: (a) the reconstruction of the information processing in the e-government in terms of how semantic technologies must be employed to support information provision and consumption through the Semantic Web; (b) the required contribution to the transformation is compared to the capabilities and expectations of administrations; and (c) available experience with the steps of transformation are reviewed and discussed as to what extent they can be expected to successfully drive the e-government to the Semantic Web. This research builds on studying the case of Schleswig-Holstein, Germany, where semantic technologies have been used within the frame of the Access-eGov3 project in order to semantically enhance electronic service interfaces with the aim of providing a new way of accessing and combining e-government services.

XSemantic: An Extension of LCA Based XML Semantic Search

NASA Astrophysics Data System (ADS)

Supasitthimethee, Umaporn; Shimizu, Toshiyuki; Yoshikawa, Masatoshi; Porkaew, Kriengkrai

One of the most convenient ways to query XML data is a keyword search because it does not require any knowledge of XML structure or learning a new user interface. However, the keyword search is ambiguous. The users may use different terms to search for the same information. Furthermore, it is difficult for a system to decide which node is likely to be chosen as a return node and how much information should be included in the result. To address these challenges, we propose an XML semantic search based on keywords called XSemantic. On the one hand, we give three definitions to complete in terms of semantics. Firstly, the semantic term expansion, our system is robust from the ambiguous keywords by using the domain ontology. Secondly, to return semantic meaningful answers, we automatically infer the return information from the user queries and take advantage of the shortest path to return meaningful connections between keywords. Thirdly, we present the semantic ranking that reflects the degree of similarity as well as the semantic relationship so that the search results with the higher relevance are presented to the users first. On the other hand, in the LCA and the proximity search approaches, we investigated the problem of information included in the search results. Therefore, we introduce the notion of the Lowest Common Element Ancestor (LCEA) and define our simple rule without any requirement on the schema information such as the DTD or XML Schema. The first experiment indicated that XSemantic not only properly infers the return information but also generates compact meaningful results. Additionally, the benefits of our proposed semantics are demonstrated by the second experiment.

The ERP correlates of self-knowledge: Are assessments of one's past, present, and future traits closer to semantic or episodic memory?

PubMed

Tanguay, Annick N; Benton, Lauren; Romio, Lorenza; Sievers, Carolin; Davidson, Patrick S R; Renoult, Louis

2018-02-01

Self-knowledge concerns one's own preferences and personality. It pertains to the self (similar to episodic memory), yet does not concern events. It is factual (like semantic memory), but also idiosyncratic. For these reasons, it is unclear where self-knowledge might fall on a continuum in relation to semantic and episodic memory. In this study, we aimed to compare the event-related potential (ERP) correlates of self-knowledge to those of semantic and episodic memory, using N400 and Late Positive Component (LPC) as proxies for semantic and episodic processing, respectively. We considered an additional factor: time perspective. Temporally distant selves have been suggested to be more semantic compared to the present self, but thinking about one's past and future selves may also engage episodic memory. Twenty-eight adults answered whether traits (e.g., persistent) were true of most people holding an occupation (e.g., soldiers; semantic memory condition), or true of themselves 5 years ago, in the present, or 5 years from now (past, present, and future self-knowledge conditions). The study ended with an episodic recognition memory task for previously seen traits. Present self-knowledge produced mean LPC amplitudes at posterior parietal sites that fell between semantic and episodic memory. Mean LPC amplitudes for past and future self-knowledge were greater than for semantic memory, and not significantly different from episodic memory. Mean N400 amplitudes for the self-knowledge conditions were smaller than for semantic memory at sagittal sites. However, this N400 effect was not separable from a preceding P200 effect at these same electrode sites. This P200 effect can be interpreted as reflecting the greater emotional salience of self as compared to general knowledge, which may have facilitated semantic processing. Overall, our findings are consistent with a distinction between knowledge of others and self-knowledge, but the closeness of self-knowledge's neural correlates to either semantic or episodic memory appears to depend to some extent on time perspective. Copyright © 2017 Elsevier Ltd. All rights reserved.

Semantic Document Model to Enhance Data and Knowledge Interoperability

NASA Astrophysics Data System (ADS)

Nešić, Saša

To enable document data and knowledge to be efficiently shared and reused across application, enterprise, and community boundaries, desktop documents should be completely open and queryable resources, whose data and knowledge are represented in a form understandable to both humans and machines. At the same time, these are the requirements that desktop documents need to satisfy in order to contribute to the visions of the Semantic Web. With the aim of achieving this goal, we have developed the Semantic Document Model (SDM), which turns desktop documents into Semantic Documents as uniquely identified and semantically annotated composite resources, that can be instantiated into human-readable (HR) and machine-processable (MP) forms. In this paper, we present the SDM along with an RDF and ontology-based solution for the MP document instance. Moreover, on top of the proposed model, we have built the Semantic Document Management System (SDMS), which provides a set of services that exploit the model. As an application example that takes advantage of SDMS services, we have extended MS Office with a set of tools that enables users to transform MS Office documents (e.g., MS Word and MS PowerPoint) into Semantic Documents, and to search local and distant semantic document repositories for document content units (CUs) over Semantic Web protocols.

Using semantics for representing experimental protocols.

PubMed

Giraldo, Olga; García, Alexander; López, Federico; Corcho, Oscar

2017-11-13

An experimental protocol is a sequence of tasks and operations executed to perform experimental research in biological and biomedical areas, e.g. biology, genetics, immunology, neurosciences, virology. Protocols often include references to equipment, reagents, descriptions of critical steps, troubleshooting and tips, as well as any other information that researchers deem important for facilitating the reusability of the protocol. Although experimental protocols are central to reproducibility, the descriptions are often cursory. There is the need for a unified framework with respect to the syntactic structure and the semantics for representing experimental protocols. In this paper we present "SMART Protocols ontology", an ontology for representing experimental protocols. Our ontology represents the protocol as a workflow with domain specific knowledge embedded within a document. We also present the S ample I nstrument R eagent O bjective (SIRO) model, which represents the minimal common information shared across experimental protocols. SIRO was conceived in the same realm as the Patient Intervention Comparison Outcome (PICO) model that supports search, retrieval and classification purposes in evidence based medicine. We evaluate our approach against a set of competency questions modeled as SPARQL queries and processed against a set of published and unpublished protocols modeled with the SP Ontology and the SIRO model. Our approach makes it possible to answer queries such as Which protocols use tumor tissue as a sample. Improving reporting structures for experimental protocols requires collective efforts from authors, peer reviewers, editors and funding bodies. The SP Ontology is a contribution towards this goal. We build upon previous experiences and bringing together the view of researchers managing protocols in their laboratory work. Website: https://smartprotocols.github.io/ .

Learning multiple relative attributes with humans in the loop.

PubMed

Qian, Buyue; Wang, Xiang; Cao, Nan; Jiang, Yu-Gang; Davidson, Ian

2014-12-01

Semantic attributes have been recognized as a more spontaneous manner to describe and annotate image content. It is widely accepted that image annotation using semantic attributes is a significant improvement to the traditional binary or multiclass annotation due to its naturally continuous and relative properties. Though useful, existing approaches rely on an abundant supervision and high-quality training data, which limit their applicability. Two standard methods to overcome small amounts of guidance and low-quality training data are transfer and active learning. In the context of relative attributes, this would entail learning multiple relative attributes simultaneously and actively querying a human for additional information. This paper addresses the two main limitations in existing work: 1) it actively adds humans to the learning loop so that minimal additional guidance can be given and 2) it learns multiple relative attributes simultaneously and thereby leverages dependence amongst them. In this paper, we formulate a joint active learning to rank framework with pairwise supervision to achieve these two aims, which also has other benefits such as the ability to be kernelized. The proposed framework optimizes over a set of ranking functions (measuring the strength of the presence of attributes) simultaneously and dependently on each other. The proposed pairwise queries take the form of which one of these two pictures is more natural? These queries can be easily answered by humans. Extensive empirical study on real image data sets shows that our proposed method, compared with several state-of-the-art methods, achieves superior retrieval performance while requires significantly less human inputs.

Pedophiles' ratings of adult and child photographs using a semantic differential.

PubMed

Hambridge, J A

1994-03-01

Recent knowledge of the widespread extent of child sexual abuse and its consequences has led to an increasing interest in the understanding and treatment of perpetrators. This study examined a group of pedophiles in an English Special Hospital to determine possible characteristics that make children attractive and adults unattractive to them, using Repertory Grid technique and Semantic Differential. A small number of significant findings suggest that pedophiles may perceive some children and women in an unusual way. The large number of insignificant findings are discussed with reference to the sensitivity of the instrument; the pedophiles' desire to give socially acceptable answers; and subject characteristics of low IQ and "psychopathic disorder." Future directions for research are suggested.

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

Rios Velazquez, E; Parmar, C; Narayan, V

Purpose: To compare the complementary value of quantitative radiomic features to that of radiologist-annotated semantic features in predicting EGFR mutations in lung adenocarcinomas. Methods: Pre-operative CT images of 258 lung adenocarcinoma patients were available. Tumors were segmented using the sing-click ensemble segmentation algorithm. A set of radiomic features was extracted using 3D-Slicer. Test-retest reproducibility and unsupervised dimensionality reduction were applied to select a subset of reproducible and independent radiomic features. Twenty semantic annotations were scored by an expert radiologist, describing the tumor, surrounding tissue and associated findings. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative radiomic and semantic featuresmore » in 172 patients (training-set, temporal split). Radiomic, semantic and combined radiomic-semantic logistic regression models to predict EGFR mutations were evaluated in and independent validation dataset of 86 patients using the area under the receiver operating curve (AUC). Results: EGFR mutations were found in 77/172 (45%) and 39/86 (45%) of the training and validation sets, respectively. Univariate AUCs showed a similar range for both feature types: radiomics median AUC = 0.57 (range: 0.50 – 0.62); semantic median AUC = 0.53 (range: 0.50 – 0.64, Wilcoxon p = 0.55). After MRMR feature selection, the best-performing radiomic, semantic, and radiomic-semantic logistic regression models, for EGFR mutations, showed a validation AUC of 0.56 (p = 0.29), 0.63 (p = 0.063) and 0.67 (p = 0.004), respectively. Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative radiologist annotations. The prognostic value of informative qualitative semantic features such as cavitation and lobulation is increased with the addition of quantitative textural features from the tumor region.« less

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.

Making Semantic Waves: A Key to Cumulative Knowledge-Building

ERIC Educational Resources Information Center

Maton, Karl

2013-01-01

The paper begins by arguing that knowledge-blindness in educational research represents a serious obstacle to understanding knowledge-building. It then offers sociological concepts from Legitimation Code Theory--"semantic gravity" and "semantic density"--that systematically conceptualize one set of organizing principles underlying knowledge…

Mechanisms of masked evaluative priming: task sets modulate behavioral and electrophysiological priming for picture and words differentially.

PubMed

Kiefer, Markus; Liegel, Nathalie; Zovko, Monika; Wentura, Dirk

2017-04-01

Research with the evaluative priming paradigm has shown that affective evaluation processes reliably influence cognition and behavior, even when triggered outside awareness. However, the precise mechanisms underlying such subliminal evaluative priming effects, response activation vs semantic processing, are matter of a debate. In this study, we determined the relative contribution of semantic processing and response activation to masked evaluative priming with pictures and words. To this end, we investigated the modulation of masked pictorial vs verbal priming by previously activated perceptual vs semantic task sets and assessed the electrophysiological correlates of priming using event-related potential (ERP) recordings. Behavioral and electrophysiological effects showed a differential modulation of pictorial and verbal subliminal priming by previously activated task sets: Pictorial priming was only observed during the perceptual but not during the semantic task set. Verbal priming, in contrast, was found when either task set was activated. Furthermore, only verbal priming was associated with a modulation of the N400 ERP component, an index of semantic processing, whereas a priming-related modulation of earlier ERPs, indexing visuo-motor S-R activation, was found for both picture and words. The results thus demonstrate that different neuro-cognitive processes contribute to unconscious evaluative priming depending on the stimulus format. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Effects of perceptual similarity but not semantic association on false recognition in aging

PubMed Central

Gill, Emma

2017-01-01

This study investigated semantic and perceptual influences on false recognition in older and young adults in a variant on the Deese-Roediger-McDermott paradigm. In two experiments, participants encoded intermixed sets of semantically associated words, and sets of unrelated words. Each set was presented in a shared distinctive font. Older adults were no more likely to falsely recognize semantically associated lure words compared to unrelated lures also presented in studied fonts. However, they showed an increase in false recognition of lures which were related to studied items only by a shared font. This increased false recognition was associated with recollective experience. The data show that older adults do not always rely more on prior knowledge in episodic memory tasks. They converge with other findings suggesting that older adults may also be more prone to perceptually-driven errors. PMID:29302398

Lexical and Sublexical Semantic Preview Benefits in Chinese Reading

ERIC Educational Resources Information Center

Yan, Ming; Zhou, Wei; Shu, Hua; Kliegl, Reinhold

2012-01-01

Semantic processing from parafoveal words is an elusive phenomenon in alphabetic languages, but it has been demonstrated only for a restricted set of noncompound Chinese characters. Using the gaze-contingent boundary paradigm, this experiment examined whether parafoveal lexical and sublexical semantic information was extracted from compound…

Determining the semantic similarities among Gene Ontology terms.

PubMed

Taha, Kamal

2013-05-01

We present in this paper novel techniques that determine the semantic relationships among GeneOntology (GO) terms. We implemented these techniques in a prototype system called GoSE, which resides between user application and GO database. Given a set S of GO terms, GoSE would return another set S' of GO terms, where each term in S' is semantically related to each term in S. Most current research is focused on determining the semantic similarities among GO ontology terms based solely on their IDs and proximity to one another in the GO graph structure, while overlooking the contexts of the terms, which may lead to erroneous results. The context of a GO term T is the set of other terms, whose existence in the GO graph structure is dependent on T. We propose novel techniques that determine the contexts of terms based on the concept of existence dependency. We present a stack-based sort-merge algorithm employing these techniques for determining the semantic similarities among GO terms.We evaluated GoSE experimentally and compared it with three existing methods. The results of measuring the semantic similarities among genes in KEGG and Pfam pathways retrieved from the DBGET and Sanger Pfam databases, respectively, have shown that our method outperforms the other three methods in recall and precision.

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.

WebGIS based on semantic grid model and web services

NASA Astrophysics Data System (ADS)

Zhang, WangFei; Yue, CaiRong; Gao, JianGuo

2009-10-01

As the combination point of the network technology and GIS technology, WebGIS has got the fast development in recent years. With the restriction of Web and the characteristics of GIS, traditional WebGIS has some prominent problems existing in development. For example, it can't accomplish the interoperability of heterogeneous spatial databases; it can't accomplish the data access of cross-platform. With the appearance of Web Service and Grid technology, there appeared great change in field of WebGIS. Web Service provided an interface which can give information of different site the ability of data sharing and inter communication. The goal of Grid technology was to make the internet to a large and super computer, with this computer we can efficiently implement the overall sharing of computing resources, storage resource, data resource, information resource, knowledge resources and experts resources. But to WebGIS, we only implement the physically connection of data and information and these is far from the enough. Because of the different understanding of the world, following different professional regulations, different policies and different habits, the experts in different field will get different end when they observed the same geographic phenomenon and the semantic heterogeneity produced. Since these there are large differences to the same concept in different field. If we use the WebGIS without considering of the semantic heterogeneity, we will answer the questions users proposed wrongly or we can't answer the questions users proposed. To solve this problem, this paper put forward and experienced an effective method of combing semantic grid and Web Services technology to develop WebGIS. In this paper, we studied the method to construct ontology and the method to combine Grid technology and Web Services and with the detailed analysis of computing characteristics and application model in the distribution of data, we designed the WebGIS query system driven by ontology based on Grid technology and Web Services.

Facilitation and Interference in Identification of Pictures and Words

DTIC Science & Technology

1994-10-05

semantic activation and episodic memory encoding. Journal of Verbal Learning and Verbal Behavior, 22, 88-104. Becker, C. A. (1979). Semantic context...set of items, such as pictures of common objects or known words, which have representations in semantic memory . To test this, we compared the...activation model in particular because nonwords have no memorial representation in semantic memory and thus cannot interfere with ore another. 2. Long-term

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…

The semantic planetary data system

NASA Technical Reports Server (NTRS)

Hughes, J. Steven; Crichton, Daniel; Kelly, Sean; Mattmann, Chris

2005-01-01

This paper will provide a brief overview of the PDS data model and the PDS catalog. It will then describe the implentation of the Semantic PDS including the development of the formal ontology, the generation of RDFS/XML and RDF/XML data sets, and the buiding of the semantic search application.

The Grammar of Mental Predicates in Japanese.

ERIC Educational Resources Information Center

Onishi, Masayuki

1997-01-01

Examines Japanese equivalents of the six mental predicates defined as semantic universals in Natural Semantic Metalanguage theory, with special attention to syntax and semantics of complementation types. It is shown that each primitive predicate has a specific set of syntactic frames for expressing primitive meaning and that extended meanings that…

Social Networking on the Semantic Web

ERIC Educational Resources Information Center

Finin, Tim; Ding, Li; Zhou, Lina; Joshi, Anupam

2005-01-01

Purpose: Aims to investigate the way that the semantic web is being used to represent and process social network information. Design/methodology/approach: The Swoogle semantic web search engine was used to construct several large data sets of Resource Description Framework (RDF) documents with social network information that were encoded using the…

An Ontology-based Context-aware System for Smart Homes: E-care@home.

PubMed

Alirezaie, Marjan; Renoux, Jennifer; Köckemann, Uwe; Kristoffersson, Annica; Karlsson, Lars; Blomqvist, Eva; Tsiftes, Nicolas; Voigt, Thiemo; Loutfi, Amy

2017-07-06

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

An Ontology-based Context-aware System for Smart Homes: E-care@home

PubMed Central

Alirezaie, Marjan; Köckemann, Uwe; Kristoffersson, Annica; Karlsson, Lars; Blomqvist, Eva; Voigt, Thiemo; Loutfi, Amy

2017-01-01

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home. PMID:28684686

A path-oriented knowledge representation system: Defusing the combinatorial system

NASA Technical Reports Server (NTRS)

Karamouzis, Stamos T.; Barry, John S.; Smith, Steven L.; Feyock, Stefan

1995-01-01

LIMAP is a programming system oriented toward efficient information manipulation over fixed finite domains, and quantification over paths and predicates. A generalization of Warshall's Algorithm to precompute paths in a sparse matrix representation of semantic nets is employed to allow questions involving paths between components to be posed and answered easily. LIMAP's ability to cache all paths between two components in a matrix cell proved to be a computational obstacle, however, when the semantic net grew to realistic size. The present paper describes a means of mitigating this combinatorial explosion to an extent that makes the use of the LIMAP representation feasible for problems of significant size. The technique we describe radically reduces the size of the search space in which LIMAP must operate; semantic nets of more than 500 nodes have been attacked successfully. Furthermore, it appears that the procedure described is applicable not only to LIMAP, but to a number of other combinatorially explosive search space problems found in AI as well.

Cooperative answers in database systems

NASA Technical Reports Server (NTRS)

Gaasterland, Terry; Godfrey, Parke; Minker, Jack; Novik, Lev

1993-01-01

A major concern of researchers who seek to improve human-computer communication involves how to move beyond literal interpretations of queries to a level of responsiveness that takes the user's misconceptions, expectations, desires, and interests into consideration. At Maryland, we are investigating how to better meet a user's needs within the framework of the cooperative answering system of Gal and Minker. We have been exploring how to use semantic information about the database to formulate coherent and informative answers. The work has two main thrusts: (1) the construction of a logic formula which embodies the content of a cooperative answer; and (2) the presentation of the logic formula to the user in a natural language form. The information that is available in a deductive database system for building cooperative answers includes integrity constraints, user constraints, the search tree for answers to the query, and false presuppositions that are present in the query. The basic cooperative answering theory of Gal and Minker forms the foundation of a cooperative answering system that integrates the new construction and presentation methods. This paper provides an overview of the cooperative answering strategies used in the CARMIN cooperative answering system, an ongoing research effort at Maryland. Section 2 gives some useful background definitions. Section 3 describes techniques for collecting cooperative logical formulae. Section 4 discusses which natural language generation techniques are useful for presenting the logic formula in natural language text. Section 5 presents a diagram of the system.

SPARQLog: SPARQL with Rules and Quantification

NASA Astrophysics Data System (ADS)

Bry, François; Furche, Tim; Marnette, Bruno; Ley, Clemens; Linse, Benedikt; Poppe, Olga

SPARQL has become the gold-standard for RDF query languages. Nevertheless, we believe there is further room for improving RDF query languages. In this chapter, we investigate the addition of rules and quantifier alternation to SPARQL. That extension, called SPARQLog, extends previous RDF query languages by arbitrary quantifier alternation: blank nodes may occur in the scope of all, some, or none of the universal variables of a rule. In addition, SPARQLog is aware of important RDF features such as the distinction between blank nodes, literals and IRIs or the RDFS vocabulary. The semantics of SPARQLog is closed (every answer is an RDF graph), but lifts RDF's restrictions on literal and blank node occurrences for intermediary data. We show how to define a sound and complete operational semantics that can be implemented using existing logic programming techniques. While SPARQLog is Turing complete, we identify a decidable (in fact, polynomial time) fragment SwARQLog ensuring polynomial data-complexity inspired from the notion of super-weak acyclicity in data exchange. Furthermore, we prove that SPARQLog with no universal quantifiers in the scope of existential ones (∀ ∃ fragment) is equivalent to full SPARQLog in presence of graph projection. Thus, the convenience of arbitrary quantifier alternation comes, in fact, for free. These results, though here presented in the context of RDF querying, apply similarly also in the more general setting of data exchange.

A Query Integrator and Manager for the Query Web

PubMed Central

Brinkley, James F.; Detwiler, Landon T.

2012-01-01

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions. PMID:22531831

Modelling Metamorphism by Abstract Interpretation

NASA Astrophysics Data System (ADS)

Dalla Preda, Mila; Giacobazzi, Roberto; Debray, Saumya; Coogan, Kevin; Townsend, Gregg M.

Metamorphic malware apply semantics-preserving transformations to their own code in order to foil detection systems based on signature matching. In this paper we consider the problem of automatically extract metamorphic signatures from these malware. We introduce a semantics for self-modifying code, later called phase semantics, and prove its correctness by showing that it is an abstract interpretation of the standard trace semantics. Phase semantics precisely models the metamorphic code behavior by providing a set of traces of programs which correspond to the possible evolutions of the metamorphic code during execution. We show that metamorphic signatures can be automatically extracted by abstract interpretation of the phase semantics, and that regular metamorphism can be modelled as finite state automata abstraction of the phase semantics.

Querying XML Data with SPARQL

NASA Astrophysics Data System (ADS)

Bikakis, Nikos; Gioldasis, Nektarios; Tsinaraki, Chrisa; Christodoulakis, Stavros

SPARQL is today the standard access language for Semantic Web data. In the recent years XML databases have also acquired industrial importance due to the widespread applicability of XML in the Web. In this paper we present a framework that bridges the heterogeneity gap and creates an interoperable environment where SPARQL queries are used to access XML databases. Our approach assumes that fairly generic mappings between ontology constructs and XML Schema constructs have been automatically derived or manually specified. The mappings are used to automatically translate SPARQL queries to semantically equivalent XQuery queries which are used to access the XML databases. We present the algorithms and the implementation of SPARQL2XQuery framework, which is used for answering SPARQL queries over XML databases.

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.

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.

Semantic-gap-oriented active learning for multilabel image annotation.

PubMed

Tang, Jinhui; Zha, Zheng-Jun; Tao, Dacheng; Chua, Tat-Seng

2012-04-01

User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivation of user feedback: to tackle the semantic gap. The size of the semantic gap of each concept is an important factor that affects the performance of user feedback. User should pay more efforts to the concepts with large semantic gaps, and vice versa. In this paper, we propose a semantic-gap-oriented active learning method, which incorporates the semantic gap measure into the information-minimization-based sample selection strategy. The basic learning model used in the active learning framework is an extended multilabel version of the sparse-graph-based semisupervised learning method that incorporates the semantic correlation. Extensive experiments conducted on two benchmark image data sets demonstrated the importance of bringing the semantic gap measure into the active learning process.

An Attribute-Treatment Interaction Study: Lexical-Set versus Semantically-Unrelated Vocabulary Instruction

ERIC Educational Resources Information Center

Hashemi, Mohammad Reza; Gowdasiaei, Farah

2005-01-01

The purpose of the current study was (a) to assess the effectiveness of the lexical-set (LS) and the semantically-unrelated (SU) vocabulary instruction, separately and relative to each other, and (b) to assess the differential effects of the two methods for students of lower and upper English proficiency levels. Two intact EFL classes were…

Does Learning to Count Involve a Semantic Induction?

ERIC Educational Resources Information Center

Davidson, Kathryn; Eng, Kortney; Barner, David

2012-01-01

We tested the hypothesis that, when children learn to correctly count sets, they make a semantic induction about the meanings of their number words. We tested the logical understanding of number words in 84 children that were classified as "cardinal-principle knowers" by the criteria set forth by Wynn (1992). Results show that these children often…

Teaching Vocabulary to Turkish Young Learners in Semantically Related and Semantically Unrelated Sets by Using Digital Storytelling

ERIC Educational Resources Information Center

Aitkuzhinova-Arslan, Ainur; Gün, Süleyman; Üstünel, Eda

2016-01-01

Teaching vocabulary is a comprehensive process in foreign language learning requiring specific techniques of appropriate instruction and accurate strategy. The present study was conducted to examine the effects of teaching vocabulary to Turkish young learners in a semantic clustering way through digital storytelling. To investigate this aim, six…

Adapting to Conversation with Semantic Dementia: Using Enactment as a Compensatory Strategy in Everyday Social Interaction

ERIC Educational Resources Information Center

Kindell, Jacqueline; Sage, Karen; Keady, John; Wilkinson, Ray

2013-01-01

Background: Studies to date in semantic dementia have examined communication in clinical or experimental settings. There is a paucity of research describing the everyday interactional skills and difficulties seen in this condition. Aims: To examine the everyday conversation, at home, of an individual with semantic dementia. Methods &…

Answer Sets in a Fuzzy Equilibrium Logic

NASA Astrophysics Data System (ADS)

Schockaert, Steven; Janssen, Jeroen; Vermeir, Dirk; de Cock, Martine

Since its introduction, answer set programming has been generalized in many directions, to cater to the needs of real-world applications. As one of the most general “classical” approaches, answer sets of arbitrary propositional theories can be defined as models in the equilibrium logic of Pearce. Fuzzy answer set programming, on the other hand, extends answer set programming with the capability of modeling continuous systems. In this paper, we combine the expressiveness of both approaches, and define answer sets of arbitrary fuzzy propositional theories as models in a fuzzification of equilibrium logic. We show that the resulting notion of answer set is compatible with existing definitions, when the syntactic restrictions of the corresponding approaches are met. We furthermore locate the complexity of the main reasoning tasks at the second level of the polynomial hierarchy. Finally, as an illustration of its modeling power, we show how fuzzy equilibrium logic can be used to find strong Nash equilibria.

Modeling and mining term association for improving biomedical information retrieval performance.

PubMed

Hu, Qinmin; Huang, Jimmy Xiangji; Hu, Xiaohua

2012-06-11

The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.

Modeling and mining term association for improving biomedical information retrieval performance

PubMed Central

2012-01-01

Background The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. Results We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. Conclusions First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages. PMID:22901087

Using the Weighted Keyword Model to Improve Information Retrieval for Answering Biomedical Questions

PubMed Central

Yu, Hong; Cao, Yong-gang

2009-01-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data. PMID:21347188

Using the weighted keyword model to improve information retrieval for answering biomedical questions.

PubMed

Yu, Hong; Cao, Yong-Gang

2009-03-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data.

SemanticOrganizer: A Customizable Semantic Repository for Distributed NASA Project Teams

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Berrios, Daniel C.; Carvalho, Robert E.; Hall, David R.; Rich, Stephen J.; Sturken, Ian B.; Swanson, Keith J.; Wolfe, Shawn R.

2004-01-01

SemanticOrganizer is a collaborative knowledge management system designed to support distributed NASA projects, including diverse teams of scientists, engineers, and accident investigators. The system provides a customizable, semantically structured information repository that stores work products relevant to multiple projects of differing types. SemanticOrganizer is one of the earliest and largest semantic web applications deployed at NASA to date, and has been used in diverse contexts ranging from the investigation of Space Shuttle Columbia's accident to the search for life on other planets. Although the underlying repository employs a single unified ontology, access control and ontology customization mechanisms make the repository contents appear different for each project team. This paper describes SemanticOrganizer, its customization facilities, and a sampling of its applications. The paper also summarizes some key lessons learned from building and fielding a successful semantic web application across a wide-ranging set of domains with diverse users.

Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans

PubMed Central

Si, Guangsen; Xu, Zeshui

2018-01-01

Hesitant fuzzy linguistic term set provides an effective tool to represent uncertain decision information. However, the semantics corresponding to the linguistic terms in it cannot accurately reflect the decision-makers’ subjective cognition. In general, different decision-makers’ sensitivities towards the semantics are different. Such sensitivities can be represented by the cumulative prospect theory value function. Inspired by this, we propose a linguistic scale function to transform the semantics corresponding to linguistic terms into the linguistic preference values. Furthermore, we propose the hesitant fuzzy linguistic preference utility set, based on which, the decision-makers can flexibly express their distinct semantics and obtain the decision results that are consistent with their cognition. For calculations and comparisons over the hesitant fuzzy linguistic preference utility sets, we introduce some distance measures and comparison laws. Afterwards, to apply the hesitant fuzzy linguistic preference utility sets in emergency management, we develop a method to obtain objective weights of attributes and then propose a hesitant fuzzy linguistic preference utility-TOPSIS method to select the best fire rescue plan. Finally, the validity of the proposed method is verified by some comparisons of the method with other two representative methods including the hesitant fuzzy linguistic-TOPSIS method and the hesitant fuzzy linguistic-VIKOR method. PMID:29614019

Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans.

PubMed

Liao, Huchang; Si, Guangsen; Xu, Zeshui; Fujita, Hamido

2018-04-03

Hesitant fuzzy linguistic term set provides an effective tool to represent uncertain decision information. However, the semantics corresponding to the linguistic terms in it cannot accurately reflect the decision-makers' subjective cognition. In general, different decision-makers' sensitivities towards the semantics are different. Such sensitivities can be represented by the cumulative prospect theory value function. Inspired by this, we propose a linguistic scale function to transform the semantics corresponding to linguistic terms into the linguistic preference values. Furthermore, we propose the hesitant fuzzy linguistic preference utility set, based on which, the decision-makers can flexibly express their distinct semantics and obtain the decision results that are consistent with their cognition. For calculations and comparisons over the hesitant fuzzy linguistic preference utility sets, we introduce some distance measures and comparison laws. Afterwards, to apply the hesitant fuzzy linguistic preference utility sets in emergency management, we develop a method to obtain objective weights of attributes and then propose a hesitant fuzzy linguistic preference utility-TOPSIS method to select the best fire rescue plan. Finally, the validity of the proposed method is verified by some comparisons of the method with other two representative methods including the hesitant fuzzy linguistic-TOPSIS method and the hesitant fuzzy linguistic-VIKOR method.

Theory of mind performance using a story comprehension task in bipolar mania compared to schizophrenia and healthy controls.

PubMed

Rossell, Susan L; Van Rheenen, Tamsyn E

2013-01-01

Theory of mind (ToM) refers to the ability to understand the mental state of self and others. There is limited research into this topic in bipolar disorder (BD), with no previous study examining ToM in a BD group within a psychotic manic phase. Twenty-eight psychotic manic BD patients were compared with 30 schizophrenia (SCZ) patients and 29 healthy controls (HC). Participants performed a ToM story comprehension task that compared ToM stories and non-ToM stories (which we relabelled non-ToM "semantic" stories). Performance was examined by answering comprehension questions. Both patient groups were equally impaired on their scores for ToM stories (scores BD = 10/24, SCZ = 9/24, HC = 14/24, p < .001). Interestingly, both patient groups showed reduced performance on non-ToM semantic stories (scores BD = 12/24, SCZ = 9/24, HC = 15/24, p < .001); SCZ showed a larger deficit. Reduced ToM performance was correlated with delusion severity in the BD group only. ToM performance was impaired in BD patients experiencing psychotic symptoms. Patient performance was also impaired on the control condition (i.e., non-ToM semantic stories) supporting an additional deficit in semantic processing.

Application of a Brazilian test of expressive vocabulary in European Portuguese children.

PubMed

Cáceres-Assenço, Ana Manhani; Ferreira, Sandra Cristina Araújo; Santos, Anabela Cruz; Befi-Lopes, Debora Maria

2018-01-01

Objective to investigate the performance of European Portuguese children in a Brazilian test of expressive vocabulary, seeking to identify differences between age groups and gender, and to verify its applicability in this population. Methods the sample consisted of 150 typical developed children, of both genders, between the ages of 5 and 6. All children attended public schools in the north area of Portugal. To assess the semantic performance, the expressive vocabulary sub-test of the language test (ABFW) was used, considering the percentage of usual verbal assignments and the classification (adequate/inadequate) according to the Brazilian reference values. Results the performance of the European Portuguese children indicated that at age 6 they have a higher percentage of correct answers in expressive vocabulary. As for the gender, there were only occasional differences: the girls showed a greater dominance in the semantic fields of clothing (both ages) and furniture and utensils (at age 5), whereas the boys showed more dominance in the semantic field means of transportation (6 years). Regarding classification, there was no difference between age groups in overall performance. Only the semantic field shapes and colors had more individuals of 6 years with inadequate performance. Conclusion the reference values adopted in the Brazilian population for semantic performance indicated that more than 80% of the children of each age group could have their performance classified as adequate. Such evidence suggests that this tool shows potential as an instrument of quantitative vocabulary's assessment of 5 and 6-years old children in European Portuguese.

Integrated Japanese Dependency Analysis Using a Dialog Context

NASA Astrophysics Data System (ADS)

Ikegaya, Yuki; Noguchi, Yasuhiro; Kogure, Satoru; Itoh, Toshihiko; Konishi, Tatsuhiro; Kondo, Makoto; Asoh, Hideki; Takagi, Akira; Itoh, Yukihiro

This paper describes how to perform syntactic parsing and semantic analysis in a dialog system. The paper especially deals with how to disambiguate potentially ambiguous sentences using the contextual information. Although syntactic parsing and semantic analysis are often studied independently of each other, correct parsing of a sentence often requires the semantic information on the input and/or the contextual information prior to the input. Accordingly, we merge syntactic parsing with semantic analysis, which enables syntactic parsing taking advantage of the semantic content of an input and its context. One of the biggest problems of semantic analysis is how to interpret dependency structures. We employ a framework for semantic representations that circumvents the problem. Within the framework, the meaning of any predicate is converted into a semantic representation which only permits a single type of predicate: an identifying predicate "aru". The semantic representations are expressed as sets of "attribute-value" pairs, and those semantic representations are stored in the context information. Our system disambiguates syntactic/semantic ambiguities of inputs referring to the attribute-value pairs in the context information. We have experimentally confirmed the effectiveness of our approach; specifically, the experiment confirmed high accuracy of parsing and correctness of generated semantic representations.

Issues in Semantic Memory: A Response to Glass and Holyoak. Technical Report No. 101.

ERIC Educational Resources Information Center

Shoben, Edward J.; And Others

Glass and Holyoak (1975) have raised two issues related to the distinction between set-theoretic and network theories of semantic memory, contending that: (a) their version of a network theory, the Marker Search model, is conceptually and empirically superior to the Feature Comparison model version of a set-theoretic theory; and (b) the contrast…

Contributions of Response Set and Semantic Relatedness to Cross-Modal Stroop-Like Picture--Word Interference in Children and Adults

ERIC Educational Resources Information Center

Hanauer, John B.; Brooks, Patricia J.

2005-01-01

Resistance to interference from irrelevant auditory stimuli undergoes development throughout childhood. To test whether semantic processes account for age-related changes in a Stroop-like picture-word interference effect, children (3-to 12-year-olds) and adults named pictures while listening to words varying in terms of semantic relatedness to the…

Semantically-Sensitive Macroprocessing

DTIC Science & Technology

1989-12-15

constr uct for protecting critical regions. Given the synchronization primitives P and V, we might implement the following transformation, where...By this we mean that the semantic model for the base language provides a primitive set of concepts, represented by data types and operations...the gener- ation of a (dynamic-) semantically equivalent program fragment ultimately expressible in terms of built-in primitives . Note that static

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

Developing Visualization Techniques for Semantics-based Information Networks

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Hall, David R.

2003-01-01

Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches

PubMed Central

Wang, Yanshan; Shen, Feichen; Liu, Sijia; Rastegar-Mojarad, Majid; Wang, Liwei

2017-01-01

Background Self-management is crucial to diabetes care and providing expert-vetted content for answering patients’ questions is crucial in facilitating patient self-management. Objective The aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients. Methods We compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum. The evaluation was based on a gold standard dataset consisting of 50 randomly selected diabetic questions where the relevancy of diabetic education materials to the questions was manually assigned by two experts. The performance was assessed using precision of top-ranked documents. Results We retrieved 7510 diabetic questions on the forum and 144 diabetic patient educational materials from the patient education database at Mayo Clinic. The mapping rate of words in each corpus mapped to the Unified Medical Language System (UMLS) was significantly different (P<.001). The topic modeling-based model outperformed the other retrieval algorithms. For example, for the top-retrieved document, the precision of the topic modeling-based, semantic group-based, and VSM models was 67.0%, 62.8%, and 54.3%, respectively. Conclusions This study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients’ questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums. PMID:29038097

Tracking neural coding of perceptual and semantic features of concrete nouns

PubMed Central

Sudre, Gustavo; Pomerleau, Dean; Palatucci, Mark; Wehbe, Leila; Fyshe, Alona; Salmelin, Riitta; Mitchell, Tom

2015-01-01

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. PMID:22565201

A Deep Learning-Based Method for Similar Patient Question Retrieval in Chinese.

PubMed

Tang, Guo Yu; Ni, Yuan; Xie, Guo Tong; Fan, Xin Li; Shi, Yan Ling

2017-01-01

The online patient question and answering (Q&A) system, either as a website or a mobile application, attracts an increasing number of users in China. Patients will post their questions and the registered doctors then provide the corresponding answers. A large amount of questions with answers from doctors are accumulated. Instead of awaiting the response from a doctor, the newly posted question could be quickly answered by finding a semantically equivalent question from the Q&A achive. In this study, we investigated a novel deep learning based method to retrieve the similar patient question in Chinese. An unsupervised learning algorithm using deep neural network is performed on the corpus to generate the word embedding. The word embedding was then used as the input to a supervised learning algorithm using a designed deep neural network, i.e. the supervised neural attention model (SNA), to predict the similarity between two questions. The experimental results showed that our SNA method achieved P@1 = 77% and P@5 = 84%, which outperformed all other compared methods.

Lexical Semantic Field as One of the Keys to Second Language Teaching

ERIC Educational Resources Information Center

Varlamova, Elena V.; Tulusina, Elena A.; Zaripova, Zarema M.; Gataullina, Veronika L.

2017-01-01

The article is devoted to the problem of the development of skills connected with the acquisition of foreign lexis (Lexis = all possible words or phrases in a language) on the basis of semantic fields (Semantic field = a lexical set of related items, e.g., colour, red, green, blue). This becomes possible due to grouping well-known and unknown to…

An Investigation of Two Instructional Settings in the Use of Semantic Mapping with Poor Readers. Program Report 85-4.

ERIC Educational Resources Information Center

Pittelman, Susan D.; And Others

A study investigated whether semantic mapping is more effective for poor readers instructed in a small group of poor readers or in a class of students with mixed reading abilities. Students in five fourth-grade classes served as the control, receiving no semantic mapping instruction. Subjects, 39 fourth-grade poor readers, were presented semantic…

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

The impact of semantic impairment on word stem completion in Alzheimer's disease.

PubMed

Beauregard, M; Chertkow, H; Gold, D; Bergman, S

2001-01-01

Both the extent of semantic memory impairment and the level of processing attained during encoding might constitute critical factors in determining the amount of word-stem completion (WSC) priming encountered in Alzheimer's disease (AD) subjects. We investigated the impact of varying encoding level in AD and elderly normal subjects, using a set of stimuli ranked as "intact" or "degraded" in terms of each subject's semantic knowledge on probe questions. For both shallow and deep encoding conditions, overall priming in the two subject groups was equivalent. However, for the deep encoding condition, consisting of a semantic judgment task performed on each target word, the priming effect noted in AD subjects was significantly smaller for semantically degraded items than for semantically intact items. Results indicate that the degree of semantic impairment represents one important variable affecting the amount of WSC priming which results when deep encoding procedures are used at study.

A Formal Theory for Modular ERDF Ontologies

NASA Astrophysics Data System (ADS)

Analyti, Anastasia; Antoniou, Grigoris; Damásio, Carlos Viegas

The success of the Semantic Web is impossible without any form of modularity, encapsulation, and access control. In an earlier paper, we extended RDF graphs with weak and strong negation, as well as derivation rules. The ERDF #n-stable model semantics of the extended RDF framework (ERDF) is defined, extending RDF(S) semantics. In this paper, we propose a framework for modular ERDF ontologies, called modular ERDF framework, which enables collaborative reasoning over a set of ERDF ontologies, while support for hidden knowledge is also provided. In particular, the modular ERDF stable model semantics of modular ERDF ontologies is defined, extending the ERDF #n-stable model semantics. Our proposed framework supports local semantics and different points of view, local closed-world and open-world assumptions, and scoped negation-as-failure. Several complexity results are provided.

A computational modeling of semantic knowledge in reading comprehension: Integrating the landscape model with latent semantic analysis.

PubMed

Yeari, Menahem; van den Broek, Paul

2016-09-01

It is a well-accepted view that the prior semantic (general) knowledge that readers possess plays a central role in reading comprehension. Nevertheless, computational models of reading comprehension have not integrated the simulation of semantic knowledge and online comprehension processes under a unified mathematical algorithm. The present article introduces a computational model that integrates the landscape model of comprehension processes with latent semantic analysis representation of semantic knowledge. In three sets of simulations of previous behavioral findings, the integrated model successfully simulated the activation and attenuation of predictive and bridging inferences during reading, as well as centrality estimations and recall of textual information after reading. Analyses of the computational results revealed new theoretical insights regarding the underlying mechanisms of the various comprehension phenomena.

Cross border semantic interoperability for clinical research: the EHR4CR semantic resources and services

PubMed Central

Daniel, Christel; Ouagne, David; Sadou, Eric; Forsberg, Kerstin; Gilchrist, Mark Mc; Zapletal, Eric; Paris, Nicolas; Hussain, Sajjad; Jaulent, Marie-Christine; MD, Dipka Kalra

2016-01-01

With the development of platforms enabling the use of routinely collected clinical data in the context of international clinical research, scalable solutions for cross border semantic interoperability need to be developed. Within the context of the IMI EHR4CR project, we first defined the requirements and evaluation criteria of the EHR4CR semantic interoperability platform and then developed the semantic resources and supportive services and tooling to assist hospital sites in standardizing their data for allowing the execution of the project use cases. The experience gained from the evaluation of the EHR4CR platform accessing to semantically equivalent data elements across 11 European participating EHR systems from 5 countries demonstrated how far the mediation model and mapping efforts met the expected requirements of the project. Developers of semantic interoperability platforms are beginning to address a core set of requirements in order to reach the goal of developing cross border semantic integration of data. PMID:27570649

Priority Intelligence Requirement Answering and Commercial Question-Answering: Identifying the Gaps

DTIC Science & Technology

2010-06-01

systems  Tagged Text  Google Patent Search  Metacarta;  Semantic MediaWiki;  Palantir   Logic Statements  Prolog    5 Powerset (Microsoft Bing);  Cyc...provided  by  companies  including  MetaCarta  and  Palantir .   MetaCarta’s  technology  [ 12]  processes  documents  in  order  to  identify  any...and so on (Figure 4).    10   Figure 4  Palantir  Screenshot  from  Palantir  Tech Blog.   The graph  is  linked  to  the histogram view  that  allows

An RDF/OWL knowledge base for query answering and decision support in clinical pharmacogenetics.

PubMed

Samwald, Matthias; Freimuth, Robert; Luciano, Joanne S; Lin, Simon; Powers, Robert L; Marshall, M Scott; Adlassnig, Klaus-Peter; Dumontier, Michel; Boyce, Richard D

2013-01-01

Genetic testing for personalizing pharmacotherapy is bound to become an important part of clinical routine. To address associated issues with data management and quality, we are creating a semantic knowledge base for clinical pharmacogenetics. The knowledge base is made up of three components: an expressive ontology formalized in the Web Ontology Language (OWL 2 DL), a Resource Description Framework (RDF) model for capturing detailed results of manual annotation of pharmacogenomic information in drug product labels, and an RDF conversion of relevant biomedical datasets. Our work goes beyond the state of the art in that it makes both automated reasoning as well as query answering as simple as possible, and the reasoning capabilities go beyond the capabilities of previously described ontologies.

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.

Exploiting semantic linkages among multiple sources for semantic information retrieval

NASA Astrophysics Data System (ADS)

Li, JianQiang; Yang, Ji-Jiang; Liu, Chunchen; Zhao, Yu; Liu, Bo; Shi, Yuliang

2014-07-01

The vision of the Semantic Web is to build a global Web of machine-readable data to be consumed by intelligent applications. As the first step to make this vision come true, the initiative of linked open data has fostered many novel applications aimed at improving data accessibility in the public Web. Comparably, the enterprise environment is so different from the public Web that most potentially usable business information originates in an unstructured form (typically in free text), which poses a challenge for the adoption of semantic technologies in the enterprise environment. Considering that the business information in a company is highly specific and centred around a set of commonly used concepts, this paper describes a pilot study to migrate the concept of linked data into the development of a domain-specific application, i.e. the vehicle repair support system. The set of commonly used concepts, including the part name of a car and the phenomenon term on the car repairing, are employed to build the linkage between data and documents distributed among different sources, leading to the fusion of documents and data across source boundaries. Then, we describe the approaches of semantic information retrieval to consume these linkages for value creation for companies. The experiments on two real-world data sets show that the proposed approaches outperform the best baseline 6.3-10.8% and 6.4-11.1% in terms of top five and top 10 precisions, respectively. We believe that our pilot study can serve as an important reference for the development of similar semantic applications in an enterprise environment.

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

Privacy Preservation in Context-Aware Systems

DTIC Science & Technology

2011-01-01

Policies and the Semantic Web The Semantic Web refers to both a vision and a set of technologies. The vision was first articulated by Tim Berners - Lee ... Berners - lee 2005) is a distributed framework for describing and reasoning over policies in the Semantic Web. It supports N3 rules ( Berners - Lee ...Connolly 2008), ( Berners - Lee et al. 2005) for representing intercon- nections between policies and resources and uses the CWM forward-chaining reasoning

Semantic transparency in free stems: The effect of Orthography-Semantics Consistency on word recognition.

PubMed

Marelli, Marco; Amenta, Simona; Crepaldi, Davide

2015-01-01

A largely overlooked side effect in most studies of morphological priming is a consistent main effect of semantic transparency across priming conditions. That is, participants are faster at recognizing stems from transparent sets (e.g., farm) in comparison to stems from opaque sets (e.g., fruit), regardless of the preceding primes. This suggests that semantic transparency may also be consistently associated with some property of the stem word. We propose that this property might be traced back to the consistency, throughout the lexicon, between the orthographic form of a word and its meaning, here named Orthography-Semantics Consistency (OSC), and that an imbalance in OSC scores might explain the "stem transparency" effect. We exploited distributional semantic models to quantitatively characterize OSC, and tested its effect on visual word identification relying on large-scale data taken from the British Lexicon Project (BLP). Results indicated that (a) the "stem transparency" effect is solid and reliable, insofar as it holds in BLP lexical decision times (Experiment 1); (b) an imbalance in terms of OSC can account for it (Experiment 2); and (c) more generally, OSC explains variance in a large item sample from the BLP, proving to be an effective predictor in visual word access (Experiment 3).

Exploring supervised and unsupervised methods to detect topics in biomedical text

PubMed Central

Lee, Minsuk; Wang, Weiqing; Yu, Hong

2006-01-01

Background Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. Results We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Conclusion Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. PMID:16539745

Investigating the high school students' cognitive structures about the work concept

NASA Astrophysics Data System (ADS)

Tavukçuoǧlu, Erdem; Özcan, Özgür

2018-02-01

The purpose of this study is to determine the high school students' cognitive structures related to the concepts of work. The participants of the study were composed of the students enrolled in 11. and 12. class of an Anatolian high school in Turkey. The data were collected via word association test related to the key concept to determine the students' cognitive structures. The collected data were analyzed according to the content analysis method. In the data analysis process, we determined firstly the number of words, the number of answers and the semantic relations between the words written by students. The words having semantic connections were grouped under the same category. Thus, at the end of the study, high school students' cognitive structures and some alternative conceptions were determined related to concepts of work.

No one way ticket from orthography to semantics in recognition memory: N400 and P200 effects of associations.

PubMed

Stuellein, Nicole; Radach, Ralph R; Jacobs, Arthur M; Hofmann, Markus J

2016-05-15

Computational models of word recognition already successfully used associative spreading from orthographic to semantic levels to account for false memories. But can they also account for semantic effects on event-related potentials in a recognition memory task? To address this question, target words in the present study had either many or few semantic associates in the stimulus set. We found larger P200 amplitudes and smaller N400 amplitudes for old words in comparison to new words. Words with many semantic associates led to larger P200 amplitudes and a smaller N400 in comparison to words with a smaller number of semantic associations. We also obtained inverted response time and accuracy effects for old and new words: faster response times and fewer errors were found for old words that had many semantic associates, whereas new words with a large number of semantic associates produced slower response times and more errors. Both behavioral and electrophysiological results indicate that semantic associations between words can facilitate top-down driven lexical access and semantic integration in recognition memory. Our results support neurophysiologically plausible predictions of the Associative Read-Out Model, which suggests top-down connections from semantic to orthographic layers. Copyright © 2016 Elsevier B.V. All rights reserved.

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 attributes are encoded in human electrocorticographic signals during visual object recognition.

PubMed

Rupp, Kyle; Roos, Matthew; Milsap, Griffin; Caceres, Carlos; Ratto, Christopher; Chevillet, Mark; Crone, Nathan E; Wolmetz, Michael

2017-03-01

Non-invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non-invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high-dimensional encoding models to map semantic attributes to spectral-temporal features of the task-related neural responses. Using these semantic attribute encoding models, untrained objects were decoded with accuracies comparable to whole-brain functional Magnetic Resonance Imaging (fMRI), and we observed that high-gamma activity (70-110Hz) at basal occipitotemporal electrodes was associated with specific semantic dimensions (manmade-animate, canonically large-small, and places-tools). Individual patient results were in close agreement with reports from other imaging modalities on the time course and functional organization of semantic processing along the ventral visual pathway during object recognition. The semantic attribute encoding model approach is critical for decoding objects absent from a training set, as well as for studying complex semantic encodings without artificially restricting stimuli to a small number of semantic categories. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Visualizing the semantic content of large text databases using text maps

NASA Technical Reports Server (NTRS)

Combs, Nathan

1993-01-01

A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content.

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.

Would you say "yes" in the evening? Time-of-day effect on response bias in four types of working memory recognition tasks.

PubMed

Lewandowska, Koryna; Wachowicz, Barbara; Marek, Tadeusz; Oginska, Halszka; Fafrowicz, Magdalena

2018-01-01

Across a wide range of tasks, cognitive functioning is affected by circadian fluctuations. In this study, we investigated diurnal variations of working memory performance, taking into account not only hits and errors rates, but also sensitivity (d') and response bias (c) indexes (established by signal detection theory). Fifty-two healthy volunteers performed four experimental tasks twice - in the morning and in the evening (approximately 1 and 10 h after awakening). All tasks were based on Deese-Roediger-McDermott paradigm version dedicated to study working/short-term memory distortions. Participants were to memorize sets of stimuli characterized by either conceptual or perceptual similarity, and to answer if they recognized subsequent stimulus (probe) as an "old" one (i.e. presented in the preceding memory set). The probe was of three types: positive, negative or related lure. In two verbal tasks, memory sets were characterized by semantic or phonological similarity. In two visual tasks, abstract objects were characterized by a number of overlapping similarities or differed in only one detail. The type of experimental material and the participants' diurnal preference were taken into account. The analysis showed significant effect of time of day on false alarms rate (F (1,50)  = 5.29, p = 0.03, η p 2  = 0.1) and response bias (F (1,50)  = 11.16, p = 0.002, η p 2  = 0.18). In other words, in the evening participants responded in more liberal way than in the morning (answering "yes" more often). As the link between variations in false alarms rate, response bias and locus coeruleus activity was indicated in literature before, we believe that our data may be interpreted as supporting the hypothesis that diurnal fluctuations in norepinephrine release have effect on cognitive functioning in terms of decision threshold.

Progress toward a Semantic eScience Framework; building on advanced cyberinfrastructure

NASA Astrophysics Data System (ADS)

McGuinness, D. L.; Fox, P. A.; West, P.; Rozell, E.; Zednik, S.; Chang, C.

2010-12-01

The configurable and extensible semantic eScience framework (SESF) has begun development and implementation of several semantic application components. Extensions and improvements to several ontologies have been made based on distinct interdisciplinary use cases ranging from solar physics, to biologicl and chemical oceanography. Importantly, these semantic representations mediate access to a diverse set of existing and emerging cyberinfrastructure. Among the advances are the population of triple stores with web accessible query services. A triple store is akin to a relational data store where the basic stored unit is a subject-predicate-object tuple. Access via a query is provided by the W3 Recommendation language specification SPARQL. Upon this middle tier of semantic cyberinfrastructure, we have developed several forms of semantic faceted search, including provenance-awareness. We report on the rapid advances in semantic technologies and tools and how we are sustaining the software path for the required technical advances as well as the ontology improvements and increased functionality of the semantic applications including how they are integrated into web-based portals (e.g. Drupal) and web services. Lastly, we indicate future work direction and opportunities for collaboration.

Visuospatial working memory in children with autism: the effect of a semantic global organization.

PubMed

Mammarella, Irene C; Giofrè, David; Caviola, Sara; Cornoldi, Cesare; Hamilton, Colin

2014-06-01

It has been reported that individuals with Autism Spectrum Disorders (ASD) perceive visual scenes as a sparse set of details rather than as a congruent and meaningful unit, failing in the extraction of the global configuration of the scene. In the present study, children with ASD were compared with typically developing (TD) children, in a visuospatial working memory task, the Visual Patterns Test (VPT). The VPT array was manipulated to vary the semantic affordance of the pattern, high semantic (global) vs. low semantic; temporal parameters were also manipulated within the change detection protocol. Overall, there was no main effect associated with Group, however there was a significant effect associated with Semantics, which was further qualified by an interaction between the Group and Semantic factors; there was only a significant effect of semantics in the TD group. The findings are discussed in light of the weak central coherence theory where the ASD group are unable to make use of long term memory semantics in order to construct global representations of the array. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Setting semantics: conceptual set can determine the physical properties that capture attention.

PubMed

Goodhew, Stephanie C; Kendall, William; Ferber, Susanne; Pratt, Jay

2014-08-01

The ability of a stimulus to capture visuospatial attention depends on the interplay between its bottom-up saliency and its relationship to an observer's top-down control set, such that stimuli capture attention if they match the predefined properties that distinguish a searched-for target from distractors (Folk, Remington, & Johnston, Journal of Experimental Psychology: Human Perception & Performance, 18, 1030-1044 1992). Despite decades of research on this phenomenon, however, the vast majority has focused exclusively on matches based on low-level physical properties. Yet if contingent capture is indeed a "top-down" influence on attention, then semantic content should be accessible and able to determine which physical features capture attention. Here we tested this prediction by examining whether a semantically defined target could create a control set for particular features. To do this, we had participants search to identify a target that was differentiated from distractors by its meaning (e.g., the word "red" among color words all written in black). Before the target array, a cue was presented, and it was varied whether the cue appeared in the physical color implied by the target word. Across three experiments, we found that cues that embodied the meaning of the word produced greater cuing than cues that did not. This suggests that top-down control sets activate content that is semantically associated with the target-defining property, and this content in turn has the ability to exogenously orient attention.

Semantic processing of EHR data for clinical research.

PubMed

Sun, Hong; Depraetere, Kristof; De Roo, Jos; Mels, Giovanni; De Vloed, Boris; Twagirumukiza, Marc; Colaert, Dirk

2015-12-01

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data. Copyright © 2015 Elsevier Inc. All rights reserved.

The effects of self-instruction training on a deaf child's semantic and pragmatic production.

PubMed

Swanson, H L

1987-10-01

Effects of self-instruction training on the communication skills of a profoundly hearing-impaired child were studied. Self-instruction training included modeling a series of problem-solving steps in order to direct communication production. Communication production was operationalized as signed semantic and pragmatic functions. A multiple baseline was used to assess treatment and generalization (treatment variations of person and setting) effects. There was evidence to suggest that self-instruction was immediately effective on pragmatic behaviors but such behaviors were reduced when another person administered treatment. In contrast, self-instruction training had a gradual influence on semantic behaviors and those effects were maintained when treatment included a different person and setting. Implications of the clinical study were discussed.

Fully convolutional network with cluster for semantic segmentation

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

Designing learning management system interoperability in semantic web

NASA Astrophysics Data System (ADS)

Anistyasari, Y.; Sarno, R.; Rochmawati, N.

2018-01-01

The extensive adoption of learning management system (LMS) has set the focus on the interoperability requirement. Interoperability is the ability of different computer systems, applications or services to communicate, share and exchange data, information, and knowledge in a precise, effective and consistent way. Semantic web technology and the use of ontologies are able to provide the required computational semantics and interoperability for the automation of tasks in LMS. The purpose of this study is to design learning management system interoperability in the semantic web which currently has not been investigated deeply. Moodle is utilized to design the interoperability. Several database tables of Moodle are enhanced and some features are added. The semantic web interoperability is provided by exploited ontology in content materials. The ontology is further utilized as a searching tool to match user’s queries and available courses. It is concluded that LMS interoperability in Semantic Web is possible to be performed.

Teaching Semantic Radicals Facilitates Inferring New Character Meaning in Sentence Reading for Nonnative Chinese Speakers

PubMed Central

Nguyen, Thi Phuong; Zhang, Jie; Li, Hong; Wu, Xinchun; Cheng, Yahua

2017-01-01

This study investigates the effects of teaching semantic radicals in inferring the meanings of unfamiliar characters among nonnative Chinese speakers. A total of 54 undergraduates majoring in Chinese Language from a university in Hanoi, Vietnam, who had 1 year of learning experience in Chinese were assigned to two experimental groups that received instructional intervention, called “old-for-new” semantic radical teaching, through two counterbalanced sets of semantic radicals, with one control group. All of the students completed pre- and post-tests of a sentence cloze task where they were required to choose an appropriate character that fit the sentence context among four options. The four options shared the same phonetic radicals but had different semantic radicals. The results showed that the pre-test and post-test score increases were significant for the experimental groups, but not for the control group. Most importantly, the experimental groups successfully transferred the semantic radical strategy to figure out the meanings of unfamiliar characters containing semantic radicals that had not been taught. The results demonstrate the effectiveness of teaching semantic radicals for lexical inference in sentence reading for nonnative speakers, and highlight the ability of transfer learning to acquire semantic categories of sub-lexical units (semantic radicals) in Chinese characters among foreign language learners. PMID:29109694

Semantic encoding of relational databases in wireless networks

NASA Astrophysics Data System (ADS)

Benjamin, David P.; Walker, Adrian

2005-03-01

Semantic Encoding is a new, patented technology that greatly increases the speed of transmission of distributed databases over networks, especially over ad hoc wireless networks, while providing a novel method of data security. It reduces bandwidth consumption and storage requirements, while speeding up query processing, encryption and computation of digital signatures. We describe the application of Semantic Encoding in a wireless setting and provide an example of its operation in which a compression of 290:1 would be achieved.

Flexible establishment of functional brain networks supports attentional modulation of unconscious cognition.

PubMed

Ulrich, Martin; Adams, Sarah C; Kiefer, Markus

2014-11-01

In classical theories of attention, unconscious automatic processes are thought to be independent of higher-level attentional influences. Here, we propose that unconscious processing depends on attentional enhancement of task-congruent processing pathways implemented by a dynamic modulation of the functional communication between brain regions. Using functional magnetic resonance imaging, we tested our model with a subliminally primed lexical decision task preceded by an induction task preparing either a semantic or a perceptual task set. Subliminal semantic priming was significantly greater after semantic compared to perceptual induction in ventral occipito-temporal (vOT) and inferior frontal cortex, brain areas known to be involved in semantic processing. The functional connectivity pattern of vOT varied depending on the induction task and successfully predicted the magnitude of behavioral and neural priming. Together, these findings support the proposal that dynamic establishment of functional networks by task sets is an important mechanism in the attentional control of unconscious processing. © 2014 Wiley Periodicals, Inc.

Learning Semantic Tags from Big Data for Clinical Text Representation.

PubMed

Li, Yanpeng; Liu, Hongfang

2015-01-01

In clinical text mining, it is one of the biggest challenges to represent medical terminologies and n-gram terms in sparse medical reports using either supervised or unsupervised methods. Addressing this issue, we propose a novel method for word and n-gram representation at semantic level. We first represent each word by its distance with a set of reference features calculated by reference distance estimator (RDE) learned from labeled and unlabeled data, and then generate new features using simple techniques of discretization, random sampling and merging. The new features are a set of binary rules that can be interpreted as semantic tags derived from word and n-grams. We show that the new features significantly outperform classical bag-of-words and n-grams in the task of heart disease risk factor extraction in i2b2 2014 challenge. It is promising to see that semantics tags can be used to replace the original text entirely with even better prediction performance as well as derive new rules beyond lexical level.

A case study of data integration for aquatic resources using semantic web technologies

USGS Publications Warehouse

Gordon, Janice M.; Chkhenkeli, Nina; Govoni, David L.; Lightsom, Frances L.; Ostroff, Andrea C.; Schweitzer, Peter N.; Thongsavanh, Phethala; Varanka, Dalia E.; Zednik, Stephan

2015-01-01

Use cases, information modeling, and linked data techniques are Semantic Web technologies used to develop a prototype system that integrates scientific observations from four independent USGS and cooperator data systems. The techniques were tested with a use case goal of creating a data set for use in exploring potential relationships among freshwater fish populations and environmental factors. The resulting prototype extracts data from the BioData Retrieval System, the Multistate Aquatic Resource Information System, the National Geochemical Survey, and the National Hydrography Dataset. A prototype user interface allows a scientist to select observations from these data systems and combine them into a single data set in RDF format that includes explicitly defined relationships and data definitions. The project was funded by the USGS Community for Data Integration and undertaken by the Community for Data Integration Semantic Web Working Group in order to demonstrate use of Semantic Web technologies by scientists. This allows scientists to simultaneously explore data that are available in multiple, disparate systems beyond those they traditionally have used.

SEMANTIC3D.NET: a New Large-Scale Point Cloud Classification Benchmark

NASA Astrophysics Data System (ADS)

Hackel, T.; Savinov, N.; Ladicky, L.; Wegner, J. D.; Schindler, K.; Pollefeys, M.

2017-05-01

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

Semantic Space as a Metapopulation System: Modelling the Wikipedia Information Flow Network

NASA Astrophysics Data System (ADS)

Masucci, A. Paolo; Kalampokis, Alkiviadis; Eguíluz, Víctor M.; Hernández-García, Emilio

The meaning of a word can be defined as an indefinite set of interpretants, which are other words that circumscribe the semantic content of the word they represent (Derrida 1982). In the same way each interpretant has a set of interpretants representing it and so on. Hence the indefinite chain of meaning assumes a rhizomatic shape that can be represented and analysed via the modern techniques of network theory (Dorogovtsev and Mendes 2013).

Selective deficits in episodic feeling of knowing in ageing: a novel use of the general knowledge task.

PubMed

Morson, Suzannah M; Moulin, Chris J A; Souchay, Céline

2015-05-01

Failure to recall an item from memory can be accompanied by the subjective experience that the item is known but currently unavailable for report. The feeling of knowing (FOK) task allows measurement of the predictive accuracy of this reflective judgement. Young and older adults were asked to provide answers to general knowledge questions both prior to and after learning, thus measuring both semantic and episodic memory for the items. FOK judgements were made at each stage for all unrecalled responses, providing a measure of predictive accuracy for semantic and episodic knowledge. Results demonstrated a selective effect of age on episodic FOK resolution, with older adults found to have impaired episodic FOK accuracy while semantic FOK accuracy remained intact. Although recall and recognition measures of episodic memory are equivalent between the two age groups, older adults may have been unable to access contextual details on which to base their FOK judgements. The results suggest that older adults are not able to accurately predict future recognition of unrecalled episodic information, and consequently may have difficulties in monitoring recently encoded memories. Copyright © 2015. Published by Elsevier B.V.

K 4R - Knowledge to the Power of RESTful, Resourceful and Reactive Rules

NASA Astrophysics Data System (ADS)

Amador, Ricardo

The Web of today clearly answers questions of the form "What is the representation of ...?". The Semantic Web (SW) of tomorrow aims at answering questions of the form "What is the meaning of ...?". It is our stance that in order to realize the full potential of the original concept proposed by Tim Berners-Lee et al. (in Scientific American, May 2001), the SW must also answer, in a meaningful way, questions of a dynamic and active nature, like "What to do if ...?" or "What to do when ...?". Moreover, SW questions of the form "What to do ...?" must be expressed and answered in a declarative, compositional and language agnostic way. It is our (hypo)thesis that formally established concepts, viz. the Web's REST architectural style, declarative SW representation of resources based on Description Logics (e.g., OWL-DL), and Reactive Rules (e.g., "on Event if Condition do Action" -ECA- rules), provide the proper theoretical foundations to achieve this goal. This paper describes our current research proposal, K 4R (pronounced, with an Italian flavor, "Che fare?"), towards achieving a declarative model for expressing (re)active behavior in and for the SW.

Rule-based support system for multiple UMLS semantic type assignments

PubMed Central

Geller, James; He, Zhe; Perl, Yehoshua; Morrey, C. Paul; Xu, Julia

2012-01-01

Background When new concepts are inserted into the UMLS, they are assigned one or several semantic types from the UMLS Semantic Network by the UMLS editors. However, not every combination of semantic types is permissible. It was observed that many concepts with rare combinations of semantic types have erroneous semantic type assignments or prohibited combinations of semantic types. The correction of such errors is resource-intensive. Objective We design a computational system to inform UMLS editors as to whether a specific combination of two, three, four, or five semantic types is permissible or prohibited or questionable. Methods We identify a set of inclusion and exclusion instructions in the UMLS Semantic Network documentation and derive corresponding rule-categories as well as rule-categories from the UMLS concept content. We then design an algorithm adviseEditor based on these rule-categories. The algorithm specifies rules for an editor how to proceed when considering a tuple (pair, triple, quadruple, quintuple) of semantic types to be assigned to a concept. Results Eight rule-categories were identified. A Web-based system was developed to implement the adviseEditor algorithm, which returns for an input combination of semantic types whether it is permitted, prohibited or (in a few cases) requires more research. The numbers of semantic type pairs assigned to each rule-category are reported. Interesting examples for each rule-category are illustrated. Cases of semantic type assignments that contradict rules are listed, including recently introduced ones. Conclusion The adviseEditor system implements explicit and implicit knowledge available in the UMLS in a system that informs UMLS editors about the permissibility of a desired combination of semantic types. Using adviseEditor might help accelerate the work of the UMLS editors and prevent erroneous semantic type assignments. PMID:23041716

The role of the right hemisphere in semantic control: A case-series comparison of right and left hemisphere stroke

PubMed Central

Thompson, Hannah E.; Henshall, Lauren; Jefferies, Elizabeth

2016-01-01

Semantic control processes guide conceptual retrieval so that we are able to focus on non-dominant associations and features when these are required for the task or context, yet the neural basis of semantic control is not fully understood. Neuroimaging studies have emphasised the role of left inferior frontal gyrus (IFG) in controlled retrieval, while neuropsychological investigations of semantic control deficits have almost exclusively focussed on patients with left-sided damage (e.g., patients with semantic aphasia, SA). Nevertheless, activation in fMRI during demanding semantic tasks typically extends to right IFG. To investigate the role of the right hemisphere (RH) in semantic control, we compared nine RH stroke patients with 21 left-hemisphere SA patients, 11 mild SA cases and 12 healthy, aged-matched controls on semantic and executive tasks, plus experimental tasks that manipulated semantic control in paradigms particularly sensitive to RH damage. RH patients had executive deficits to parallel SA patients but they performed well on standard semantic tests. Nevertheless, multimodal semantic control deficits were found in experimental tasks involving facial emotions and the ‘summation’ of meaning across multiple items. On these tasks, RH patients showed effects similar to those in SA cases – multimodal deficits that were sensitive to distractor strength and cues and miscues, plus increasingly poor performance in cyclical matching tasks which repeatedly probed the same set of concepts. Thus, despite striking differences in single-item comprehension, evidence presented here suggests semantic control is bilateral, and disruption of this component of semantic cognition can be seen following damage to either hemisphere. PMID:26945505

Navigation as a New Form of Search for Agricultural Learning Resources in Semantic Repositories

NASA Astrophysics Data System (ADS)

Cano, Ramiro; Abián, Alberto; Mena, Elena

Education is essential when it comes to raise public awareness on the environmental and economic benefits of organic agriculture and agroecology (OA & AE). Organic.Edunet, an EU funded project, aims at providing a freely-available portal where learning contents on OA & AE can be published and accessed through specialized technologies. This paper describes a novel mechanism for providing semantic capabilities (such as semantic navigational queries) to an arbitrary set of agricultural learning resources, in the context of the Organic.Edunet initiative.

A Robust Geometric Model for Argument Classification

NASA Astrophysics Data System (ADS)

Giannone, Cristina; Croce, Danilo; Basili, Roberto; de Cao, Diego

Argument classification is the task of assigning semantic roles to syntactic structures in natural language sentences. Supervised learning techniques for frame semantics have been recently shown to benefit from rich sets of syntactic features. However argument classification is also highly dependent on the semantics of the involved lexicals. Empirical studies have shown that domain dependence of lexical information causes large performance drops in outside domain tests. In this paper a distributional approach is proposed to improve the robustness of the learning model against out-of-domain lexical phenomena.

tESA: a distributional measure for calculating semantic relatedness.

PubMed

Rybinski, Maciej; Aldana-Montes, José Francisco

2016-12-28

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in the ever growing domain of biomedical informatics. The problem of most state-of-the-art methods for calculating semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this paper we present tESA, an extension to a well known Explicit Semantic Relatedness (ESA) method. In our extension we use two separate sets of vectors, corresponding to different sections of the articles from the underlying corpus of documents, as opposed to the original method, which only uses a single vector space. We present an evaluation of Life Sciences domain-focused applicability of both tESA and domain-adapted Explicit Semantic Analysis. The methods are tested against a set of standard benchmarks established for the evaluation of biomedical semantic relatedness quality. Our experiments show that the propsed method achieves results comparable with or superior to the current state-of-the-art methods. Additionally, a comparative discussion of the results obtained with tESA and ESA is presented, together with a study of the adaptability of the methods to different corpora and their performance with different input parameters. Our findings suggest that combined use of the semantics from different sections (i.e. extending the original ESA methodology with the use of title vectors) of the documents of scientific corpora may be used to enhance the performance of a distributional semantic relatedness measures, which can be observed in the largest reference datasets. We also present the impact of the proposed extension on the size of distributional representations.

ANALYTiC: An Active Learning System for Trajectory Classification.

PubMed

Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

2017-01-01

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

A Semantic Frame Work Reconstructed from Comparative Linguistics.

ERIC Educational Resources Information Center

Key, Mary Ritchie

A theory of semantics focusing on relationships between meaning and sound patterns in language evolution is proposed. Using cognate sets from traditional comparative studies of closely-related languages in well-defined language families, the theory addresses the use and shifting of language components. The theory begins with the ego attempting to…

Attentional Sensitization of Unconscious Cognition: Task Sets Modulate Subsequent Masked Semantic Priming

ERIC Educational Resources Information Center

Kiefer, Markus; Martens, Ulla

2010-01-01

According to classical theories, automatic processes are autonomous and independent of higher level cognitive influence. In contrast, the authors propose that automatic processing depends on attentional sensitization of task-congruent processing pathways. In 3 experiments, the authors tested this hypothesis with a modified masked semantic priming…

E.S.T. and the Oracle.

ERIC Educational Resources Information Center

Richardson, Ian M.

1990-01-01

A possible syllabus for English for Science and Technology is suggested based upon a set of causal relations, arising from a logical description of the presuppositional rhetoric of scientific passages that underlie most semantic functions. An empirical study is reported of the semantic functions present in 52 randomly selected passages.…

Semantic and Phonological Task-Set Priming and Stimulus Processing Investigated Using Magnetoencephalography (MEG)

ERIC Educational Resources Information Center

McNab, F.; Rippon, G.; Hillebrand, A.; Singh, K. D.; Swithenby, S. J.

2007-01-01

In this study the neural substrates of semantic and phonological task priming and task performance were investigated using single word task-primes. Magnetoencephalography (MEG) data were analysed using Synthetic Aperture Magnetometry (SAM) to determine the spatiotemporal and spectral characteristics of cortical responses. Comparisons were made…

Mind Operational Semantics and Brain Operational Architectonics: A Putative Correspondence

PubMed Central

Benedetti, Giulio; Marchetti, Giorgio; Fingelkurts, Alexander A; Fingelkurts, Andrew A

2010-01-01

Despite allowing for the unprecedented visualization of brain functional activity, modern neurobiological techniques have not yet been able to provide satisfactory answers to important questions about the relationship between brain and mind. The aim of this paper is to show how two different but complementary approaches, Mind Operational Semantics (OS) and Brain Operational Architectonics (OA), can help bridge the gap between a specific kind of mental activity—the higher-order reflective thought or linguistic thought—and brain. The fundamental notion that allows the two different approaches to be jointly used under a common framework is that of operation. According to OS, which is based on introspection and linguistic data, the meanings of words can be analyzed in terms of elemental mental operations (EOMC), amongst which those of attention play a key role. Linguistic thought is made possible by special kinds of elements, which OS calls “correlators”, which have the function of tying together the other elements of thought, which OS calls “correlata” (a "correlational network” that is, a sentence, is so formed). Therefore, OS conceives of linguistic thought as a hierarchy of operations of increasing complexity. Likewise, according to OA, which is based on the joint analysis of cognitive and electromagnetic data (EEG and MEG), every conscious phenomenon is brought to existence by the joint operations of many functional and transient neuronal assemblies in the brain. According to OA, the functioning of the brain is always operational (made up of operations), and its structure is characterized by a hierarchy of operations of increasing complexity: single neurons, single assemblies of neurons, synchronized neuronal assemblies or Operational Modules (OM), integrated or complex OMs. The authors put forward the hypothesis that the whole level of OS’s description (EOMC, correlators, and correlational networks) corresponds to the level of OMs (or set of them) of different complexity within OA’s theory: EOMC could correspond to simple OMs, correlators to complex OMs and the correlational network to a set of simple and complex OMs. Finally, a set of experiments is proposed to verify the putative correspondence between OS and OA and prove the existence of an integrated continuum between brain and mind. PMID:21113277

Visual analytics for semantic queries of TerraSAR-X image content

NASA Astrophysics Data System (ADS)

Espinoza-Molina, Daniela; Alonso, Kevin; Datcu, Mihai

2015-10-01

With the continuous image product acquisition of satellite missions, the size of the image archives is considerably increasing every day as well as the variety and complexity of their content, surpassing the end-user capacity to analyse and exploit them. Advances in the image retrieval field have contributed to the development of tools for interactive exploration and extraction of the images from huge archives using different parameters like metadata, key-words, and basic image descriptors. Even though we count on more powerful tools for automated image retrieval and data analysis, we still face the problem of understanding and analyzing the results. Thus, a systematic computational analysis of these results is required in order to provide to the end-user a summary of the archive content in comprehensible terms. In this context, visual analytics combines automated analysis with interactive visualizations analysis techniques for an effective understanding, reasoning and decision making on the basis of very large and complex datasets. Moreover, currently several researches are focused on associating the content of the images with semantic definitions for describing the data in a format to be easily understood by the end-user. In this paper, we present our approach for computing visual analytics and semantically querying the TerraSAR-X archive. Our approach is mainly composed of four steps: 1) the generation of a data model that explains the information contained in a TerraSAR-X product. The model is formed by primitive descriptors and metadata entries, 2) the storage of this model in a database system, 3) the semantic definition of the image content based on machine learning algorithms and relevance feedback, and 4) querying the image archive using semantic descriptors as query parameters and computing the statistical analysis of the query results. The experimental results shows that with the help of visual analytics and semantic definitions we are able to explain the image content using semantic terms and the relations between them answering questions such as what is the percentage of urban area in a region? or what is the distribution of water bodies in a city?

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches.

PubMed

Zeng, Yuqun; Liu, Xusheng; Wang, Yanshan; Shen, Feichen; Liu, Sijia; Rastegar-Mojarad, Majid; Wang, Liwei; Liu, Hongfang

2017-10-16

Self-management is crucial to diabetes care and providing expert-vetted content for answering patients' questions is crucial in facilitating patient self-management. The aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients. We compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum. The evaluation was based on a gold standard dataset consisting of 50 randomly selected diabetic questions where the relevancy of diabetic education materials to the questions was manually assigned by two experts. The performance was assessed using precision of top-ranked documents. We retrieved 7510 diabetic questions on the forum and 144 diabetic patient educational materials from the patient education database at Mayo Clinic. The mapping rate of words in each corpus mapped to the Unified Medical Language System (UMLS) was significantly different (P<.001). The topic modeling-based model outperformed the other retrieval algorithms. For example, for the top-retrieved document, the precision of the topic modeling-based, semantic group-based, and VSM models was 67.0%, 62.8%, and 54.3%, respectively. This study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients' questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums. ©Yuqun Zeng, Xusheng Liu, Yanshan Wang, Feichen Shen, Sijia Liu, Majid Rastegar Mojarad, Liwei Wang, Hongfang Liu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.10.2017.

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

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

The effects of bilingual language proficiency on recall accuracy and semantic clustering in free recall output: evidence for shared semantic associations across languages.

PubMed

Francis, Wendy S; Taylor, Randolph S; Gutiérrez, Marisela; Liaño, Mary K; Manzanera, Diana G; Penalver, Renee M

2018-05-19

Two experiments investigated how well bilinguals utilise long-standing semantic associations to encode and retrieve semantic clusters in verbal episodic memory. In Experiment 1, Spanish-English bilinguals (N = 128) studied and recalled word and picture sets. Word recall was equivalent in L1 and L2, picture recall was better in L1 than in L2, and the picture superiority effect was stronger in L1 than in L2. Semantic clustering in word and picture recall was equivalent in L1 and L2. In Experiment 2, Spanish-English bilinguals (N = 128) and English-speaking monolinguals (N = 128) studied and recalled word sequences that contained semantically related pairs. Data were analyzed using a multinomial processing tree approach, the pair-clustering model. Cluster formation was more likely for semantically organised than for randomly ordered word sequences. Probabilities of cluster formation, cluster retrieval, and retrieval of unclustered items did not differ across languages or language groups. Language proficiency has little if any impact on the utilisation of long-standing semantic associations, which are language-general.

Patient-related constraints on get- and be-passive uses in English: evidence from paraphrasing

PubMed Central

Thompson, Dominic; Ling, S. P.; Myachykov, Andriy; Ferreira, Fernanda; Scheepers, Christoph

2013-01-01

In English, transitive events can be described in various ways. The main possibilities are active-voice and passive-voice, which are assumed to have distinct semantic and pragmatic functions. Within the passive, there are two further options, namely be-passive or get-passive. While these two forms are generally understood to differ, there is little agreement on precisely how and why. The passive Patient is frequently cited as playing a role, though again agreement on the specifics is rare. Here we present three paraphrasing experiments investigating Patient-related constraints on the selection of active vs. passive voice, and be- vs. get-passive, respectively. Participants either had to re-tell short stories in their own words (Experiments 1 and 2) or had to answer specific questions about the Patient in those short stories (Experiment 3). We found that a given Agent in a story promotes the use of active-voice, while a given Patient promotes be-passives specifically. Meanwhile, get-passive use increases when the Patient is marked as important. We argue that the three forms of transitive description are functionally and semantically distinct, and can be arranged along two dimensions: Patient Prominence and Patient Importance. We claim that active-voice has a near-complementary relationship with the be-passive, driven by which protagonist is given. Since both get and be are passive, they share the features of a Patient-subject and an optional Agent by-phrase; however, get specifically responds to a Patient being marked as important. Each of these descriptions has its own set of features that differentiate it from the others. PMID:24273527

From Science to e-Science to Semantic e-Science: A Heliosphysics Case Study

NASA Technical Reports Server (NTRS)

Narock, Thomas; Fox, Peter

2011-01-01

The past few years have witnessed unparalleled efforts to make scientific data web accessible. The Semantic Web has proven invaluable in this effort; however, much of the literature is devoted to system design, ontology creation, and trials and tribulations of current technologies. In order to fully develop the nascent field of Semantic e-Science we must also evaluate systems in real-world settings. We describe a case study within the field of Heliophysics and provide a comparison of the evolutionary stages of data discovery, from manual to semantically enable. We describe the socio-technical implications of moving toward automated and intelligent data discovery. In doing so, we highlight how this process enhances what is currently being done manually in various scientific disciplines. Our case study illustrates that Semantic e-Science is more than just semantic search. The integration of search with web services, relational databases, and other cyberinfrastructure is a central tenet of our case study and one that we believe has applicability as a generalized research area within Semantic e-Science. This case study illustrates a specific example of the benefits, and limitations, of semantically replicating data discovery. We show examples of significant reductions in time and effort enable by Semantic e-Science; yet, we argue that a "complete" solution requires integrating semantic search with other research areas such as data provenance and web services.

Corpus annotation for mining biomedical events from literature

PubMed Central

Kim, Jin-Dong; Ohta, Tomoko; Tsujii, Jun'ichi

2008-01-01

Background Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation. Results We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation. Conclusion The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain. PMID:18182099

Classification with an edge: Improving semantic image segmentation with boundary detection

NASA Astrophysics Data System (ADS)

Marmanis, D.; Schindler, K.; Wegner, J. D.; Galliani, S.; Datcu, M.; Stilla, U.

2018-01-01

We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model. First, we construct a comparatively simple, memory-efficient model by adding boundary detection to the SEGNET encoder-decoder architecture. Second, we also include boundary detection in FCN-type models and set up a high-end classifier ensemble. We show that boundary detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark.

Semantic web data warehousing for caGrid.

PubMed

McCusker, James P; Phillips, Joshua A; González Beltrán, Alejandra; Finkelstein, Anthony; Krauthammer, Michael

2009-10-01

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges.

The Software Therapist: Usability Problem Diagnosis Through Latent Semantic Analysis

DTIC Science & Technology

2006-06-14

at a given level is equivalent to removing attributes that don’t apply to a given usability situation, thereby filtering or pruning off irrelevant sub...Each answer prunes the number of stages remaining. Through a process of elimination, the Wizard helps evaluators home in on the correct stage...the diagnosis for one problem report, the user may want to take a break to get a cup of coffee or take a short walk, but when ready to continue with

Linguistic multi-criteria decision-making with representing semantics by programming

NASA Astrophysics Data System (ADS)

Yang, Wu-E.; Ma, Chao-Qun; Han, Zhi-Qiu

2017-01-01

A linguistic multi-criteria decision-making method is introduced. In this method, a maximising discrimination programming assigns the semanteme values to linguistic variables to represent their semantics. Incomplete preferences from using linguistic information are expressed by the constraints of the model. Such assignment can amplify the difference between alternatives. Thus, the discrimination of the decision model is increased, which facilitates the decision-maker to rank or order the alternatives for making a decision. We also discuss the parameter setting and its influence, and use an application example to illustrate the proposed method. Further, the results with three types of semantic structure highlight the ability of the method in handling different semantic structures.

Training propositional reasoning.

PubMed

Klauer, K C; Meiser, T; Naumer, B

2000-08-01

Two experiments compared the effects of four training conditions on propositional reasoning. A syntactic training demonstrated formal derivations, in an abstract semantic training the standard truth-table definitions of logical connectives were explained, and a domain-specific semantic training provided thematic contexts for the premises of the reasoning task. In a control training, an inductive reasoning task was practised. In line with the account by mental models, both kinds of semantic training were significantly more effective than the control and the syntactic training, whereas there were no significant differences between the control and the syntactic training, nor between the two kinds of semantic training. Experiment 2 replicated this pattern of effects using a different set of syntactic and domain-specific training conditions.

Semantic features of 'stepped' versus 'continuous' contours in German intonation.

PubMed

Dombrowski, Ernst

2013-01-01

This study analyses the meaning spaces of German pitch contours using two modes of melodic movement: continuous or in steps of sustained pitch. Both the continuous and stepped movements are represented by a set of five basic patterns, the latter being derived from the former. Thirty-six German native speakers judged the pattern sets on a 12-scale semantic differential. The semantic profiles confirm that stepped contours can be conceived of as stylized intonation, in a formal as well as in a functional sense. On the one hand, continuous (non-stylized) and stepped (stylized) contours are assigned different overall meanings (especially on the scales astonished - commonplace and interested - not interested). On the other hand, listeners organize the two contour sets in a similar fashion, which speaks in favour of parallel pattern inventories of continuous and stepped movement, respectively. However, the meaning space of the stylized patterns is affected by formal restrictions, for instance in the step transformation of continuous rises. © 2014 S. Karger AG, Basel.

The differential contributions of pFC and temporo-parietal cortex to multimodal semantic control: exploring refractory effects in semantic aphasia.

PubMed

Gardner, Hannah E; Lambon Ralph, Matthew A; Dodds, Naomi; Jones, Theresa; Ehsan, Sheeba; Jefferies, Elizabeth

2012-04-01

Aphasic patients with multimodal semantic impairment following pFC or temporo-parietal (TP) cortex damage (semantic aphasia [SA]) have deficits characterized by poor control of semantic activation/retrieval, as opposed to loss of semantic knowledge per se. In line with this, SA patients show "refractory effects"; that is, declining accuracy in cyclical word-picture matching tasks when semantically related sets are presented rapidly and repeatedly. This is argued to follow a build-up of competition between targets and distractors. However, the link between poor semantic control and refractory effects is still controversial for two reasons. (1) Some theories propose that refractory effects are specific to verbal or auditory tasks, yet SA patients show poor control over semantic processing in both word and picture semantic tasks. (2) SA can result from lesions to either the left pFC or TP cortex, yet previous work suggests that refractory effects are specifically linked to the left inferior frontal cortex. For the first time, verbal, visual, and nonverbal auditory refractory effects were explored in nine SA patients who had pFC (pFC+) or TP cortex (TP-only) lesions. In all modalities, patient accuracy declined significantly over repetitions. This refractory effect at the group level was driven by pFC+ patients and was not shown by individuals with TP-only lesions. These findings support the theory that SA patients have reduced control over multimodal semantic retrieval and, additionally, suggest there may be functional specialization within the posterior versus pFC elements of the semantic control network.

When fruits lose to animals: Disorganized search of semantic memory in Parkinson's disease.

PubMed

Tagini, Sofia; Seyed-Allaei, Shima; Scarpina, Federica; Toraldo, Alessio; Mauro, Alessandro; Cherubini, Paolo; Reverberi, Carlo

2018-04-16

The semantic fluency task is widely used in both clinical and research settings to assess both the integrity of the semantic store and the effectiveness of the search through it. Our aim was to investigate whether nondemented Parkinson's disease (PD) patients show an impairment in the strategic exploration of the semantic store and whether the tested semantic category has an impact on multiple measures of performance. We compared 74 nondemented PD patients with 254 healthy subjects in a semantic fluency test using relatively small (fruits) and large (animals) semantic categories. Number of words produced, number of explored semantic subcategories, and degree of order in the produced sequences were computed as dependent variables. PD patients produced fewer words than healthy subjects did, regardless of the category. Number of subcategories was also lower in PD patients than in healthy subjects, without a significant difference between categories. Critically, PD patients' sequences were less semantically organized than were those of controls, but this effect appeared in only the smaller category (fruits), thus pointing to a lack of strategy in exploring the semantic store. Our results show that the semantic fluency deficit in PD patients has a strategic component, even though that may not be the only cause of the impaired performance. Furthermore, our evidence suggests that the semantic category used in the test influences performance, hence providing an explanation for the failure by previous studies, which often used large categories such as animals, to detect strategy deficits in PD. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The naming impairment of living and nonliving items in Alzheimer's disease.

PubMed

Montanes, P; Goldblum, M C; Boller, F

1995-01-01

Several studies of semantic abilities in Dementia of the Alzheimer Type (DAT) suggest that their semantic disorders may affect specific categories of knowledge. In particular, the existence of a category-specific semantic impairment affecting, selectively, living things has frequently been reported in association with DAT. We report here results from two naming tasks of 25 DAT patients and two subgroups within this population. The first naming task used 48 black and white line drawings from Snodgrass and Vanderwart (1980) which controlled the visual complexity of stimuli from living and nonliving categories. The second task used 44 colored pictures (to assess the influence of word frequency in living vs. nonliving categories). Within the set of black and white pictures, both DAT patients and controls obtained significantly lower scores on high visual complexity stimuli than on stimuli of low visual complexity. A clear effect of semantic category emerged for DAT patients and controls, with a lower performance on the living category. Within the colored set, pictures corresponding to high frequency words gave rise to significantly higher scores than pictures corresponding to low frequency words. No significant difference emerged between living versus nonliving categories, either in DAT patients or in controls. In the two tasks, the two subgroups of DAT patients presented a different profile of performance and error type. As color constitutes the main difference between the two sets of pictures, our results point to the relevance of this cue in the processing of semantic information, with visual complexity and frequency also being very relevant.

A semantic data dictionary method for database schema integration in CIESIN

NASA Astrophysics Data System (ADS)

Hinds, N.; Huang, Y.; Ravishankar, C.

1993-08-01

CIESIN (Consortium for International Earth Science Information Network) is funded by NASA to investigate the technology necessary to integrate and facilitate the interdisciplinary use of Global Change information. A clear of this mission includes providing a link between the various global change data sets, in particular the physical sciences and the human (social) sciences. The typical scientist using the CIESIN system will want to know how phenomena in an outside field affects his/her work. For example, a medical researcher might ask: how does air-quality effect emphysema? This and many similar questions will require sophisticated semantic data integration. The researcher who raised the question may be familiar with medical data sets containing emphysema occurrences. But this same investigator may know little, if anything, about the existance or location of air-quality data. It is easy to envision a system which would allow that investigator to locate and perform a ``join'' on two data sets, one containing emphysema cases and the other containing air-quality levels. No such system exists today. One major obstacle to providing such a system will be overcoming the heterogeneity which falls into two broad categories. ``Database system'' heterogeneity involves differences in data models and packages. ``Data semantic'' heterogeneity involves differences in terminology between disciplines which translates into data semantic issues, and varying levels of data refinement, from raw to summary. Our work investigates a global data dictionary mechanism to facilitate a merged data service. Specially, we propose using a semantic tree during schema definition to aid in locating and integrating heterogeneous databases.

Establishing semantic interoperability of biomedical metadata registries using extended semantic relationships.

PubMed

Park, Yu Rang; Yoon, Young Jo; Kim, Hye Hyeon; Kim, Ju Han

2013-01-01

Achieving semantic interoperability is critical for biomedical data sharing between individuals, organizations and systems. The ISO/IEC 11179 MetaData Registry (MDR) standard has been recognized as one of the solutions for this purpose. The standard model, however, is limited. Representing concepts consist of two or more values, for instance, are not allowed including blood pressure with systolic and diastolic values. We addressed the structural limitations of ISO/IEC 11179 by an integrated metadata object model in our previous research. In the present study, we introduce semantic extensions for the model by defining three new types of semantic relationships; dependency, composite and variable relationships. To evaluate our extensions in a real world setting, we measured the efficiency of metadata reduction by means of mapping to existing others. We extracted metadata from the College of American Pathologist Cancer Protocols and then evaluated our extensions. With no semantic loss, one third of the extracted metadata could be successfully eliminated, suggesting better strategy for implementing clinical MDRs with improved efficiency and utility.

The neural correlates of semantic richness: evidence from an fMRI study of word learning.

PubMed

Ferreira, Roberto A; Göbel, Silke M; Hymers, Mark; Ellis, Andrew W

2015-04-01

We investigated the neural correlates of concrete nouns with either many or few semantic features. A group of 21 participants underwent two days of training and were then asked to categorize 40 newly learned words and a set of matched familiar words as living or nonliving in an MRI scanner. Our results showed that the most reliable effects of semantic richness were located in the left angular gyrus (AG) and middle temporal gyrus (MTG), where activation was higher for semantically rich than poor words. Other areas showing the same pattern included bilateral precuneus and posterior cingulate gyrus. Our findings support the view that AG and anterior MTG, as part of the multimodal network, play a significant role in representing and integrating semantic features from different input modalities. We propose that activation in bilateral precuneus and posterior cingulate gyrus reflects interplay between AG and episodic memory systems during semantic retrieval. Copyright © 2015 Elsevier Inc. All rights reserved.

When emotional prosody and semantics dance cheek to cheek: ERP evidence.

PubMed

Kotz, Sonja A; Paulmann, Silke

2007-06-02

To communicate emotionally entails that a listener understands a verbal message but also the emotional prosody going along with it. So far the time course and interaction of these emotional 'channels' is still poorly understood. The current set of event-related brain potential (ERP) experiments investigated both the interactive time course of emotional prosody with semantics and of emotional prosody independent of emotional semantics using a cross-splicing method. In a probe verification task (Experiment 1) prosodic expectancy violations elicited a positivity, while a combined prosodic-semantic expectancy violation elicited a negativity. Comparable ERP results were obtained in an emotional prosodic categorization task (Experiment 2). The present data support different ERP responses with distinct time courses and topographies elicited as a function of prosodic expectancy and combined prosodic-semantic expectancy during emotional prosodic processing and combined emotional prosody/emotional semantic processing. These differences suggest that the interaction of more than one emotional channel facilitates subtle transitions in an emotional sentence context.

The Nombela 2.0 semantic battery: an updated Spanish instrument for the study of semantic processing.

PubMed

Moreno-Martínez, F Javier; Rodríguez-Rojo, Inmaculada C

2015-01-01

In this study, the Nombela 2.0 semantic battery is presented. This is a new version of its earlier precedent: the battery Nombela (I), in an attempt to improve it (dealing with ceiling effects) and reducing the application time by decreasing the number of tasks. The battery is constructed on a common set of 98 stimuli, including both living and nonliving semantic domains. It consists of five tasks designed to explore category specificity by tapping semantic production and comprehension, using both visual and verbal input. All of the items were rated according to Spanish norms, as stated in a previous study of our group, and all of the tasks were matched across domain on six nuisance variables. The present study has two goals: (i) to make available the updated version (2.0) of the Nombela semantic memory battery and (ii) to characterize and compare the neuropsychological profiles of two different patient groups: mild cognitive impairment and Alzheimer disease, with regard to normal controls.

The role of the right hemisphere in semantic control: A case-series comparison of right and left hemisphere stroke.

PubMed

Thompson, Hannah E; Henshall, Lauren; Jefferies, Elizabeth

2016-05-01

Semantic control processes guide conceptual retrieval so that we are able to focus on non-dominant associations and features when these are required for the task or context, yet the neural basis of semantic control is not fully understood. Neuroimaging studies have emphasised the role of left inferior frontal gyrus (IFG) in controlled retrieval, while neuropsychological investigations of semantic control deficits have almost exclusively focussed on patients with left-sided damage (e.g., patients with semantic aphasia, SA). Nevertheless, activation in fMRI during demanding semantic tasks typically extends to right IFG. To investigate the role of the right hemisphere (RH) in semantic control, we compared nine RH stroke patients with 21 left-hemisphere SA patients, 11 mild SA cases and 12 healthy, aged-matched controls on semantic and executive tasks, plus experimental tasks that manipulated semantic control in paradigms particularly sensitive to RH damage. RH patients had executive deficits to parallel SA patients but they performed well on standard semantic tests. Nevertheless, multimodal semantic control deficits were found in experimental tasks involving facial emotions and the 'summation' of meaning across multiple items. On these tasks, RH patients showed effects similar to those in SA cases - multimodal deficits that were sensitive to distractor strength and cues and miscues, plus increasingly poor performance in cyclical matching tasks which repeatedly probed the same set of concepts. Thus, despite striking differences in single-item comprehension, evidence presented here suggests semantic control is bilateral, and disruption of this component of semantic cognition can be seen following damage to either hemisphere. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Adapting to conversation with semantic dementia: using enactment as a compensatory strategy in everyday social interaction

PubMed Central

Kindell, Jacqueline; Sage, Karen; Keady, John; Wilkinson, Ray

2014-01-01

Background Studies to date in semantic dementia have examined communication in clinical or experimental settings. There is a paucity of research describing the everyday interactional skills and difficulties seen in this condition. Aims To examine the everyday conversation, at home, of an individual with semantic dementia. Methods & Procedures A 71-year-old man with semantic dementia and his wife were given a video camera and asked to record natural conversation in the home situation with no researcher present. Recordings were also made in the home environment, with the individual with semantic dementia in conversation with a member of the research team. Conversation analysis was used to transcribe and analyse the data. Recurring features were noted to identify conversational patterns. Outcomes & Results Analysis demonstrated a repeated practice by the speaker with semantic dementia of acting out a diversity of scenes (enactment). As such, the speaker regularly used direct reported speech along with paralinguistic features (such as pitch and loudness) and non-vocal communication (such as body posture, pointing and facial expression) as an adaptive strategy to communicate with others in conversation. Conclusions & Implications This case shows that while severe difficulties may be present on neuropsychological assessment, relatively effective communicative strategies may be evident in conversation. A repeated practice of enactment in conversation allowed this individual to act out, or perform what he wanted to say, allowing him to generate a greater level of meaningful communication than his limited vocabulary alone could achieve through describing the events concerned. Such spontaneously acquired adaptive strategies require further attention in both research and clinical settings in semantic dementia and analysis of interaction in this condition, using conversation analysis, may be helpful. PMID:24033649

Pattern search in multi-structure data: a framework for the next-generation evidence-based medicine

NASA Astrophysics Data System (ADS)

Sukumar, Sreenivas R.; Ainsworth, Keela C.

2014-03-01

With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. Addressing this need, we pose and answer the following questions: (i) How can we jointly analyze and explore measurement data in context with qualitative domain knowledge? (ii) How can we search and hypothesize patterns (not known apriori) from such multi-structure data? (iii) How can we build predictive models by integrating weakly-associated multi-relational multi-structure data? We propose a framework towards answering these questions. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Early Decomposition in Visual Word Recognition: Dissociating Morphology, Form, and Meaning

ERIC Educational Resources Information Center

Marslen-Wilson, William D.; Bozic, Mirjana; Randall, Billi

2008-01-01

The role of morphological, semantic, and form-based factors in the early stages of visual word recognition was investigated across different SOAs in a masked priming paradigm, focusing on English derivational morphology. In a first set of experiments, stimulus pairs co-varying in morphological decomposability and in semantic and orthographic…

Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-WorldChemical Production

EPA Science Inventory

A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodology towards automated inventory modeling of chemical manufacturing in life cycle assessment. The cradle-to-gate life cycle inventory for chemical manufacturing is a detailed collection of ...

Algorithmic Procedure for Finding Semantically Related Journals.

ERIC Educational Resources Information Center

Pudovkin, Alexander I.; Garfield, Eugene

2002-01-01

Using citations, papers and references as parameters a relatedness factor (RF) is computed for a series of journals. Sorting these journals by the RF produces a list of journals most closely related to a specified starting journal. The method appears to select a set of journals that are semantically most similar to the target journal. The…

Sleep Increases Explicit Solutions and Reduces Intuitive Judgments of Semantic Coherence

ERIC Educational Resources Information Center

Zander, Thea; Volz, Kirsten G.; Born, Jan; Diekelmann, Susanne

2017-01-01

Sleep fosters the generation of explicit knowledge. Whether sleep also benefits implicit intuitive decisions about underlying patterns is unclear. We examined sleep's role in explicit and intuitive semantic coherence judgments. Participants encoded sets of three words and after a sleep or wake period were required to judge the potential…

Dynamic switching between semantic and episodic memory systems.

PubMed

Kompus, Kristiina; Olsson, Carl-Johan; Larsson, Anne; Nyberg, Lars

2009-09-01

It has been suggested that episodic and semantic long-term memory systems interact during retrieval. Here we examined the flexibility of memory retrieval in an associative task taxing memories of different strength, assumed to differentially engage episodic and semantic memory. Healthy volunteers were pre-trained on a set of 36 face-name pairs over a 6-week period. Another set of 36 items was shown only once during the same time period. About 3 months after the training period all items were presented in a randomly intermixed order in an event-related fMRI study of face-name memory. Once presented items differentially activated anterior cingulate cortex and a right prefrontal region that previously have been associated with episodic retrieval mode. High-familiar items were associated with stronger activation of posterior cortices and a left frontal region. These findings fit a model of memory retrieval by which early processes determine, on a trial-by-trial basis, if the task can be solved by the default semantic system. If not, there is a dynamic shift to cognitive control processes that guide retrieval from episodic memory.

Medical Image Analysis by Cognitive Information Systems - a Review.

PubMed

Ogiela, Lidia; Takizawa, Makoto

2016-10-01

This publication presents a review of medical image analysis systems. The paradigms of cognitive information systems will be presented by examples of medical image analysis systems. The semantic processes present as it is applied to different types of medical images. Cognitive information systems were defined on the basis of methods for the semantic analysis and interpretation of information - medical images - applied to cognitive meaning of medical images contained in analyzed data sets. Semantic analysis was proposed to analyzed the meaning of data. Meaning is included in information, for example in medical images. Medical image analysis will be presented and discussed as they are applied to various types of medical images, presented selected human organs, with different pathologies. Those images were analyzed using different classes of cognitive information systems. Cognitive information systems dedicated to medical image analysis was also defined for the decision supporting tasks. This process is very important for example in diagnostic and therapy processes, in the selection of semantic aspects/features, from analyzed data sets. Those features allow to create a new way of analysis.

Semantic layers for illustrative volume rendering.

PubMed

Rautek, Peter; Bruckner, Stefan; Gröller, Eduard

2007-01-01

Direct volume rendering techniques map volumetric attributes (e.g., density, gradient magnitude, etc.) to visual styles. Commonly this mapping is specified by a transfer function. The specification of transfer functions is a complex task and requires expert knowledge about the underlying rendering technique. In the case of multiple volumetric attributes and multiple visual styles the specification of the multi-dimensional transfer function becomes more challenging and non-intuitive. We present a novel methodology for the specification of a mapping from several volumetric attributes to multiple illustrative visual styles. We introduce semantic layers that allow a domain expert to specify the mapping in the natural language of the domain. A semantic layer defines the mapping of volumetric attributes to one visual style. Volumetric attributes and visual styles are represented as fuzzy sets. The mapping is specified by rules that are evaluated with fuzzy logic arithmetics. The user specifies the fuzzy sets and the rules without special knowledge about the underlying rendering technique. Semantic layers allow for a linguistic specification of the mapping from attributes to visual styles replacing the traditional transfer function specification.

Graph Mining Meets the Semantic Web

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

Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluatemore » the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.« less

Generating Researcher Networks with Identified Persons on a Semantic Service Platform

NASA Astrophysics Data System (ADS)

Jung, Hanmin; Lee, Mikyoung; Kim, Pyung; Lee, Seungwoo

This paper describes a Semantic Web-based method to acquire researcher networks by means of identification scheme, ontology, and reasoning. Three steps are required to realize it; resolving co-references, finding experts, and generating researcher networks. We adopt OntoFrame as an underlying semantic service platform and apply reasoning to make direct relations between far-off classes in ontology schema. 453,124 Elsevier journal articles with metadata and full-text documents in information technology and biomedical domains have been loaded and served on the platform as a test set.

Personal semantic memory: insights from neuropsychological research on amnesia.

PubMed

Grilli, Matthew D; Verfaellie, Mieke

2014-08-01

This paper provides insight into the cognitive and neural mechanisms of personal semantic memory, knowledge that is specific and unique to individuals, by reviewing neuropsychological research on stable amnesia secondary to medial temporal lobe damage. The results reveal that personal semantic memory does not depend on a unitary set of cognitive and neural mechanisms. Findings show that autobiographical fact knowledge reflects an experience-near type of personal semantic memory that relies on the medial temporal lobe for retrieval, albeit less so than personal episodic memory. Additional evidence demonstrates that new autobiographical fact learning likely relies on the medial temporal lobe, but the extent to which remains unclear. Other findings show that retrieval of personal traits/roles and new learning of personal traits/roles and thoughts/beliefs are independent of the medial temporal lobe and thus may represent highly conceptual types of personal semantic memory that are stored in the neocortex. Published by Elsevier Ltd.

An Italian battery for the assessment of semantic memory disorders.

PubMed

Catricalà, Eleonora; Della Rosa, Pasquale A; Ginex, Valeria; Mussetti, Zoe; Plebani, Valentina; Cappa, Stefano F

2013-06-01

We report the construction and standardization of a new comprehensive battery of tests for the assessment of semantic memory disorders. The battery is constructed on a common set of 48 stimuli, belonging to both living and non-living categories, rigidly controlled for several confounding variables, and is based on an empirically derived corpus of semantic features. It includes six tasks, in order to assess semantic memory through different modalities of input and output: two naming tasks, one with colored pictures and the other in response to an oral description, a word-picture matching task, a picture sorting task, a free generation of features task and a sentence verification task. Normative data on 106 Italian subjects pooled across homogenous subgroups for age, sex and education are reported. The new battery allows an in-depth investigation of category-specific disorders and of progressive semantic memory deficits at features level, overcoming some of the limitations of existing tests.

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.

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.

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.

Towards semantic interoperability for electronic health records.

PubMed

Garde, Sebastian; Knaup, Petra; Hovenga, Evelyn; Heard, Sam

2007-01-01

In the field of open electronic health records (EHRs), openEHR as an archetype-based approach is being increasingly recognised. It is the objective of this paper to shortly describe this approach, and to analyse how openEHR archetypes impact on health professionals and semantic interoperability. Analysis of current approaches to EHR systems, terminology and standards developments. In addition to literature reviews, we organised face-to-face and additional telephone interviews and tele-conferences with members of relevant organisations and committees. The openEHR archetypes approach enables syntactic interoperability and semantic interpretability -- both important prerequisites for semantic interoperability. Archetypes enable the formal definition of clinical content by clinicians. To enable comprehensive semantic interoperability, the development and maintenance of archetypes needs to be coordinated internationally and across health professions. Domain knowledge governance comprises a set of processes that enable the creation, development, organisation, sharing, dissemination, use and continuous maintenance of archetypes. It needs to be supported by information technology. To enable EHRs, semantic interoperability is essential. The openEHR archetypes approach enables syntactic interoperability and semantic interpretability. However, without coordinated archetype development and maintenance, 'rank growth' of archetypes would jeopardize semantic interoperability. We therefore believe that openEHR archetypes and domain knowledge governance together create the knowledge environment required to adopt EHRs.

Is there a universal answering strategy for rejecting negative propositions? Typological evidence on the use of prosody and gesture

PubMed Central

González-Fuente, Santiago; Tubau, Susagna; Espinal, M. Teresa; Prieto, Pilar

2015-01-01

Previous research has proposed that languages diverge with respect to how their speakers confirm and contradict negative questions. Taking into account the classification between truth-based and polarity-based languages, this paper is mainly concerned with the expression of REJECT (a semantic operation that signals a contradiction move with respect to the common ground, along Krifka's lines) in two languages belonging to two typologically distinct answering systems, namely Catalan (polarity-based) and Russian (a mixed system using polarity-based, truth-based, and echoic strategies). This investigation has two goals. First, to assess empirically the relevance of prosodic and gestural patterns in the interpretation of confirming and rejecting responses to negative polar questions. Second, to test the claim that in fact speakers resort to strikingly similar universal strategies at the time of expressing rejecting answers to discourse accessible negative assertions and negative polar questions, namely the use of linguistic units that encode REJECT in combination with ASSERT. The results of our investigation support the existence of a universal answering system for rejecting negative polar questions that integrates lexical and syntactic strategies with prosodic and gestural patterns, and instantiate the REJECT and ASSERT operators. We will also discuss the implications these results have for the truth-based vs. polarity-based taxonomy. PMID:26217255

Determining Semantically Related Significant Genes.

PubMed

Taha, Kamal

2014-01-01

GO relation embodies some aspects of existence dependency. If GO term xis existence-dependent on GO term y, the presence of y implies the presence of x. Therefore, the genes annotated with the function of the GO term y are usually functionally and semantically related to the genes annotated with the function of the GO term x. A large number of gene set enrichment analysis methods have been developed in recent years for analyzing gene sets enrichment. However, most of these methods overlook the structural dependencies between GO terms in GO graph by not considering the concept of existence dependency. We propose in this paper a biological search engine called RSGSearch that identifies enriched sets of genes annotated with different functions using the concept of existence dependency. We observe that GO term xcannot be existence-dependent on GO term y, if x- and y- have the same specificity (biological characteristics). After encoding into a numeric format the contributions of GO terms annotating target genes to the semantics of their lowest common ancestors (LCAs), RSGSearch uses microarray experiment to identify the most significant LCA that annotates the result genes. We evaluated RSGSearch experimentally and compared it with five gene set enrichment systems. Results showed marked improvement.

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.

Adapting Semantic Natural Language Processing Technology to Address Information Overload in Influenza Epidemic Management

PubMed Central

Keselman, Alla; Rosemblat, Graciela; Kilicoglu, Halil; Fiszman, Marcelo; Jin, Honglan; Shin, Dongwook; Rindflesch, Thomas C.

2013-01-01

Explosion of disaster health information results in information overload among response professionals. The objective of this project was to determine the feasibility of applying semantic natural language processing (NLP) technology to addressing this overload. The project characterizes concepts and relationships commonly used in disaster health-related documents on influenza pandemics, as the basis for adapting an existing semantic summarizer to the domain. Methods include human review and semantic NLP analysis of a set of relevant documents. This is followed by a pilot-test in which two information specialists use the adapted application for a realistic information seeking task. According to the results, the ontology of influenza epidemics management can be described via a manageable number of semantic relationships that involve concepts from a limited number of semantic types. Test users demonstrate several ways to engage with the application to obtain useful information. This suggests that existing semantic NLP algorithms can be adapted to support information summarization and visualization in influenza epidemics and other disaster health areas. However, additional research is needed in the areas of terminology development (as many relevant relationships and terms are not part of existing standardized vocabularies), NLP, and user interface design. PMID:24311971

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.

Semantic web data warehousing for caGrid

PubMed Central

McCusker, James P; Phillips, Joshua A; Beltrán, Alejandra González; Finkelstein, Anthony; Krauthammer, Michael

2009-01-01

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG® Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges. PMID:19796399

Factors Responsible for Performance on the Day-Night Task: Response Set or Semantics?

ERIC Educational Resources Information Center

Simpson, Andrew; Riggs, Kevin J.

2005-01-01

In a recent study Diamond, Kirkham and Amso (2002) obtained evidence consistent with the claim that the day-night task requires inhibition because the picture and its corresponding conflicting response are semantically related. In their study children responded more accurately in a dog-pig condition (see /day picture/ say "dog"; see /night…

Treatment Integrity of Elaborated Semantic Feature Analysis Aphasia Therapy Delivered in Individual and Group Settings

ERIC Educational Resources Information Center

Kladouchou, Vasiliki; Papathanasiou, Ilias; Efstratiadou, Eva A.; Christaki, Vasiliki; Hilari, Katerina

2017-01-01

Background & Aims: This study ran within the framework of the Thales Aphasia Project that investigated the efficacy of elaborated semantic feature analysis (ESFA). We evaluated the treatment integrity (TI) of ESFA, i.e., the degree to which therapists implemented treatment as intended by the treatment protocol, in two different formats:…

Function Follows Form: Activation of Shape and Function Features during Object Identification

ERIC Educational Resources Information Center

Yee, Eiling; Huffstetler, Stacy; Thompson-Schill, Sharon L.

2011-01-01

Most theories of semantic memory characterize knowledge of a given object as comprising a set of semantic features. But how does conceptual activation of these features proceed during object identification? We present the results of a pair of experiments that demonstrate that object recognition is a dynamically unfolding process in which function…

Atypical Neurophysiology Underlying Episodic and Semantic Memory in Adults with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Massand, Esha; Bowler, Dermot M.

2015-01-01

Individuals with autism spectrum disorder (ASD) show atypicalities in episodic memory (Boucher et al. in Psychological Bulletin, 138 (3), 458-496, 2012). We asked participants to recall the colours of a set of studied line drawings (episodic judgement), or to recognize line drawings alone (semantic judgement). Cycowicz et al. ("Journal of…

Case-Based Learning, Pedagogical Innovation, and Semantic Web Technologies

ERIC Educational Resources Information Center

Martinez-Garcia, A.; Morris, S.; Tscholl, M.; Tracy, F.; Carmichael, P.

2012-01-01

This paper explores the potential of Semantic Web technologies to support teaching and learning in a variety of higher education settings in which some form of case-based learning is the pedagogy of choice. It draws on the empirical work of a major three year research and development project in the United Kingdom: "Ensemble: Semantic…

Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

Ontology modularization to improve semantic medical image annotation.

PubMed

Wennerberg, Pinar; Schulz, Klaus; Buitelaar, Paul

2011-02-01

Searching for medical images and patient reports is a significant challenge in a clinical setting. The contents of such documents are often not described in sufficient detail thus making it difficult to utilize the inherent wealth of information contained within them. Semantic image annotation addresses this problem by describing the contents of images and reports using medical ontologies. Medical images and patient reports are then linked to each other through common annotations. Subsequently, search algorithms can more effectively find related sets of documents on the basis of these semantic descriptions. A prerequisite to realizing such a semantic search engine is that the data contained within should have been previously annotated with concepts from medical ontologies. One major challenge in this regard is the size and complexity of medical ontologies as annotation sources. Manual annotation is particularly time consuming labor intensive in a clinical environment. In this article we propose an approach to reducing the size of clinical ontologies for more efficient manual image and text annotation. More precisely, our goal is to identify smaller fragments of a large anatomy ontology that are relevant for annotating medical images from patients suffering from lymphoma. Our work is in the area of ontology modularization, which is a recent and active field of research. We describe our approach, methods and data set in detail and we discuss our results. Copyright © 2010 Elsevier Inc. All rights reserved.

Factors responsible for performance on the day-night task: response set or semantics?

PubMed

Simpson, Andrew; Riggs, Kevin J

2005-07-01

In a recent study Diamond, Kirkham and Amso (2002) obtained evidence consistent with the claim that the day-night task requires inhibition because the picture and its corresponding conflicting response are semantically related. In their study children responded more accurately in a dog-pig condition (see /day picture/ say "dog"; see /night picture/ say "pig") than the standard day-night condition (see /day picture/ say "night"; see /night picture/ say "day"). However, there is another effect that may have made the day-night condition harder than the dog-pig condition: the response set effect. In the day-night condition the names of the two stimuli ("day" and "night") and the two corresponding conflicting responses ("night" and "day") are from the same response set: both "day" and "night". In the dog-pig condition the names of the stimuli ("day", "night") and the corresponding responses ("dog", "pig") are from a different response set. In two experiments (Experiment 1 with 4-year-olds (n = 25); Experiment 2 with , 4-, 5-, 7- and 11-year-olds (n = 81)) children were tested on four experimental conditions that enabled the effects of semantics and response set to be separated. Overall, our data suggest that response set is a major factor in creating the inhibitory demands of the day-night task in children of all ages. Results are discussed in relation to other inhibitory tasks.

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.

Comparative analysis of semantic localization accuracies between adult and pediatric DICOM CT images

NASA Astrophysics Data System (ADS)

Robertson, Duncan; Pathak, Sayan D.; Criminisi, Antonio; White, Steve; Haynor, David; Chen, Oliver; Siddiqui, Khan

2012-02-01

Existing literature describes a variety of techniques for semantic annotation of DICOM CT images, i.e. the automatic detection and localization of anatomical structures. Semantic annotation facilitates enhanced image navigation, linkage of DICOM image content and non-image clinical data, content-based image retrieval, and image registration. A key challenge for semantic annotation algorithms is inter-patient variability. However, while the algorithms described in published literature have been shown to cope adequately with the variability in test sets comprising adult CT scans, the problem presented by the even greater variability in pediatric anatomy has received very little attention. Most existing semantic annotation algorithms can only be extended to work on scans of both adult and pediatric patients by adapting parameters heuristically in light of patient size. In contrast, our approach, which uses random regression forests ('RRF'), learns an implicit model of scale variation automatically using training data. In consequence, anatomical structures can be localized accurately in both adult and pediatric CT studies without the need for parameter adaptation or additional information about patient scale. We show how the RRF algorithm is able to learn scale invariance from a combined training set containing a mixture of pediatric and adult scans. Resulting localization accuracy for both adult and pediatric data remains comparable with that obtained using RRFs trained and tested using only adult data.

Learning semantic histopathological representation for basal cell carcinoma classification

NASA Astrophysics Data System (ADS)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

Understanding in an instant: neurophysiological evidence for mechanistic language circuits in the brain.

PubMed

Pulvermüller, Friedemann; Shtyrov, Yury; Hauk, Olaf

2009-08-01

How long does it take the human mind to grasp the idea when hearing or reading a sentence? Neurophysiological methods looking directly at the time course of brain activity indexes of comprehension are critical for finding the answer to this question. As the dominant cognitive approaches, models of serial/cascaded and parallel processing, make conflicting predictions on the time course of psycholinguistic information access, they can be tested using neurophysiological brain activation recorded in MEG and EEG experiments. Seriality and cascading of lexical, semantic and syntactic processes receives support from late (latency approximately 1/2s) sequential neurophysiological responses, especially N400 and P600. However, parallelism is substantiated by early near-simultaneous brain indexes of a range of psycholinguistic processes, up to the level of semantic access and context integration, emerging already 100-250ms after critical stimulus information is present. Crucially, however, there are reliable latency differences of 20-50ms between early cortical area activations reflecting lexical, semantic and syntactic processes, which are left unexplained by current serial and parallel brain models of language. We here offer a mechanistic model grounded in cortical nerve cell circuits that builds upon neuroanatomical and neurophysiological knowledge and explains both near-simultaneous activations and fine-grained delays. A key concept is that of discrete distributed cortical circuits with specific inter-area topographies. The full activation, or ignition, of specifically distributed binding circuits explains the near-simultaneity of early neurophysiological indexes of lexical, syntactic and semantic processing. Activity spreading within circuits determined by between-area conduction delays accounts for comprehension-related regional activation differences in the millisecond range.

Brain-based individual difference measures of reading skill in deaf and hearing adults.

PubMed

Mehravari, Alison S; Emmorey, Karen; Prat, Chantel S; Klarman, Lindsay; Osterhout, Lee

2017-07-01

Most deaf children and adults struggle to read, but some deaf individuals do become highly proficient readers. There is disagreement about the specific causes of reading difficulty in the deaf population, and consequently, disagreement about the effectiveness of different strategies for teaching reading to deaf children. Much of the disagreement surrounds the question of whether deaf children read in similar or different ways as hearing children. In this study, we begin to answer this question by using real-time measures of neural language processing to assess if deaf and hearing adults read proficiently in similar or different ways. Hearing and deaf adults read English sentences with semantic, grammatical, and simultaneous semantic/grammatical errors while event-related potentials (ERPs) were recorded. The magnitude of individuals' ERP responses was compared to their standardized reading comprehension test scores, and potentially confounding variables like years of education, speechreading skill, and language background of deaf participants were controlled for. The best deaf readers had the largest N400 responses to semantic errors in sentences, while the best hearing readers had the largest P600 responses to grammatical errors in sentences. These results indicate that equally proficient hearing and deaf adults process written language in different ways, suggesting there is little reason to assume that literacy education should necessarily be the same for hearing and deaf children. The results also show that the most successful deaf readers focus on semantic information while reading, which suggests aspects of education that may promote improved literacy in the deaf population. Copyright © 2017 Elsevier Ltd. All rights reserved.

Semantic Interoperability of Health Risk Assessments

PubMed Central

Rajda, Jay; Vreeman, Daniel J.; Wei, Henry G.

2011-01-01

The health insurance and benefits industry has administered Health Risk Assessments (HRAs) at an increasing rate. These are used to collect data on modifiable health risk factors for wellness and disease management programs. However, there is significant variability in the semantics of these assessments, making it difficult to compare data sets from the output of 2 different HRAs. There is also an increasing need to exchange this data with Health Information Exchanges and Electronic Medical Records. To standardize the data and concepts from these tools, we outline a process to determine presence of certain common elements of modifiable health risk extracted from these surveys. This information is coded using concept identifiers, which allows cross-survey comparison and analysis. We propose that using LOINC codes or other universal coding schema may allow semantic interoperability of a variety of HRA tools across the industry, research, and clinical settings. PMID:22195174

Constraint-Based Abstract Semantics for Temporal Logic: A Direct Approach to Design and Implementation

NASA Astrophysics Data System (ADS)

Banda, Gourinath; Gallagher, John P.

interpretation provides a practical approach to verifying properties of infinite-state systems. We apply the framework of abstract interpretation to derive an abstract semantic function for the modal μ-calculus, which is the basis for abstract model checking. The abstract semantic function is constructed directly from the standard concrete semantics together with a Galois connection between the concrete state-space and an abstract domain. There is no need for mixed or modal transition systems to abstract arbitrary temporal properties, as in previous work in the area of abstract model checking. Using the modal μ-calculus to implement CTL, the abstract semantics gives an over-approximation of the set of states in which an arbitrary CTL formula holds. Then we show that this leads directly to an effective implementation of an abstract model checking algorithm for CTL using abstract domains based on linear constraints. The implementation of the abstract semantic function makes use of an SMT solver. We describe an implemented system for proving properties of linear hybrid automata and give some experimental results.

Semantic contribution to verbal short-term memory: are pleasant words easier to remember than neutral words in serial recall and serial recognition?

PubMed

Monnier, Catherine; Syssau, Arielle

2008-01-01

In the four experiments reported here, we examined the role of word pleasantness on immediate serial recall and immediate serial recognition. In Experiment 1, we compared verbal serial recall of pleasant and neutral words, using a limited set of items. In Experiment 2, we replicated Experiment 1 with an open set of words (i.e., new items were used on every trial). In Experiments 3 and 4, we assessed immediate serial recognition of pleasant and neutral words, using item sets from Experiments 1 and 2. Pleasantness was found to have a facilitation effect on both immediate serial recall and immediate serial recognition. This study supplies some new supporting arguments in favor of a semantic contribution to verbal short-term memory performance. The pleasantness effect observed in immediate serial recognition showed that, contrary to a number of earlier findings, performance on this task can also turn out to be dependent on semantic factors. The results are discussed in relation to nonlinguistic and psycholinguistic models of short-term memory.

English semantic word-pair norms and a searchable Web portal for experimental stimulus creation.

PubMed

Buchanan, Erin M; Holmes, Jessica L; Teasley, Marilee L; Hutchison, Keith A

2013-09-01

As researchers explore the complexity of memory and language hierarchies, the need to expand normed stimulus databases is growing. Therefore, we present 1,808 words, paired with their features and concept-concept information, that were collected using previously established norming methods (McRae, Cree, Seidenberg, & McNorgan Behavior Research Methods 37:547-559, 2005). This database supplements existing stimuli and complements the Semantic Priming Project (Hutchison, Balota, Cortese, Neely, Niemeyer, Bengson, & Cohen-Shikora 2010). The data set includes many types of words (including nouns, verbs, adjectives, etc.), expanding on previous collections of nouns and verbs (Vinson & Vigliocco Journal of Neurolinguistics 15:317-351, 2008). We describe the relation between our and other semantic norms, as well as giving a short review of word-pair norms. The stimuli are provided in conjunction with a searchable Web portal that allows researchers to create a set of experimental stimuli without prior programming knowledge. When researchers use this new database in tandem with previous norming efforts, precise stimuli sets can be created for future research endeavors.

[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.

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.

A mixed Rasch model of dual-process conditional reasoning.

PubMed

Bonnefon, Jean-François; Eid, Michael; Vautier, Stéphane; Jmel, Saïd

2008-05-01

A fine-grained dual-process approach to conditional reasoning is advocated: Responses to conditional syllogisms are reached through the operation of either one of two systems, each of which can rely on two different mechanisms. System1 relies either on pragmatic implicatures or on the retrieval of information from semantic memory; System2 operates first through inhibition of System1, then (but not always) through activation of analytical processes. It follows that reasoners will fall into one of four groups of increasing reasoning ability, each group being uniquely characterized by (a) the modal pattern of individual answers to blocks of affirming the consequent (AC), denying the antecedent (DA), and modus tollens (MT) syllogisms featuring the same conditional; and (b) the average rate of determinate answers to AC, DA, and MT. This account receives indirect support from the extant literature and direct support from a mixed Rasch model of responses given to 18 syllogisms by 486 adult reasoners.

Semantic representations in the temporal pole predict false memories

PubMed Central

Chadwick, Martin J.; Anjum, Raeesa S.; Kumaran, Dharshan; Schacter, Daniel L.; Spiers, Hugo J.; Hassabis, Demis

2016-01-01

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the “semantic hub” of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories. PMID:27551087

Social Semantics for an Effective Enterprise

NASA Technical Reports Server (NTRS)

Berndt, Sarah; Doane, Mike

2012-01-01

An evolution of the Semantic Web, the Social Semantic Web (s2w), facilitates knowledge sharing with "useful information based on human contributions, which gets better as more people participate." The s2w reaches beyond the search box to move us from a collection of hyperlinked facts, to meaningful, real time context. When focused through the lens of Enterprise Search, the Social Semantic Web facilitates the fluid transition of meaningful business information from the source to the user. It is the confluence of human thought and computer processing structured with the iterative application of taxonomies, folksonomies, ontologies, and metadata schemas. The importance and nuances of human interaction are often deemphasized when focusing on automatic generation of semantic markup, which results in dissatisfied users and unrealized return on investment. Users consistently qualify the value of information sets through the act of selection, making them the de facto stakeholders of the Social Semantic Web. Employers are the ultimate beneficiaries of s2w utilization with a better informed, more decisive workforce; one not achieved with an IT miracle technology, but by improved human-computer interactions. Johnson Space Center Taxonomist Sarah Berndt and Mike Doane, principal owner of Term Management, LLC discuss the planning, development, and maintenance stages for components of a semantic system while emphasizing the necessity of a Social Semantic Web for the Enterprise. Identification of risks and variables associated with layering the successful implementation of a semantic system are also modeled.

Semantic representations in the temporal pole predict false memories.

PubMed

Chadwick, Martin J; Anjum, Raeesa S; Kumaran, Dharshan; Schacter, Daniel L; Spiers, Hugo J; Hassabis, Demis

2016-09-06

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories.

Does task-irrelevant colour information create extraneous cognitive load? Evidence from a learning task.

PubMed

Miller, Paul; Hazan-Liran, Batel; Cohen, Danielle

2018-06-01

Previous studies have shown that task-irrelevant information impedes learning by creating extraneous cognitive load. But still open is whether such intrusion reflects a purely semantic phenomenon or whether it also stands for sheer perceptual interference. Using Cognitive Load Theory as a framework, this study aimed to answer this question by examining whether and how task-irrelevant colour information modifies extraneous cognitive load in relation to a new code-learning paradigm. For this purpose, university students were asked to learn, based on an example, associations between colour-related and colour-unrelated words and digits presented in black or in a mismatched ink colour. Evident costs in learning efficacy were found in learning the associations between words and digits for colour-related, but not for colour-unrelated, word stimuli. This suggests that interference by task-irrelevant information in learning stands for a mere semantic conflict. Implications of the findings for extraneous cognitive load on learning efficacy are discussed.

Where do dialectal effects on speech processing come from? Evidence from a cross-dialect investigation.

PubMed

Larraza, Saioa; Samuel, Arthur G; Oñederra, Miren Lourdes

2016-07-20

Accented speech has been seen as an additional impediment for speech processing; it usually adds linguistic and cognitive load to the listener's task. In the current study we analyse where the processing costs of regional dialects come from, a question that has not been answered yet. We quantify the proficiency of Basque-Spanish bilinguals who have different native dialects of Basque on many dimensions and test for costs at each of three levels of processing-phonemic discrimination, word recognition, and semantic processing. The ability to discriminate a dialect-specific contrast is affected by a bilingual's linguistic background less than lexical access is, and an individual's difficulty in lexical access is correlated with basic discrimination problems. Once lexical access is achieved, dialectal variation has little impact on semantic processing. The results are discussed in terms of the presence or absence of correlations between different processing levels. The implications of the results are considered for how models of spoken word recognition handle dialectal variation.

Effects of plausibility on structural priming.

PubMed

Christianson, Kiel; Luke, Steven G; Ferreira, Fernanda

2010-03-01

We report a replication and extension of Ferreira (2003), in which it was observed that native adult English speakers misinterpret passive sentences that relate implausible but not impossible semantic relationships (e.g., The angler was caught by the fish) significantly more often than they do plausible passives or plausible or implausible active sentences. In the experiment reported here, participants listened to the same plausible and implausible passive and active sentences as in Ferreira (2003), answered comprehension questions, and then orally described line drawings of simple transitive actions. The descriptions were analyzed as a measure of structural priming (Bock, 1986). Question accuracy data replicated Ferreira (2003). Production data yielded an interaction: Passive descriptions were produced more often after plausible passives and implausible actives. We interpret these results as indicative of a language processor that proceeds along differentiated morphosyntactic and semantic routes. The processor may end up adjudicating between conflicting outputs from these routes by settling on a "good enough" representation that is not completely faithful to the input.

Joint classification and contour extraction of large 3D point clouds

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2017-08-01

We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours. Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with >109 points.

Effects of semantic neighborhood density in abstract and concrete words.

PubMed

Reilly, Megan; Desai, Rutvik H

2017-12-01

Concrete and abstract words are thought to differ along several psycholinguistic variables, such as frequency and emotional content. Here, we consider another variable, semantic neighborhood density, which has received much less attention, likely because semantic neighborhoods of abstract words are difficult to measure. Using a corpus-based method that creates representations of words that emphasize featural information, the current investigation explores the relationship between neighborhood density and concreteness in a large set of English nouns. Two important observations emerge. First, semantic neighborhood density is higher for concrete than for abstract words, even when other variables are accounted for, especially for smaller neighborhood sizes. Second, the effects of semantic neighborhood density on behavior are different for concrete and abstract words. Lexical decision reaction times are fastest for words with sparse neighborhoods; however, this effect is stronger for concrete words than for abstract words. These results suggest that semantic neighborhood density plays a role in the cognitive and psycholinguistic differences between concrete and abstract words, and should be taken into account in studies involving lexical semantics. Furthermore, the pattern of results with the current feature-based neighborhood measure is very different from that with associatively defined neighborhoods, suggesting that these two methods should be treated as separate measures rather than two interchangeable measures of semantic neighborhoods. Copyright © 2017 Elsevier B.V. All rights reserved.

BioWarehouse: a bioinformatics database warehouse toolkit

PubMed Central

Lee, Thomas J; Pouliot, Yannick; Wagner, Valerie; Gupta, Priyanka; Stringer-Calvert, David WJ; Tenenbaum, Jessica D; Karp, Peter D

2006-01-01

Background This article addresses the problem of interoperation of heterogeneous bioinformatics databases. Results We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. Conclusion BioWarehouse embodies significant progress on the database integration problem for bioinformatics. PMID:16556315

BioWarehouse: a bioinformatics database warehouse toolkit.

PubMed

Lee, Thomas J; Pouliot, Yannick; Wagner, Valerie; Gupta, Priyanka; Stringer-Calvert, David W J; Tenenbaum, Jessica D; Karp, Peter D

2006-03-23

This article addresses the problem of interoperation of heterogeneous bioinformatics databases. We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. BioWarehouse embodies significant progress on the database integration problem for bioinformatics.

Biology Question Generation from a Semantic Network

NASA Astrophysics Data System (ADS)

Zhang, Lishan

Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students' learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student's current competence so that a suitable question could be selected based on the student's previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group. To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators. A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from Amazon Mechanical Turk, it turned out that the two types of questions performed very closely on all the three measures.

vSPARQL: A View Definition Language for the Semantic Web

PubMed Central

Shaw, Marianne; Detwiler, Landon T.; Noy, Natalya; Brinkley, James; Suciu, Dan

2010-01-01

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages. PMID:20800106

Causal Evidence for a Mechanism of Semantic Integration in the Angular Gyrus as Revealed by High-Definition Transcranial Direct Current Stimulation

PubMed Central

Peelle, Jonathan E.; Bonner, Michael F.; Grossman, Murray

2016-01-01

A defining aspect of human cognition is the ability to integrate conceptual information into complex semantic combinations. For example, we can comprehend “plaid” and “jacket” as individual concepts, but we can also effortlessly combine these concepts to form the semantic representation of “plaid jacket.” Many neuroanatomic models of semantic memory propose that heteromodal cortical hubs integrate distributed semantic features into coherent representations. However, little work has specifically examined these proposed integrative mechanisms and the causal role of these regions in semantic integration. Here, we test the hypothesis that the angular gyrus (AG) is critical for integrating semantic information by applying high-definition transcranial direct current stimulation (tDCS) to an fMRI-guided region-of-interest in the left AG. We found that anodal stimulation to the left AG modulated semantic integration but had no effect on a letter-string control task. Specifically, anodal stimulation to the left AG resulted in faster comprehension of semantically meaningful combinations like “tiny radish” relative to non-meaningful combinations, such as “fast blueberry,” when compared to the effects observed during sham stimulation and stimulation to a right-hemisphere control brain region. Moreover, the size of the effect from brain stimulation correlated with the degree of semantic coherence between the word pairs. These findings demonstrate that the left AG plays a causal role in the integration of lexical-semantic information, and that high-definition tDCS to an associative cortical hub can selectively modulate integrative processes in semantic memory. SIGNIFICANCE STATEMENT A major goal of neuroscience is to understand the neural basis of behaviors that are fundamental to human intelligence. One essential behavior is the ability to integrate conceptual knowledge from semantic memory, allowing us to construct an almost unlimited number of complex concepts from a limited set of basic constituents (e.g., “leaf” and “wet” can be combined into the more complex representation “wet leaf”). Here, we present a novel approach to studying integrative processes in semantic memory by applying focal brain stimulation to a heteromodal cortical hub implicated in semantic processing. Our findings demonstrate a causal role of the left angular gyrus in lexical-semantic integration and provide motivation for novel therapeutic applications in patients with lexical-semantic deficits. PMID:27030767

Causal Evidence for a Mechanism of Semantic Integration in the Angular Gyrus as Revealed by High-Definition Transcranial Direct Current Stimulation.

PubMed

Price, Amy Rose; Peelle, Jonathan E; Bonner, Michael F; Grossman, Murray; Hamilton, Roy H

2016-03-30

A defining aspect of human cognition is the ability to integrate conceptual information into complex semantic combinations. For example, we can comprehend "plaid" and "jacket" as individual concepts, but we can also effortlessly combine these concepts to form the semantic representation of "plaid jacket." Many neuroanatomic models of semantic memory propose that heteromodal cortical hubs integrate distributed semantic features into coherent representations. However, little work has specifically examined these proposed integrative mechanisms and the causal role of these regions in semantic integration. Here, we test the hypothesis that the angular gyrus (AG) is critical for integrating semantic information by applying high-definition transcranial direct current stimulation (tDCS) to an fMRI-guided region-of-interest in the left AG. We found that anodal stimulation to the left AG modulated semantic integration but had no effect on a letter-string control task. Specifically, anodal stimulation to the left AG resulted in faster comprehension of semantically meaningful combinations like "tiny radish" relative to non-meaningful combinations, such as "fast blueberry," when compared to the effects observed during sham stimulation and stimulation to a right-hemisphere control brain region. Moreover, the size of the effect from brain stimulation correlated with the degree of semantic coherence between the word pairs. These findings demonstrate that the left AG plays a causal role in the integration of lexical-semantic information, and that high-definition tDCS to an associative cortical hub can selectively modulate integrative processes in semantic memory. A major goal of neuroscience is to understand the neural basis of behaviors that are fundamental to human intelligence. One essential behavior is the ability to integrate conceptual knowledge from semantic memory, allowing us to construct an almost unlimited number of complex concepts from a limited set of basic constituents (e.g., "leaf" and "wet" can be combined into the more complex representation "wet leaf"). Here, we present a novel approach to studying integrative processes in semantic memory by applying focal brain stimulation to a heteromodal cortical hub implicated in semantic processing. Our findings demonstrate a causal role of the left angular gyrus in lexical-semantic integration and provide motivation for novel therapeutic applications in patients with lexical-semantic deficits. Copyright © 2016 the authors 0270-6474/16/363829-10$15.00/0.

A normative set of 98 pairs of nonsensical pictures (droodles).

PubMed

Nishimoto, Takehiko; Ueda, Takashi; Miyawaki, Kaori; Une, Yuko; Takahashi, Masaru

2010-08-01

Our purpose in the present study is to provide a normative set of nonsensical pictures known as droodles and to demonstrate the role of semantic comprehension in facilitating recall of pictorial stimuli. The set consists of 98 pairs of droodles. Experiment 1 standardized these pictorial stimuli with respect to several variables, such as appropriateness of verbal labels, relationship between two droodles, and correct recall. Appropriateness of verbal labels was rated higher for pictures presented in pairs than for pictures presented singly. Experiment 2 used the standardized set of droodles in a recall experiment similar to those of Bower, Karlin, and Dueck (1975) and others. As we expected, semantic interpretation can strongly facilitate recall. Multiple regression analysis showed that several measures had significant power of explanation for recall performance. The full set of norms and pictures from this article may be downloaded from http://brm.psychonomic-journals.org/content/supplemental.

OlyMPUS - The Ontology-based Metadata Portal for Unified Semantics

NASA Astrophysics Data System (ADS)

Huffer, E.; Gleason, J. L.

2015-12-01

The Ontology-based Metadata Portal for Unified Semantics (OlyMPUS), funded by the NASA Earth Science Technology Office Advanced Information Systems Technology program, is an end-to-end system designed to support data consumers and data providers, enabling the latter to register their data sets and provision them with the semantically rich metadata that drives the Ontology-Driven Interactive Search Environment for Earth Sciences (ODISEES). OlyMPUS leverages the semantics and reasoning capabilities of ODISEES to provide data producers with a semi-automated interface for producing the semantically rich metadata needed to support ODISEES' data discovery and access services. It integrates the ODISEES metadata search system with multiple NASA data delivery tools to enable data consumers to create customized data sets for download to their computers, or for NASA Advanced Supercomputing (NAS) facility registered users, directly to NAS storage resources for access by applications running on NAS supercomputers. A core function of NASA's Earth Science Division is research and analysis that uses the full spectrum of data products available in NASA archives. Scientists need to perform complex analyses that identify correlations and non-obvious relationships across all types of Earth System phenomena. Comprehensive analytics are hindered, however, by the fact that many Earth science data products are disparate and hard to synthesize. Variations in how data are collected, processed, gridded, and stored, create challenges for data interoperability and synthesis, which are exacerbated by the sheer volume of available data. Robust, semantically rich metadata can support tools for data discovery and facilitate machine-to-machine transactions with services such as data subsetting, regridding, and reformatting. Such capabilities are critical to enabling the research activities integral to NASA's strategic plans. However, as metadata requirements increase and competing standards emerge, metadata provisioning becomes increasingly burdensome to data producers. The OlyMPUS system helps data providers produce semantically rich metadata, making their data more accessible to data consumers, and helps data consumers quickly discover and download the right data for their research.

Using a high-dimensional graph of semantic space to model relationships among words

PubMed Central

Jackson, Alice F.; Bolger, Donald J.

2014-01-01

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD). PMID:24860525

Using a high-dimensional graph of semantic space to model relationships among words.

PubMed

Jackson, Alice F; Bolger, Donald J

2014-01-01

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD).

Towards a semantic medical Web: HealthCyberMap's tool for building an RDF metadata base of health information resources based on the Qualified Dublin Core Metadata Set.

PubMed

Boulos, Maged N; Roudsari, Abdul V; Carson, Ewart R

2002-07-01

HealthCyberMap (http://healthcybermap.semanticweb.org/) aims at mapping Internet health information resources in novel ways for enhanced retrieval and navigation. This is achieved by collecting appropriate resource metadata in an unambiguous form that preserves semantics. We modelled a qualified Dublin Core (DC) metadata set ontology with extra elements for resource quality and geographical provenance in Prot g -2000. A metadata collection form helps acquiring resource instance data within Prot g . The DC subject field is populated with UMLS terms directly imported from UMLS Knowledge Source Server using UMLS tab, a Prot g -2000 plug-in. The project is saved in RDFS/RDF. The ontology and associated form serve as a free tool for building and maintaining an RDF medical resource metadata base. The UMLS tab enables browsing and searching for concepts that best describe a resource, and importing them to DC subject fields. The resultant metadata base can be used with a search and inference engine, and have textual and/or visual navigation interface(s) applied to it, to ultimately build a medical Semantic Web portal. Different ways of exploiting Prot g -2000 RDF output are discussed. By making the context and semantics of resources, not merely their raw text and formatting, amenable to computer 'understanding,' we can build a Semantic Web that is more useful to humans than the current Web. This requires proper use of metadata and ontologies. Clinical codes can reliably describe the subjects of medical resources, establish the semantic relationships (as defined by underlying coding scheme) between related resources, and automate their topical categorisation.

BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.

PubMed

Sogancioglu, Gizem; Öztürk, Hakime; Özgür, Arzucan

2017-07-15

The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text. We propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods. The experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric. A web-based system for biomedical semantic sentence similarity computation, the source code, and the annotated benchmark data set are available at: http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . gizemsogancioglu@gmail.com or arzucan.ozgur@boun.edu.tr. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

UltiMatch-NL: A Web Service Matchmaker Based on Multiple Semantic Filters

PubMed Central

Mohebbi, Keyvan; Ibrahim, Suhaimi; Zamani, Mazdak; Khezrian, Mojtaba

2014-01-01

In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters. PMID:25157872

Comparison of affective and semantic priming in different SOA.

PubMed

Jiang, Zhongqing; Qu, Yuhong; Xiao, Yanli; Wu, Qi; Xia, Likun; Li, Wenhui; Liu, Ying

2016-11-01

Researchers have been at odds on whether affective or semantic priming is faster or stronger. The present study selects a series of facial expression photos and words, which have definite emotional meaning or gender meaning, to set up experiment including both affective and semantic priming. The intensity of emotion and gender information in the prime as well as the strength of emotional or semantic (in gender) relationship between the prime and the target is matched. Three groups of participants are employed separately in our experiment varied with stimulus onset asynchrony (SOA) as 50, 250 or 500 ms. The results show that the difference between two types of priming effect is revealed when the SOA is at 50 ms, in which the affective priming effect is presented when the prime has negative emotion. It indicates that SOA can affect the comparison between the affective and semantic priming, and the former takes the priority in the automatic processing level.

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.

UltiMatch-NL: a Web service matchmaker based on multiple semantic filters.

PubMed

Mohebbi, Keyvan; Ibrahim, Suhaimi; Zamani, Mazdak; Khezrian, Mojtaba

2014-01-01

In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.

Everyday conversation requires cognitive inference: neural bases of comprehending implicated meanings in conversations.

PubMed

Jang, Gijeong; Yoon, Shin-ae; Lee, Sung-Eun; Park, Haeil; Kim, Joohan; Ko, Jeong Hoon; Park, Hae-Jeong

2013-11-01

In ordinary conversations, literal meanings of an utterance are often quite different from implicated meanings and the inference about implicated meanings is essentially required for successful comprehension of the speaker's utterances. Inference of finding implicated meanings is based on the listener's assumption that the conversational partner says only relevant matters according to the maxim of relevance in Grice's theory of conversational implicature. To investigate the neural correlates of comprehending implicated meanings under the maxim of relevance, a total of 23 participants underwent an fMRI task with a series of conversational pairs, each consisting of a question and an answer. The experimental paradigm was composed of three conditions: explicit answers, moderately implicit answers, and highly implicit answers. Participants were asked to decide whether the answer to the Yes/No question meant 'Yes' or 'No'. Longer reaction time was required for the highly implicit answers than for the moderately implicit answers without affecting the accuracy. The fMRI results show that the left anterior temporal lobe, left angular gyrus, and left posterior middle temporal gyrus had stronger activation in both moderately and highly implicit conditions than in the explicit condition. Comprehension of highly implicit answers had increased activations in additional regions including the left inferior frontal gyrus, left medial prefrontal cortex, left posterior cingulate cortex and right anterior temporal lobe. The activation results indicate involvement of these regions in the inference process to build coherence between literally irrelevant but pragmatically associated utterances under the maxim of relevance. Especially, the left anterior temporal lobe showed high sensitivity to the level of implicitness and showed increased activation for highly versus moderately implicit conditions, which imply its central role in inference such as semantic integration. The right hemisphere activation, uniquely found in the anterior temporal lobe for highly implicit utterances, suggests its competence for integrating distant concepts in implied utterances under the relevance principle. Copyright © 2013 Elsevier Inc. All rights reserved.

Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases

PubMed Central

2011-01-01

Background Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions. Methods An application-specific ontology was developed for managing TMA and DNA microarray databases by semantic web technologies. Data were represented as Resource Description Framework (RDF) according to the framework of the ontology. Applications for hypothesis testing (Xperanto-RDF) for TMA data were designed and implemented by (1) formulating the syntactic and semantic structures of the hypotheses derived from TMA experiments, (2) formulating SPARQLs to reflect the semantic structures of the hypotheses, and (3) performing statistical test with the result sets returned by the SPARQLs. Results When a user designs a hypothesis in Xperanto-RDF and submits it, the hypothesis can be tested against TMA experimental data stored in Xperanto-RDF. When we evaluated four previously validated hypotheses as an illustration, all the hypotheses were supported by Xperanto-RDF. Conclusions We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited. PMID:21342584

When does word meaning affect immediate serial recall in semantic dementia?

PubMed

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

2004-03-01

Patients with semantic dementia can show superior immediate recall of words that they still understand relatively well, as compared with more semantically degraded words, suggesting that conceptual knowledge makes a major contribution to phonological short-term memory. However, a number of studies have failed to show such a recall difference, challenging this view. We examined the effect of several methodological factors on the recall of known and degraded words in 4 patients with semantic dementia, in order to investigate possible reasons for this discrepancy. In general, our patients did exhibit poorer recall of the degraded words and made more phonological errors on these items. In addition, set size affected the magnitude of the recall advantage for known words. This finding suggests that semantic degradation influenced the rate of learning in the immediate recall task when the same items were presented repeatedly. The methods used to select known and degraded items also impacted on the recall difference. List length, however, did not affect the advantage for known words. The coherence of items in phonological short-term memory was affected by their semantic status, but not by the length of the material to be retained. The implications of these findings for the role of semantic and phonological representations in verbal short-term memory are discussed.

Improving life sciences information retrieval using semantic web technology.

PubMed

Quan, Dennis

2007-05-01

The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model; designing semantic lenses that extract contextually relevant subsets of information; and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.

Preliminary Development of a Multidimensional Semantic Patient Experience Measurement Questionnaire.

PubMed

Kleiss, James A

2016-10-01

The purpose of this research was to assess the utility and reliability of a multidimensional patient experience measurement questionnaire in a clinical setting. Patient experience has emerged as an important metric for quality of healthcare. A number of separate concepts have been used to measure patient experience, but psychological research suggests that subjective experience is actually a composite of several independent concepts including: (a) evaluation/valence, (b) potency/control, (c) activity/arousal, and (d) novelty. The present research evaluates the reliability of a multidimensional patient experience measurement questionnaire in a clinical setting. A multidimensional semantic differential questionnaire was developed to measure the four underlying semantic dimensions of patient experience mentioned above. A group of 60 patients used the questionnaire to assess prescan expectations and postscan experience of a magnetic resonance scan. Data for one patient were deleted because their scan was interrupted. Results revealed more positive evaluation/valence, higher potency/control, and lower activity/arousal for postscan ratings compared to prescan expectations. Ratings of novelty were neutral in both the prescan and the postscan conditions. Subsequent analysis suggested that internal consistency for some concepts could be improved by replacing several specific rating scales. Present results provide evidence of the utility and reliability of a multidimensional semantic questionnaire for measuring patient experience in an actual clinical setting. Recommendations to improve internal consistency for the concepts potency/control, activity/arousal, and novelty were also provided. © The Author(s) 2016.

An ontological system for interoperable spatial generalisation in biodiversity monitoring

NASA Astrophysics Data System (ADS)

Nieland, Simon; Moran, Niklas; Kleinschmit, Birgit; Förster, Michael

2015-11-01

Semantic heterogeneity remains a barrier to data comparability and standardisation of results in different fields of spatial research. Because of its thematic complexity, differing acquisition methods and national nomenclatures, interoperability of biodiversity monitoring information is especially difficult. Since data collection methods and interpretation manuals broadly vary there is a need for automatised, objective methodologies for the generation of comparable data-sets. Ontology-based applications offer vast opportunities in data management and standardisation. This study examines two data-sets of protected heathlands in Germany and Belgium which are based on remote sensing image classification and semantically formalised in an OWL2 ontology. The proposed methodology uses semantic relations of the two data-sets, which are (semi-)automatically derived from remote sensing imagery, to generate objective and comparable information about the status of protected areas by utilising kernel-based spatial reclassification. This automatised method suggests a generalisation approach, which is able to generate delineation of Special Areas of Conservation (SAC) of the European biodiversity Natura 2000 network. Furthermore, it is able to transfer generalisation rules between areas surveyed with varying acquisition methods in different countries by taking into account automated inference of the underlying semantics. The generalisation results were compared with the manual delineation of terrestrial monitoring. For the different habitats in the two sites an accuracy of above 70% was detected. However, it has to be highlighted that the delineation of the ground-truth data inherits a high degree of uncertainty, which is discussed in this study.

45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... code sets inherent to a transaction, and not related to the format of the transaction. Data elements... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...

45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... sets inherent to a transaction, and not related to the format of the transaction. Data elements that... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...

45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... sets inherent to a transaction, and not related to the format of the transaction. Data elements that... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...

45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... sets inherent to a transaction, and not related to the format of the transaction. Data elements that... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...

45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... code sets inherent to a transaction, and not related to the format of the transaction. Data elements... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...

The modulatory influence of the functional COMT Val158Met polymorphism on lexical decisions and semantic priming.

PubMed

Reuter, Martin; Montag, Christian; Peters, Kristina; Kocher, Anne; Kiefer, Markus

2009-01-01

The role of the prefrontal Cortex (PFC) in higher cognitive functions - including working memory, conflict resolution, set shifting and semantic processing - has been demonstrated unequivocally. Despite the great heterogeneity among tasks measuring these phenotypes, due in part to the different cognitive sub-processes implied and the specificity of the stimulus material used, there is agreement that all of these tasks recruit an executive control system located in the PFC. On a biochemical level it is known that the dopaminergic system plays an important role in executive control functions. Evidence comes from molecular genetics relating the functional COMT Val158Met polymorphism to working memory and set shifting. In order determine whether this pattern of findings generalises to linguistic and semantic processing, we investigated the effects of the COMT Val158Met polymorphism in lexical decision making using masked and unmasked versions of the semantic priming paradigm on N = 104 healthy subjects. Although we observed strong priming effects in all conditions (masked priming, unmasked priming with short/long stimulus asynchronies (SOAs), direct and indirect priming), COMT was not significantly related to priming, suggesting no reliable influence on semantic processing. However, COMT Val158Met was strongly associated with lexical decision latencies in all priming conditions if considered separately, explaining between 9 and 14.5% of the variance. Therefore, the findings indicate that COMT mainly influences more general executive control functions in the PFC supporting the speed of lexical decisions.

Selective preservation of memory for people in the context of semantic memory disorder: patterns of association and dissociation.

PubMed

Lyons, Frances; Kay, Janice; Hanley, J Richard; Haslam, Catherine

2006-01-01

A number of single cases in the literature demonstrate that person-specific semantic knowledge can be selectively impaired after acquired brain damage compared with that of object categories. However, there has been little unequivocal evidence for the reverse dissociation, selective preservation of person-specific semantic knowledge. Recently, three case studies have been published which provide support for the claim that such knowledge can be selectively preserved [Kay, J., & Hanley, J. R. (2002). Preservation of memory for people in semantic memory disorder: Further category-specific semantic dissociation. Cognitive Neuropsychology, 19, 113-134; Lyons, F., Hanley, J. R., & Kay, J. (2002). Anomia for common names and geographical names with preserved retrieval of names of people: A semantic memory disorder. Cortex, 38, 23-35; Thompson, S. A, Graham, K. S., Williams, G., Patterson, K., Kapur, N., & Hodges, J. R. (2004). Dissociating person-specific from general semantic knowledge: Roles of the left and right temporal lobes. Neuropsychologia, 42, 359-370]. In this paper, we supply further evidence from a series of 18 patients with acquired language disorder. Of this set, a number were observed to be impaired on tests of semantic association and word-picture matching using names of object categories (e.g. objects, animals and foods), but preserved on similar tests using names of famous people. Careful methodology was applied to match object and person-specific categories for item difficulty. The study also examined whether preservation of person-specific semantic knowledge was associated with preservation of knowledge of 'biological categories' such as fruit and vegetables and animals, or with preservation of 'token' knowledge of singular categories such as countries. The findings are discussed in the context of a variety of accounts that examine whether semantic memory has a categorical structure.

Flynn Effects on Sub-Factors of Episodic and Semantic Memory: Parallel Gains over Time and the Same Set of Determining Factors

ERIC Educational Resources Information Center

Ronnlund, Michael; Nilsson, Lars-Goran.

2009-01-01

The study examined the extent to which time-related gains in cognitive performance, so-called Flynn effects, generalize across sub-factors of episodic memory (recall and recognition) and semantic memory (knowledge and fluency). We conducted time-sequential analyses of data drawn from the Betula prospective cohort study, involving four age-matched…

The geometric semantics of algebraic quantum mechanics.

PubMed

Cruz Morales, John Alexander; Zilber, Boris

2015-08-06

In this paper, we will present an ongoing project that aims to use model theory as a suitable mathematical setting for studying the formalism of quantum mechanics. We argue that this approach provides a geometric semantics for such a formalism by means of establishing a (non-commutative) duality between certain algebraic and geometric objects. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A Mathematical Analysis of Semantic Maps, with Theoretical and Applied Implications for Blended Learning Software

ERIC Educational Resources Information Center

Tang, Michael; David, Hyerle; Byrne, Roxanne; Tran, John

2012-01-01

This paper is a mathematical (Boolean) analysis a set of cognitive maps called Thinking Maps[R], based on Albert Upton's semantic principles developed in his seminal works, Design for Thinking (1961) and Creative Analysis (1961). Albert Upton can be seen as a brilliant thinker who was before his time or after his time depending on the future of…

The "Individualised Accounting Questions" Technique: Using Excel to Generate Quantitative Exercises for Large Classes with Unique Individual Answers

ERIC Educational Resources Information Center

Nnadi, Matthias; Rosser, Mike

2014-01-01

The "individualised accounting questions" (IAQ) technique set out in this paper encourages independent active learning. It enables tutors to set individualised accounting questions and construct an answer grid that can be used for any number of students, with numerical values for each student's answers based on their student enrolment…

The semantic richness of abstract concepts

PubMed Central

Recchia, Gabriel; Jones, Michael N.

2012-01-01

We contrasted the predictive power of three measures of semantic richness—number of features (NFs), contextual dispersion (CD), and a novel measure of number of semantic neighbors (NSN)—for a large set of concrete and abstract concepts on lexical decision and naming tasks. NSN (but not NF) facilitated processing for abstract concepts, while NF (but not NSN) facilitated processing for the most concrete concepts, consistent with claims that linguistic information is more relevant for abstract concepts in early processing. Additionally, converging evidence from two datasets suggests that when NSN and CD are controlled for, the features that most facilitate processing are those associated with a concept's physical characteristics and real-world contexts. These results suggest that rich linguistic contexts (many semantic neighbors) facilitate early activation of abstract concepts, whereas concrete concepts benefit more from rich physical contexts (many associated objects and locations). PMID:23205008

Practical Semantic Astronomy

NASA Astrophysics Data System (ADS)

Graham, Matthew; Gray, N.; Burke, D.

2010-01-01

Many activities in the era of data-intensive astronomy are predicated upon some transference of domain knowledge and expertise from human to machine. The semantic infrastructure required to support this is no longer a pipe dream of computer science but a set of practical engineering challenges, more concerned with deployment and performance details than AI abstractions. The application of such ideas promises to help in such areas as contextual data access, exploiting distributed annotation and heterogeneous sources, and intelligent data dissemination and discovery. In this talk, we will review the status and use of semantic technologies in astronomy, particularly to address current problems in astroinformatics, with such projects as SKUA and AstroCollation.

Taxonomy, Ontology and Semantics at Johnson Space Center

NASA Technical Reports Server (NTRS)

Berndt, Sarah Ann

2011-01-01

At NASA Johnson Space Center (JSC), the Chief Knowledge Officer has been developing the JSC Taxonomy to capitalize on the accomplishments of yesterday while maintaining the flexibility needed for the evolving information environment of today. A clear vision and scope for the semantic system is integral to its success. The vision for the JSC Taxonomy is to connect information stovepipes to present a unified view for information and knowledge across the Center, across organizations, and across decades. Semantic search at JSC means seemless integration of disparate information sets into a single interface. Ever increasing use, interest, and organizational participation mark successful integration and provide the framework for future application.

Spatiotemporal-Thematic Data Processing for the Semantic Web

NASA Astrophysics Data System (ADS)

Hakimpour, Farshad; Aleman-Meza, Boanerges; Perry, Matthew; Sheth, Amit

This chapter presents practical approaches to data processing in the space, time and theme dimensions using existing Semantic Web technologies. It describes how we obtain geographic and event data from Internet sources and also how we integrate them into an RDF store. We briefly introduce a set of functionalities in space, time and semantics. These functionalities are implemented based on our existing technology for main-memory-based RDF data processing developed at the LSDIS Lab. A number of these functionalities are exposed as REST Web services. We present two sample client-side applications that are developed using a combination of our services with Google Maps service.

Categorizing words through semantic memory navigation

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, J.; Arenas, A.

2010-03-01

Semantic memory is the cognitive system devoted to storage and retrieval of conceptual knowledge. Empirical data indicate that semantic memory is organized in a network structure. Everyday experience shows that word search and retrieval processes provide fluent and coherent speech, i.e. are efficient. This implies either that semantic memory encodes, besides thousands of words, different kind of links for different relationships (introducing greater complexity and storage costs), or that the structure evolves facilitating the differentiation between long-lasting semantic relations from incidental, phenomenological ones. Assuming the latter possibility, we explore a mechanism to disentangle the underlying semantic backbone which comprises conceptual structure (extraction of categorical relations between pairs of words), from the rest of information present in the structure. To this end, we first present and characterize an empirical data set modeled as a network, then we simulate a stochastic cognitive navigation on this topology. We schematize this latter process as uncorrelated random walks from node to node, which converge to a feature vectors network. By doing so we both introduce a novel mechanism for information retrieval, and point at the problem of category formation in close connection to linguistic and non-linguistic experience.

Linked Data: what does it offer Earth Sciences?

NASA Astrophysics Data System (ADS)

Cox, Simon; Schade, Sven

2010-05-01

'Linked Data' is a current buzz-phrase promoting access to various forms of data on the internet. It starts from the two principles that have underpinned the architecture and scalability of the World Wide Web: 1. Universal Resource Identifiers - using the http protocol which is supported by the DNS system. 2. Hypertext - in which URIs of related resources are embedded within a document. Browsing is the key mode of interaction, with traversal of links between resources under control of the client. Linked Data also adds, or re-emphasizes: • Content negotiation - whereby the client uses http headers to tell the service what representation of a resource is acceptable, • Semantic Web principles - formal semantics for links, following the RDF data model and encoding, and • The 'mashup' effect - in which original and unexpected value may emerge from reuse of data, even if published in raw or unpolished form. Linked Data promotes typed links to all kinds of data, so is where the semantic web meets the 'deep web', i.e. resources which may be accessed using web protocols, but are in representations not indexed by search engines. Earth sciences are data rich, but with a strong legacy of specialized formats managed and processed by disconnected applications. However, most contemporary research problems require a cross-disciplinary approach, in which the heterogeneity resulting from that legacy is a significant challenge. In this context, Linked Data clearly has much to offer the earth sciences. But, there are some important questions to answer. What is a resource? Most earth science data is organized in arrays and databases. A subset useful for a particular study is usually identified by a parameterized query. The Linked Data paradigm emerged from the world of documents, and will often only resolve data-sets. It is impractical to create even nested navigation resources containing links to all potentially useful objects or subsets. From the viewpoint of human user interfaces, the browse metaphor, which has been such an important part of the success of the web, must be augmented with other interaction mechanisms, including query. What are the impacts on search and metadata? Hypertext provides links selected by the page provider. However, science should endeavor to be exhaustive in its use of data. Resource discovery through links must be supplemented by more systematic data discovery through search. Conversely, the crawlers that generate search indexes must be fed by resource providers (a) serving navigation pages with links to every dataset (b) adding enough 'metadata' (semantics) on each link to effectively populate the indexes. Linked Data makes this easier due to its integration with semantic web technologies, including structured vocabularies. What is the relation between structured data and Linked Data? Linked Data has focused on web-pages (primarily HTML) for human browsing, and RDF for semantics, assuming that other representations are opaque. However, this overlooks the wealth of XML data on the web, some of which is structured according to XML Schemas that provide semantics. Technical applications can use content-negotiation to get a structured representation, and exploit its semantics. Particularly relevant for earth sciences are data representations based on OGC Geography Markup Language (GML), such as GeoSciML, O&M and MOLES. GML was strongly influenced by RDF, and typed links are intrinsic: xlink:href plays the role that rdf:resource does in RDF representations. Services which expose GML-formatted resources (such as OGC Web Feature Service) are a prototype of Linked Data. Giving credit where it is due. Organizations investing in data collection may be reluctant to publish the raw data prior to completing an initial analysis. To encourage early data publication the system must provide suitable incentives, and citation analysis must recognize the increasing diversity of publication routes and forms. Linked Data makes it easier to include rich citation information when data is both published and used.

Mind-Sets for a Happier Life.

ERIC Educational Resources Information Center

Webber, Jo

1997-01-01

Discusses how irrational beliefs can be disputed and replaced with alternative, healthier mind-sets. Focuses on cognitive restructuring theory, changing unhealthy thoughts, disputation, semantics, and adopting alternative mind-sets, such as life is bizarre and funny. Claims that using words that indicate preferences and possibilities can help…

Metacognitive effects of initial question difficulty on subsequent memory performance.

PubMed

Pansky, Ainat; Goldsmith, Morris

2014-10-01

In two experiments, we examined whether relative retrieval fluency (the relative ease or difficulty of answering questions from memory) would be translated, via metacognitive monitoring and control processes, into an overt effect on the controlled behavior-that is, the decision whether to answer a question or abstain. Before answering a target set of multiple-choice general-knowledge questions (intermediate-difficulty questions in Exp. 1, deceptive questions in Exp. 2), the participants first answered either a set of difficult questions or a set of easy questions. For each question, they provided a forced-report answer, followed by a subjective assessment of the likelihood that their answer was correct (confidence) and by a free-report control decision-whether or not to report the answer for a potential monetary bonus (or penalty). The participants' ability to answer the target questions (forced-report proportion correct) was unaffected by the initial question difficulty. However, a predicted metacognitive contrast effect was observed: When the target questions were preceded by a set of difficult rather than easy questions, the participants were more confident in their answers to the target questions, and hence were more likely to report them, thus increasing the quantity of freely reported correct information. The option of free report was more beneficial after initial question difficulty than after initial question ease, in terms of both the gain in accuracy (Exp. 2) and a smaller cost in quantity (Exps. 1 and 2). These results demonstrate that changes in subjective experience can influence metacognitive monitoring and control, thereby affecting free-report memory performance independently of forced-report performance.

How to Constrain and Maintain a Lexicon for the Treatment of Progressive Semantic Naming Deficits: Principles of Item Selection for Formal Semantic Therapy

PubMed Central

Reilly, Jamie

2015-01-01

The progressive degradation of semantic memory is a common feature of many forms of dementia, including Alzheimer’s Disease and the semantic variant of Primary Progressive Aphasia (svPPA). One of the most functionally debilitating effects of this semantic impairment is the inability to name common people and objects (i.e., anomia). Clinical management of a progressive, semantically-based anomia presents extraordinary challenge for neurorehabilitation. Techniques such as errorless learning and spaced-retrieval training show promise for retraining forgotten words. However, we lack complementary detail about what to train (i.e., item selection) and how to flexibly adapt the training to a declining cognitive system. In this position paper, I weigh the relative merits of several treatment rationales (e.g., restore vs. compensate) and advocate for maintenance of known words over reacquisition of forgotten knowledge in the context of semantic treatment paradigms. I propose a system for generating an item pool and outline a set of core principles for training and sustaining a micro-lexicon consisting of approximately 100 words. These principles are informed by lessons learned over the course of a Phase I treatment study targeting language maintenance over a 5-year span in Alzheimer’s Disease and Frontotemporal Degeneration. Finally, I propose a semantic training approach that capitalizes on lexical frequency and repeated training on conceptual structure to offset the loss of key vocabulary as disease severity worsens. PMID:25609229

Semantic Neighborhood Effects for Abstract versus Concrete Words

PubMed Central

Danguecan, Ashley N.; Buchanan, Lori

2016-01-01

Studies show that semantic effects may be task-specific, and thus, that semantic representations are flexible and dynamic. Such findings are critical to the development of a comprehensive theory of semantic processing in visual word recognition, which should arguably account for how semantic effects may vary by task. It has been suggested that semantic effects are more directly examined using tasks that explicitly require meaning processing relative to those for which meaning processing is not necessary (e.g., lexical decision task). The purpose of the present study was to chart the processing of concrete versus abstract words in the context of a global co-occurrence variable, semantic neighborhood density (SND), by comparing word recognition response times (RTs) across four tasks varying in explicit semantic demands: standard lexical decision task (with non-pronounceable non-words), go/no-go lexical decision task (with pronounceable non-words), progressive demasking task, and sentence relatedness task. The same experimental stimulus set was used across experiments and consisted of 44 concrete and 44 abstract words, with half of these being low SND, and half being high SND. In this way, concreteness and SND were manipulated in a factorial design using a number of visual word recognition tasks. A consistent RT pattern emerged across tasks, in which SND effects were found for abstract (but not necessarily concrete) words. Ultimately, these findings highlight the importance of studying interactive effects in word recognition, and suggest that linguistic associative information is particularly important for abstract words. PMID:27458422

Semantic Neighborhood Effects for Abstract versus Concrete Words.

PubMed

Danguecan, Ashley N; Buchanan, Lori

2016-01-01

Studies show that semantic effects may be task-specific, and thus, that semantic representations are flexible and dynamic. Such findings are critical to the development of a comprehensive theory of semantic processing in visual word recognition, which should arguably account for how semantic effects may vary by task. It has been suggested that semantic effects are more directly examined using tasks that explicitly require meaning processing relative to those for which meaning processing is not necessary (e.g., lexical decision task). The purpose of the present study was to chart the processing of concrete versus abstract words in the context of a global co-occurrence variable, semantic neighborhood density (SND), by comparing word recognition response times (RTs) across four tasks varying in explicit semantic demands: standard lexical decision task (with non-pronounceable non-words), go/no-go lexical decision task (with pronounceable non-words), progressive demasking task, and sentence relatedness task. The same experimental stimulus set was used across experiments and consisted of 44 concrete and 44 abstract words, with half of these being low SND, and half being high SND. In this way, concreteness and SND were manipulated in a factorial design using a number of visual word recognition tasks. A consistent RT pattern emerged across tasks, in which SND effects were found for abstract (but not necessarily concrete) words. Ultimately, these findings highlight the importance of studying interactive effects in word recognition, and suggest that linguistic associative information is particularly important for abstract words.

vSPARQL: a view definition language for the semantic web.

PubMed

Shaw, Marianne; Detwiler, Landon T; Noy, Natalya; Brinkley, James; Suciu, Dan

2011-02-01

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages. Copyright © 2010 Elsevier Inc. All rights reserved.

Conceptual Foundations of Quantum Mechanics:. the Role of Evidence Theory, Quantum Sets, and Modal Logic

NASA Astrophysics Data System (ADS)

Resconi, Germano; Klir, George J.; Pessa, Eliano

Recognizing that syntactic and semantic structures of classical logic are not sufficient to understand the meaning of quantum phenomena, we propose in this paper a new interpretation of quantum mechanics based on evidence theory. The connection between these two theories is obtained through a new language, quantum set theory, built on a suggestion by J. Bell. Further, we give a modal logic interpretation of quantum mechanics and quantum set theory by using Kripke's semantics of modal logic based on the concept of possible worlds. This is grounded on previous work of a number of researchers (Resconi, Klir, Harmanec) who showed how to represent evidence theory and other uncertainty theories in terms of modal logic. Moreover, we also propose a reformulation of the many-worlds interpretation of quantum mechanics in terms of Kripke's semantics. We thus show how three different theories — quantum mechanics, evidence theory, and modal logic — are interrelated. This opens, on one hand, the way to new applications of quantum mechanics within domains different from the traditional ones, and, on the other hand, the possibility of building new generalizations of quantum mechanics itself.

The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions.

PubMed

Jackson, Rebecca L; Hoffman, Paul; Pobric, Gorana; Lambon Ralph, Matthew A

2016-02-03

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex. Copyright © 2016 Jackson et al.

The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions

PubMed Central

Jackson, Rebecca L.; Hoffman, Paul; Pobric, Gorana

2016-01-01

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. SIGNIFICANCE STATEMENT Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex. PMID:26843633

Neural Mechanisms Underlying Learning following Semantic Mediation Treatment in a case of Phonologic Alexia

PubMed Central

Kurland, Jacquie; Cortes, Carlos R; Wilke, Marko; Sperling, Anne J; Lott, Susan N; Tagamets, Malle A; VanMeter, John; Friedman, Rhonda B

2009-01-01

Patients with phonologic alexia can be trained to read semantically impoverished words (e.g., functors) by pairing them with phonologically-related semantically rich words (e.g, nouns). What mechanisms underlie success in this cognitive re-training approach? Does the mechanism change if the skill is “overlearned”, i.e., practiced beyond criterion? We utilized fMRI pre- and post-treatment, and after overlearning, to assess treatment-related functional reorganization in a patient with phonologic alexia, two years post left temporoparietal stroke. Pre-treatment, there were no statistically significant differences in activation profiles across the sets of words. Post-treatment, accuracy on the two trained sets improved. Compared with untrained words, reading trained words recruited larger and more significant clusters of activation in the right hemisphere, including right inferior frontal and inferior parietal cortex. Post-overlearning, with near normal performance on overlearned words, predominant activation shifted to left hemisphere regions, including perilesional activation in superior parietal lobe, when reading overlearned vs. untrained words. PMID:20119495

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

Chemical markup, XML, and the world wide web. 6. CMLReact, an XML vocabulary for chemical reactions.

PubMed

Holliday, Gemma L; Murray-Rust, Peter; Rzepa, Henry S

2006-01-01

A set of components (CMLReact) for managing chemical and biochemical reactions has been added to CML. These can be combined to support most of the strategies for the formal representation of reactions. The elements, attributes, and types are formally defined as XMLSchema components, and their semantics are developed. New syntax and semantics in CML are reported and illustrated with 10 examples.

Supervised guiding long-short term memory for image caption generation based on object classes

NASA Astrophysics Data System (ADS)

Wang, Jian; Cao, Zhiguo; Xiao, Yang; Qi, Xinyuan

2018-03-01

The present models of image caption generation have the problems of image visual semantic information attenuation and errors in guidance information. In order to solve these problems, we propose a supervised guiding Long Short Term Memory model based on object classes, named S-gLSTM for short. It uses the object detection results from R-FCN as supervisory information with high confidence, and updates the guidance word set by judging whether the last output matches the supervisory information. S-gLSTM learns how to extract the current interested information from the image visual se-mantic information based on guidance word set. The interested information is fed into the S-gLSTM at each iteration as guidance information, to guide the caption generation. To acquire the text-related visual semantic information, the S-gLSTM fine-tunes the weights of the network through the back-propagation of the guiding loss. Complementing guidance information at each iteration solves the problem of visual semantic information attenuation in the traditional LSTM model. Besides, the supervised guidance information in our model can reduce the impact of the mismatched words on the caption generation. We test our model on MSCOCO2014 dataset, and obtain better performance than the state-of-the- art models.

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

Preserved strategic grain-size regulation in memory reporting in patients with schizophrenia.

PubMed

Akdogan, Elçin; Izaute, Marie; Bacon, Elisabeth

2014-07-15

Cognitive and introspection disturbances are considered core features of schizophrenia. In real life, people are usually free to choose which aspects of an event they recall, how much detail to volunteer, and what degree of confidence to impart. Their decision will depend on various situational and personal goals. The authors explored whether schizophrenia patients are able to achieve a compromise between accuracy and informativeness when reporting semantic information. Twenty-five patients and 23 healthy matched control subjects answered general knowledge questions requiring numerical answers (how high is the Eiffel tower?), freely at first and then through a metamemory-based control. In the second phase, they answered with respect to two predefined intervals, one narrow and one broad; attributed a confidence judgment to both answers; and afterward selected one of the two answers. Data were analyzed using analyses of variance with group as the between-subjects factor. Patients reported information at a self-paced level of precision less accurately than healthy participants. However, they benefited remarkably from the framing of the response and from the metamemory processes of monitoring and control to the point of improving their memory reporting and matching healthy subjects' accuracy. In spite of their memory deficit during free reporting, after accuracy monitoring, patients strategically regulated the grain size of their memory reporting and proved able to manage the competing goals of accuracy and informativeness. These results give some cause for optimism as to the possibility for patients to adapt to everyday life situations. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.

Sea Ice Characteristics and the Open-Linked Data World

NASA Astrophysics Data System (ADS)

Khalsa, S. J. S.; McGuinness, D. L.; Duerr, R.; Pulsifer, P. L.; Fox, P. A.; Thompson, C.; Yan, R.

2014-12-01

The audience for sea ice data sets has broadened dramatically over the past several decades. Initially the National Snow and Ice Data Center (NSIDC) sea ice products were used primarily by sea ice specialists. However, now they are in demand by researchers in many different domains and some are used by the public. This growth in the number and type of users has presented challenges to content providers aimed particularly at supporting interdisciplinary and multidisciplinary data use. In our experience, it is generally insufficient to simply make the data available as originally formatted. New audiences typically need data in different forms; forms that meet their needs, that work with their specific tools. Moreover, simple data reformatting is rarely enough. The data needs to be aggregated, transformed or otherwise converted into forms that better serve the needs of the new audience. The Semantic Sea Ice Interoperability Initiative (SSIII) is an NSF-funded research project aimed at making sea ice data more useful to more people using semantic technologies. The team includes domain and science data experts as well as knowledge representation and linked data experts. Beginning with a series of workshops involving members of the operations, sea ice research and modeling communities, as well as members of local communities in Alaska, a suite of ontologies describing the physical characteristics of sea ice have been developed and used to provide one of NSIDC's data sets, the operational Arctic sea ice charts obtained from the Canadian Ice Center, as open-linked data. These data extend nearly a decade into the past and can now be queried either directly through a publicly available SPARQL end point (for those who are familiar with open-linked data) or through a simple Open Geospatial Consortium (OGC) standards map-based query tool. Questions like "What were the characteristics (i.e., sea ice concentration, form and stage of development) of the sea ice in the region surrounding my ship/polar bear on date X?" can now be answered. This service may be of interest within the broad polar community - especially those who already are familiar with either open-linked data or OGC services. We seek feedback, collaborators, and users.

Is Semantic Processing During Sentence Reading Autonomous or Controlled? Evidence from the N400 Component in a Dual Task Paradigm

PubMed Central

Hohlfeld, Annette; Martín-Loeches, Manuel; Sommer, Werner

2015-01-01

The present study contributes to the discussion on the automaticity of semantic processing. Whereas most previous research investigated semantic processing at word level, the present study addressed semantic processing during sentence reading. A dual task paradigm was combined with the recording of event-related brain potentials. Previous research at word level processing reported different patterns of interference with the N400 by additional tasks: attenuation of amplitude or delay of latency. In the present study, we presented Spanish sentences that were semantically correct or contained a semantic violation in a critical word. At different intervals preceding the critical word a tone was presented that required a high-priority choice response. At short intervals/high temporal overlap between the tasks mean amplitude of the N400 was reduced relative to long intervals/low temporal overlap, but there were no shifts of peak latency. We propose that processing at sentence level exerts a protective effect against the additional task. This is in accord with the attentional sensitization model (Kiefer & Martens, 2010), which suggests that semantic processing is an automatic process that can be enhanced by the currently activated task set. The present experimental sentences also induced a P600, which is taken as an index of integrative processing. Additional task effects are comparable to those in the N400 time window and are briefly discussed. PMID:26203312

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.

Category-specific semantic deficits: the role of familiarity and property type reexamined.

PubMed

Bunn, E M; Tyler, L K; Moss, H E

1998-07-01

Category-specific deficits for living things have been explained variously as an artifact due to differences in the familiarity of concepts in different categories (E. Funnell & J. Sheridan, 1992) or as the result of an underlying impairment to sensory knowledge (E. K. Warrington & T. Shallice, 1984). Efforts to test these hypotheses empirically have been hindered by the shortcomings of currently available stimulus materials. A new set of stimuli are described that the authors developed to overcome the limitations of existing sets. The set consists of color photographs, matched across categories for familiarity and visual complexity. This set was used to test the semantic knowledge of a classic patient, J.B.R. (E. K. Warrington & T. Shallice, 1984). The results suggest that J.B.R.'s deficit for living things cannot be explained in terms of familiarity effects and that the most severely affected categories are those whose identification is most dependent on sensory information.

Domain and Intelligence Based Multimedia Question Answering System

ERIC Educational Resources Information Center

Kumar, K. Magesh; Valarmathie, P.

2016-01-01

Multimedia question answering systems have become very popular over the past few years. It allows users to share their thoughts by answering a given question or obtain information from a set of answered questions. However, existing QA systems support only textual answer which is not so instructive for many users. The user's discussion can be…

What exactly do numbers mean?

PubMed Central

Huang, Yi Ting; Spelke, Elizabeth; Snedeker, Jesse

2014-01-01

Number words are generally used to refer to the exact cardinal value of a set, but cognitive scientists disagree about their meanings. Although most psychological analyses presuppose that numbers have exact semantics (two means EXACTLY TWO), many linguistic accounts propose that numbers have lower-bounded semantics (AT LEAST TWO), and that speakers restrict their reference through a pragmatic inference (scalar implicature). We address this debate through studies of children who are in the process of acquiring the meanings of numbers. Adults and 2- and 3-year-olds were tested in a novel paradigm that teases apart semantic and pragmatic aspects of interpretation (the covered box task). Our findings establish that when scalar implicatures are cancelled in the critical trials of this task, both adults and children consistently give exact interpretations for number words. These results, in concert with recent work on real-time processing, provide the first unambiguous evidence that number words have exact semantics. PMID:25285053

Framework for Building Collaborative Research Environment

DOE PAGES

Devarakonda, Ranjeet; Palanisamy, Giriprakash; San Gil, Inigo

2014-10-25

Wide range of expertise and technologies are the key to solving some global problems. Semantic web technology can revolutionize the nature of how scientific knowledge is produced and shared. The semantic web is all about enabling machine-machine readability instead of a routine human-human interaction. Carefully structured data, as in machine readable data is the key to enabling these interactions. Drupal is an example of one such toolset that can render all the functionalities of Semantic Web technology right out of the box. Drupal’s content management system automatically stores the data in a structured format enabling it to be machine. Withinmore » this paper, we will discuss how Drupal promotes collaboration in a research setting such as Oak Ridge National Laboratory (ORNL) and Long Term Ecological Research Center (LTER) and how it is effectively using the Semantic Web in achieving this.« less

Extracting and Comparing Places Using Geo-Social Media

NASA Astrophysics Data System (ADS)

Ostermann, F. O.; Huang, H.; Andrienko, G.; Andrienko, N.; Capineri, C.; Farkas, K.; Purves, R. S.

2015-08-01

Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places.

Set of Criteria for Efficiency of the Process Forming the Answers to Multiple-Choice Test Items

ERIC Educational Resources Information Center

Rybanov, Alexander Aleksandrovich

2013-01-01

Is offered the set of criteria for assessing efficiency of the process forming the answers to multiple-choice test items. To increase accuracy of computer-assisted testing results, it is suggested to assess dynamics of the process of forming the final answer using the following factors: loss of time factor and correct choice factor. The model…

Distinct Effects of Lexical and Semantic Competition during Picture Naming in Younger Adults, Older Adults, and People with Aphasia

PubMed Central

Britt, Allison E.; Ferrara, Casey; Mirman, Daniel

2016-01-01

Producing a word requires selecting among a set of similar alternatives. When many semantically related items become activated, the difficulty of the selection process is increased. Experiment 1 tested naming of items with either multiple synonymous labels (“Alternate Names,” e.g., gift/present) or closely semantically related but non-equivalent responses (“Near Semantic Neighbors,” e.g., jam/jelly). Picture naming was fastest and most accurate for pictures with only one label (“High Name Agreement”), slower and less accurate in the Alternate Names condition, and slowest and least accurate in the Near Semantic Neighbors condition. These results suggest that selection mechanisms in picture naming operate at two distinct levels of processing: selecting between similar but non-equivalent names requires two selection processes (semantic and lexical), whereas selecting among equivalent names only requires one selection at the lexical level. Experiment 2 examined how these selection mechanisms are affected by normal aging and found that older adults had significantly more difficulty in the Near Semantic Neighbors condition, but not in the Alternate Names condition. This suggests that aging affects semantic processing and selection more strongly than it affects lexical selection. Experiment 3 examined the role of the left inferior frontal gyrus (LIFG) in these selection processes by testing individuals with aphasia secondary to stroke lesions that either affected the LIFG or spared it. Surprisingly, there was no interaction between condition and lesion group: the presence of LIFG damage was not associated with substantively worse naming performance for pictures with multiple acceptable labels. These results are not consistent with a simple view of LIFG as the locus of lexical selection and suggest a more nuanced view of the neural basis of lexical and semantic selection. PMID:27458393

Intention, emotion, and action: a neural theory based on semantic pointers.

PubMed

Schröder, Tobias; Stewart, Terrence C; Thagard, Paul

2014-06-01

We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is shown by a model that simulates psychologically important cases of intention. © 2013 Cognitive Science Society, Inc.

An improved method for functional similarity analysis of genes based on Gene Ontology.

PubMed

Tian, Zhen; Wang, Chunyu; Guo, Maozu; Liu, Xiaoyan; Teng, Zhixia

2016-12-23

Measures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem. We propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms. The proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/ .

CUILESS2016: a clinical corpus applying compositional normalization of text mentions.

PubMed

Osborne, John D; Neu, Matthew B; Danila, Maria I; Solorio, Thamar; Bethard, Steven J

2018-01-10

Traditionally text mention normalization corpora have normalized concepts to single ontology identifiers ("pre-coordinated concepts"). Less frequently, normalization corpora have used concepts with multiple identifiers ("post-coordinated concepts") but the additional identifiers have been restricted to a defined set of relationships to the core concept. This approach limits the ability of the normalization process to express semantic meaning. We generated a freely available corpus using post-coordinated concepts without a defined set of relationships that we term "compositional concepts" to evaluate their use in clinical text. We annotated 5397 disorder mentions from the ShARe corpus to SNOMED CT that were previously normalized as "CUI-less" in the "SemEval-2015 Task 14" shared task because they lacked a pre-coordinated mapping. Unlike the previous normalization method, we do not restrict concept mappings to a particular set of the Unified Medical Language System (UMLS) semantic types and allow normalization to occur to multiple UMLS Concept Unique Identifiers (CUIs). We computed annotator agreement and assessed semantic coverage with this method. We generated the largest clinical text normalization corpus to date with mappings to multiple identifiers and made it freely available. All but 8 of the 5397 disorder mentions were normalized using this methodology. Annotator agreement ranged from 52.4% using the strictest metric (exact matching) to 78.2% using a hierarchical agreement that measures the overlap of shared ancestral nodes. Our results provide evidence that compositional concepts can increase semantic coverage in clinical text. To our knowledge we provide the first freely available corpus of compositional concept annotation in clinical text.

Semantic Enrichment of Movement Behavior with Foursquare--A Visual Analytics Approach.

PubMed

Krueger, Robert; Thom, Dennis; Ertl, Thomas

2015-08-01

In recent years, many approaches have been developed that efficiently and effectively visualize movement data, e.g., by providing suitable aggregation strategies to reduce visual clutter. Analysts can use them to identify distinct movement patterns, such as trajectories with similar direction, form, length, and speed. However, less effort has been spent on finding the semantics behind movements, i.e. why somebody or something is moving. This can be of great value for different applications, such as product usage and consumer analysis, to better understand urban dynamics, and to improve situational awareness. Unfortunately, semantic information often gets lost when data is recorded. Thus, we suggest to enrich trajectory data with POI information using social media services and show how semantic insights can be gained. Furthermore, we show how to handle semantic uncertainties in time and space, which result from noisy, unprecise, and missing data, by introducing a POI decision model in combination with highly interactive visualizations. Finally, we evaluate our approach with two case studies on a large electric scooter data set and test our model on data with known ground truth.

A suffix arrays based approach to semantic search in P2P systems

NASA Astrophysics Data System (ADS)

Shi, Qingwei; Zhao, Zheng; Bao, Hu

2007-09-01

Building a semantic search system on top of peer-to-peer (P2P) networks is becoming an attractive and promising alternative scheme for the reason of scalability, Data freshness and search cost. In this paper, we present a Suffix Arrays based algorithm for Semantic Search (SASS) in P2P systems, which generates a distributed Semantic Overlay Network (SONs) construction for full-text search in P2P networks. For each node through the P2P network, SASS distributes document indices based on a set of suffix arrays, by which clusters are created depending on words or phrases shared between documents, therefore, the search cost for a given query is decreased by only scanning semantically related documents. In contrast to recently announced SONs scheme designed by using metadata or predefined-class, SASS is an unsupervised approach for decentralized generation of SONs. SASS is also an incremental, linear time algorithm, which efficiently handle the problem of nodes update in P2P networks. Our simulation results demonstrate that SASS yields high search efficiency in dynamic environments.

Informatics in radiology: radiology gamuts ontology: differential diagnosis for the Semantic Web.

PubMed

Budovec, Joseph J; Lam, Cesar A; Kahn, Charles E

2014-01-01

The Semantic Web is an effort to add semantics, or "meaning," to empower automated searching and processing of Web-based information. The overarching goal of the Semantic Web is to enable users to more easily find, share, and combine information. Critical to this vision are knowledge models called ontologies, which define a set of concepts and formalize the relations between them. Ontologies have been developed to manage and exploit the large and rapidly growing volume of information in biomedical domains. In diagnostic radiology, lists of differential diagnoses of imaging observations, called gamuts, provide an important source of knowledge. The Radiology Gamuts Ontology (RGO) is a formal knowledge model of differential diagnoses in radiology that includes 1674 differential diagnoses, 19,017 terms, and 52,976 links between terms. Its knowledge is used to provide an interactive, freely available online reference of radiology gamuts ( www.gamuts.net ). A Web service allows its content to be discovered and consumed by other information systems. The RGO integrates radiologic knowledge with other biomedical ontologies as part of the Semantic Web. © RSNA, 2014.

To ontologise or not to ontologise: An information model for a geospatial knowledge infrastructure

NASA Astrophysics Data System (ADS)

Stock, Kristin; Stojanovic, Tim; Reitsma, Femke; Ou, Yang; Bishr, Mohamed; Ortmann, Jens; Robertson, Anne

2012-08-01

A geospatial knowledge infrastructure consists of a set of interoperable components, including software, information, hardware, procedures and standards, that work together to support advanced discovery and creation of geoscientific resources, including publications, data sets and web services. The focus of the work presented is the development of such an infrastructure for resource discovery. Advanced resource discovery is intended to support scientists in finding resources that meet their needs, and focuses on representing the semantic details of the scientific resources, including the detailed aspects of the science that led to the resource being created. This paper describes an information model for a geospatial knowledge infrastructure that uses ontologies to represent these semantic details, including knowledge about domain concepts, the scientific elements of the resource (analysis methods, theories and scientific processes) and web services. This semantic information can be used to enable more intelligent search over scientific resources, and to support new ways to infer and visualise scientific knowledge. The work describes the requirements for semantic support of a knowledge infrastructure, and analyses the different options for information storage based on the twin goals of semantic richness and syntactic interoperability to allow communication between different infrastructures. Such interoperability is achieved by the use of open standards, and the architecture of the knowledge infrastructure adopts such standards, particularly from the geospatial community. The paper then describes an information model that uses a range of different types of ontologies, explaining those ontologies and their content. The information model was successfully implemented in a working geospatial knowledge infrastructure, but the evaluation identified some issues in creating the ontologies.

ResearchEHR: use of semantic web technologies and archetypes for the description of EHRs.

PubMed

Robles, Montserrat; Fernández-Breis, Jesualdo Tomás; Maldonado, Jose A; Moner, David; Martínez-Costa, Catalina; Bosca, Diego; Menárguez-Tortosa, Marcos

2010-01-01

In this paper, we present the ResearchEHR project. It focuses on the usability of Electronic Health Record (EHR) sources and EHR standards for building advanced clinical systems. The aim is to support healthcare professional, institutions and authorities by providing a set of generic methods and tools for the capture, standardization, integration, description and dissemination of health related information. ResearchEHR combines several tools to manage EHR at two different levels. The internal level that deals with the normalization and semantic upgrading of exiting EHR by using archetypes and the external level that uses Semantic Web technologies to specify clinical archetypes for advanced EHR architectures and systems.

Supervised Semantic Classification for Nuclear Proliferation Monitoring

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

Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

2010-01-01

Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferationmore » monitoring using high resolution remote sensing images.« less

Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects.

DTIC Science & Technology

1985-09-01

Minsky , Marvin and Seymour Papert, [1969], Perceptrons, MIT Press, Cam- bridge, Ma. Mitchell, T. M., [1978], "Version spaces: A candidiate elimination...with respect to a suitable set of affine transformations. This is one area in which classic perceptrons fall short [ Minsky and Papert 19691. The third...Quillian, M. Ross, [1968], "Semantic Memory" (PhD Thesis), in Semantic Infor- mation Processing, M. Minsky (ed.), MIT Press, Cambridge MA. Schlesinger, G

Jumping across biomedical contexts using compressive data fusion

PubMed Central

Zitnik, Marinka; Zupan, Blaz

2016-01-01

Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects—such as a gene and a disease—can be related in different ways, for example, directly via gene–disease associations or indirectly via functional annotations, chemicals and pathways. Different ways of relating these objects carry different semantic meanings. However, traditional methods disregard these semantics and thus cannot fully exploit their value in data modeling. Results: We present Medusa, an approach to detect size-k modules of objects that, taken together, appear most significant to another set of objects. Medusa operates on large-scale collections of heterogeneous datasets and explicitly distinguishes between diverse data semantics. It advances research along two dimensions: it builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program. Medusa is flexible in choosing or combining semantic meanings and provides theoretical guarantees about detection quality. In a systematic study on 310 complex diseases, we show the effectiveness of Medusa in associating genes with diseases and detecting disease modules. We demonstrate that in predicting gene–disease associations Medusa compares favorably to methods that ignore diverse semantic meanings. We find that the utility of different semantics depends on disease categories and that, overall, Medusa recovers disease modules more accurately when combining different semantics. Availability and implementation: Source code is at http://github.com/marinkaz/medusa Contact: marinka@cs.stanford.edu, blaz.zupan@fri.uni-lj.si PMID:27307649

Semantic Web Services Challenge, Results from the First Year. Series: Semantic Web And Beyond, Volume 8.

NASA Astrophysics Data System (ADS)

Petrie, C.; Margaria, T.; Lausen, H.; Zaremba, M.

Explores trade-offs among existing approaches. Reveals strengths and weaknesses of proposed approaches, as well as which aspects of the problem are not yet covered. Introduces software engineering approach to evaluating semantic web services. Service-Oriented Computing is one of the most promising software engineering trends because of the potential to reduce the programming effort for future distributed industrial systems. However, only a small part of this potential rests on the standardization of tools offered by the web services stack. The larger part of this potential rests upon the development of sufficient semantics to automate service orchestration. Currently there are many different approaches to semantic web service descriptions and many frameworks built around them. A common understanding, evaluation scheme, and test bed to compare and classify these frameworks in terms of their capabilities and shortcomings, is necessary to make progress in developing the full potential of Service-Oriented Computing. The Semantic Web Services Challenge is an open source initiative that provides a public evaluation and certification of multiple frameworks on common industrially-relevant problem sets. This edited volume reports on the first results in developing common understanding of the various technologies intended to facilitate the automation of mediation, choreography and discovery for Web Services using semantic annotations. Semantic Web Services Challenge: Results from the First Year is designed for a professional audience composed of practitioners and researchers in industry. Professionals can use this book to evaluate SWS technology for their potential practical use. The book is also suitable for advanced-level students in computer science.

Semantic word category processing in semantic dementia and posterior cortical atrophy.

PubMed

Shebani, Zubaida; Patterson, Karalyn; Nestor, Peter J; Diaz-de-Grenu, Lara Z; Dawson, Kate; Pulvermüller, Friedemann

2017-08-01

There is general agreement that perisylvian language cortex plays a major role in lexical and semantic processing; but the contribution of additional, more widespread, brain areas in the processing of different semantic word categories remains controversial. We investigated word processing in two groups of patients whose neurodegenerative diseases preferentially affect specific parts of the brain, to determine whether their performance would vary as a function of semantic categories proposed to recruit those brain regions. Cohorts with (i) Semantic Dementia (SD), who have anterior temporal-lobe atrophy, and (ii) Posterior Cortical Atrophy (PCA), who have predominantly parieto-occipital atrophy, performed a lexical decision test on words from five different lexico-semantic categories: colour (e.g., yellow), form (oval), number (seven), spatial prepositions (under) and function words (also). Sets of pseudo-word foils matched the target words in length and bi-/tri-gram frequency. Word-frequency was matched between the two visual word categories (colour and form) and across the three other categories (number, prepositions, and function words). Age-matched healthy individuals served as controls. Although broad word processing deficits were apparent in both patient groups, the deficit was strongest for colour words in SD and for spatial prepositions in PCA. The patterns of performance on the lexical decision task demonstrate (a) general lexicosemantic processing deficits in both groups, though more prominent in SD than in PCA, and (b) differential involvement of anterior-temporal and posterior-parietal cortex in the processing of specific semantic categories of words. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Semantic diversity: a measure of semantic ambiguity based on variability in the contextual usage of words.

PubMed

Hoffman, Paul; Lambon Ralph, Matthew A; Rogers, Timothy T

2013-09-01

Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.

The Grounded Expertise Components Approach in the Novel Area of Cryptic Crossword Solving.

PubMed

Friedlander, Kathryn J; Fine, Philip A

2016-01-01

This paper presents a relatively unexplored area of expertise research which focuses on the solving of British-style cryptic crossword puzzles. Unlike its American "straight-definition" counterparts, which are primarily semantically-cued retrieval tasks, the British cryptic crossword is an exercise in code-cracking detection work. Solvers learn to ignore the superficial "surface reading" of the clue, which is phrased to be deliberately misleading, and look instead for a grammatical set of coded instructions which, if executed precisely, will lead to the correct (and only) answer. Sample clues are set out to illustrate the task requirements and demands. Hypothesized aptitudes for the field might include high fluid intelligence, skill at quasi-algebraic puzzles, pattern matching, visuospatial manipulation, divergent thinking and breaking frame abilities. These skills are additional to the crystallized knowledge and word-retrieval demands which are also a feature of American crossword puzzles. The authors present results from an exploratory survey intended to identify the characteristics of the cryptic crossword solving population, and outline the impact of these results on the direction of their subsequent research. Survey results were strongly supportive of a number of hypothesized skill-sets and guided the selection of appropriate test content and research paradigms which formed the basis of an extensive research program to be reported elsewhere. The paper concludes by arguing the case for a more grounded approach to expertise studies, termed the Grounded Expertise Components Approach. In this, the design and scope of the empirical program flows from a detailed and objectively-based characterization of the research population at the very onset of the program.

When is a research question not a research question?

PubMed

Mayo, Nancy E; Asano, Miho; Barbic, Skye Pamela

2013-06-01

Research is undertaken to answer important questions yet often the question is poorly expressed and lacks information on the population, the exposure or intervention, the comparison, and the outcome. An optimal research question sets out what the investigator wants to know, not what the investigator might do, nor what the results of the study might ultimately contribute. The purpose of this paper is to estimate the extent to which rehabilitation scientists optimally define their research questions. A cross-sectional survey of the rehabilitation research articles published during 2008. Two raters independently rated each question according to pre-specified criteria; a third rater adjudicated all discrepant ratings. The proportion of the 258 articles with a question formulated as methods or expected contribution and not as what knowledge was being sought was 65%; 30% of questions required reworking. The designs which most often had poorly formulated research questions were randomized trials, cross-sectional and measurement studies. Formulating the research question is not purely a semantic concern. When the question is poorly formulated, the design, analysis, sample size calculations, and presentation of results may not be optimal. The gap between research and clinical practice could be bridged by a clear, complete, and informative research question.

Populating a Library of Reusable H-Boms Assessment of a Feasible Image Based Modeling Workflow

NASA Astrophysics Data System (ADS)

Santagati, C.; Lo Turco, M.; D'Agostino, G.

2017-08-01

The paper shows the intermediate results of a research activity aimed at populating a library of reusable Historical Building Object Models (H-BOMs) by testing a full digital workflow that takes advantages from using Structure from Motion (SfM) models and is centered on the geometrical/stylistic/materic analysis of the architectural element (portal, window, altar). The aim is to find common (invariant) and uncommon (variant) features in terms of identification of architectural parts and their relationships, geometrical rules, dimensions and proportions, construction materials and measure units, in order to model archetypal shapes from which it is possible to derive all the style variations. At this regard, a set of 14th - 16th century gothic portals of the catalan-aragonese architecture in Etnean area of Eastern Sicily has been studied and used to assess the feasibility of the identified workflow. This approach tries to answer the increasingly demand for guidelines and standards in the field of Cultural Heritage Conservation to create and manage semantic-aware 3D models able to include all the information (both geometrical and alphanumerical ones) concerning historical buildings and able to be reused in several projects.

ERPs, semantic processing and age.

PubMed

Miyamoto, T; Katayama, J; Koyama, T

1998-06-01

ERPs (N400, LPC and CNV) were elicited in two sets of subjects grouped according to age (young vs. elderly) using a word-pair category matching paradigm. Each prime consisted of a Japanese noun (constructed from two to four characters of the Hiragana) followed by one Chinese character (Kanji) as the target, this latter representing one of five semantic categories. There were two equally probable target conditions: match or mismatch. Each target was preceded by a prime, either belonging to, or not belonging to, the same semantic category. The subjects were required to respond with a specified button press to the given target according to the condition. We found RTs to be longer in the elderly subjects and under the mismatch condition. N400 amplitude was reduced in the elderly subjects under the mismatch condition and there was no difference between match and mismatch response, which were similar in amplitude to that under match condition for the young subjects. In addition, the CNV amplitudes were larger in the elderly subjects. These results suggested that functional changes in semantic processing through aging (larger semantic networks and diffuse semantic activation) were the cause of this N400 reduction, attributing a subsidiary role to attentional disturbance. We also discuss the importance of taking age-related changes into consideration in clinical studies.

Unambiguous UML Composite Structures: The OMEGA2 Experience

NASA Astrophysics Data System (ADS)

Ober, Iulian; Dragomir, Iulia

Starting from version 2.0, UML introduced hierarchical composite structures, which are a very expressive way of defining complex software architectures, but which have a very loosely defined semantics in the standard. In this paper we propose a set of consistency rules that ensure UML composite structures are unambiguous and can be given a precise semantics. Our primary application of the static consistency rules defined in this paper is within the OMEGA UML profile [6], but these rules are general and applicable to other hierarchical component models based on the same concepts, such as MARTE GCM or SysML. The rule set has been formalized in OCL and is currently used in the OMEGA UML compiler.

Psychosocial barriers associated with organ donation in Mexico.

PubMed

Marván, Maria Luisa; Álvarez Del Río, Asunción; Jasso, Kristian; Santillán-Doherty, Patricio

2017-11-01

There is a severe shortage of organs for transplantation worldwide, and Mexico has one of the lowest organ donation rates. In this study, we explored the psychosocial barriers that prevent posthumous organ donation by Mexicans. We asked 218 adults who were not willing to be donors to complete the sentence "I don't want to donate my organs after death because organ donation is…" The data were analyzed using the Natural Semantic Networks Technique. The most important answers given by the participants were related to mistrust. Older participants and those with limited education gave more answers that reflect misconceptions about organ donation. Many participants acknowledged its benefits, even though they did not want to be donors, especially the youngest and those with a higher education. Mistrust and poor education are problems that urgently need to be addressed in order to increase acceptance of organ donation and transplantation. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

Text categorization of biomedical data sets using graph kernels and a controlled vocabulary.

PubMed

Bleik, Said; Mishra, Meenakshi; Huan, Jun; Song, Min

2013-01-01

Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.

Muscle Logic: New Knowledge Resource for Anatomy Enables Comprehensive Searches of the Literature on the Feeding Muscles of Mammals

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

Druzinsky, Robert E.; Balhoff, James P.; Crompton, Alfred W.

Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp.org/), a publicly-available, online repositorymore » for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). Lastly, we compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities.« less

Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet.

PubMed

Guo, Haihong; Na, Xu; Hou, Li; Li, Jiao

2017-06-20

In question answering (QA) system development, question classification is crucial for identifying information needs and improving the accuracy of returned answers. Although the questions are domain-specific, they are asked by non-professionals, making the question classification task more challenging. This study aimed to classify health care-related questions posted by the general public (Chinese speakers) on the Internet. A topic-based classification schema for health-related questions was built by manually annotating randomly selected questions. The Kappa statistic was used to measure the interrater reliability of multiple annotation results. Using the above corpus, we developed a machine-learning method to automatically classify these questions into one of the following six classes: Condition Management, Healthy Lifestyle, Diagnosis, Health Provider Choice, Treatment, and Epidemiology. The consumer health question schema was developed with a four-hierarchical-level of specificity, comprising 48 quaternary categories and 35 annotation rules. The 2000 sample questions were coded with 2000 major codes and 607 minor codes. Using natural language processing techniques, we expressed the Chinese questions as a set of lexical, grammatical, and semantic features. Furthermore, the effective features were selected to improve the question classification performance. From the 6-category classification results, we achieved an average precision of 91.41%, recall of 89.62%, and F 1 score of 90.24%. In this study, we developed an automatic method to classify questions related to Chinese health care posted by the general public. It enables Artificial Intelligence (AI) agents to understand Internet users' information needs on health care. ©Haihong Guo, Xu Na, Li Hou, Jiao Li. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.06.2017.

Muscle Logic: New Knowledge Resource for Anatomy Enables Comprehensive Searches of the Literature on the Feeding Muscles of Mammals

DOE PAGES

Druzinsky, Robert E.; Balhoff, James P.; Crompton, Alfred W.; ...

2016-02-12

Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp.org/), a publicly-available, online repositorymore » for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). Lastly, we compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities.« less

Semantic relatedness for evaluation of course equivalencies

NASA Astrophysics Data System (ADS)

Yang, Beibei

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 Brown corpus, or more recently, Wikipedia. This dissertation proposes two approaches to applying semantic relatedness to the problem of suggesting transfer course equivalencies. Two course descriptions are given as input to feed the proposed algorithms, which output a value that can be used to help determine if the courses are equivalent. The first proposed approach uses traditional knowledge sources such as WordNet and corpora for courses from multiple fields of study. The second approach uses Wikipedia, the openly-editable encyclopedia, and it focuses on courses from a technical field such as Computer Science. This work shows that it is promising to adapt semantic relatedness to the education field for matching equivalencies between transfer courses. A semantic relatedness measure using traditional knowledge sources such as WordNet performs relatively well on non-technical courses. However, due to the "knowledge acquisition bottleneck," such a resource is not ideal for technical courses, which use an extensive and growing set of technical terms. To address the problem, this work proposes a Wikipedia-based approach which is later shown to be more correlated to human judgment compared to previous work.

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

PubMed Central

Waagmeester, Andra; Pico, Alexander R.

2016-01-01

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web. PMID:27336457

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources.

PubMed

Waagmeester, Andra; Kutmon, Martina; Riutta, Anders; Miller, Ryan; Willighagen, Egon L; Evelo, Chris T; Pico, Alexander R

2016-06-01

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

Towards a Framework for Developing Semantic Relatedness Reference Standards

PubMed Central

Pakhomov, Serguei V.S.; Pedersen, Ted; McInnes, Bridget; Melton, Genevieve B.; Ruggieri, Alexander; Chute, Christopher G.

2010-01-01

Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are not publicly available and the published information provided in manuscripts that evaluate computerized semantic relatedness measurement approaches is not sufficient to reproduce the results. Our proposed framework is based on the experiences of medical informatics and computational linguistics communities and addresses practical and theoretical issues with creating reference standards for semantic relatedness. We demonstrate the use of the framework on a pilot set of 101 medical term pairs rated for semantic relatedness by 13 medical coding experts. While the reliability of this particular reference standard is in the “moderate” range; we show that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers. We test two ontology-based measures of relatedness and provide both the reference standard containing individual ratings and the R program used to analyze the ratings as open-source. Currently, these resources are intended to be used to reproduce and compare results of studies involving computerized measures of semantic relatedness. Our framework may be extended to the development of reference standards in other research areas in medical informatics including automatic classification, information retrieval from medical records and vocabulary/ontology development. PMID:21044697

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

PubMed Central

Valavanis, Ioannis; Pilalis, Eleftherios; Georgiadis, Panagiotis; Kyrtopoulos, Soterios; Chatziioannou, Aristotelis

2015-01-01

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies. PMID:27600245

When generating answers benefits arithmetic skill: the importance of prior knowledge.

PubMed

Rittle-Johnson, Bethany; Kmicikewycz, Alexander Oleksij

2008-09-01

People remember information better if they generate the information while studying rather than read the information. However, prior research has not investigated whether this generation effect extends to related but unstudied items and has not been conducted in classroom settings. We compared third graders' success on studied and unstudied multiplication problems after they spent a class period generating answers to problems or reading the answers from a calculator. The effect of condition interacted with prior knowledge. Students with low prior knowledge had higher accuracy in the generate condition, but as prior knowledge increased, the advantage of generating answers decreased. The benefits of generating answers may extend to unstudied items and to classroom settings, but only for learners with low prior knowledge.

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Spatial Relation Predicates in Topographic Feature Semantics

USGS Publications Warehouse

Varanka, Dalia E.; Caro, Holly K.

2013-01-01

Topographic data are designed and widely used for base maps of diverse applications, yet the power of these information sources largely relies on the interpretive skills of map readers and relational database expert users once the data are in map or geographic information system (GIS) form. Advances in geospatial semantic technology offer data model alternatives for explicating concepts and articulating complex data queries and statements. To understand and enrich the vocabulary of topographic feature properties for semantic technology, English language spatial relation predicates were analyzed in three standard topographic feature glossaries. The analytical approach drew from disciplinary concepts in geography, linguistics, and information science. Five major classes of spatial relation predicates were identified from the analysis; representations for most of these are not widely available. The classes are: part-whole (which are commonly modeled throughout semantic and linked-data networks), geometric, processes, human intention, and spatial prepositions. These are commonly found in the ‘real world’ and support the environmental science basis for digital topographical mapping. The spatial relation concepts are based on sets of relation terms presented in this chapter, though these lists are not prescriptive or exhaustive. The results of this study make explicit the concepts forming a broad set of spatial relation expressions, which in turn form the basis for expanding the range of possible queries for topographical data analysis and mapping.

Towards a semantic web of paleoclimatology

NASA Astrophysics Data System (ADS)

Emile-Geay, J.; Eshleman, J. A.

2012-12-01

The paleoclimate record is information-rich, yet signifiant technical barriers currently exist before it can be used to automatically answer scientific questions. Here we make the case for a universal format to structure paleoclimate data. A simple example demonstrates the scientific utility of such a self-contained way of organizing coral data and meta-data in the Matlab language. This example is generalized to a universal ontology that may form the backbone of an open-source, open-access and crowd-sourced paleoclimate database. Its key attributes are: 1. Parsability: the format is self-contained (hence machine-readable), and would therefore enable a semantic web of paleoclimate information. 2. Universality: the format is platform-independent (readable on all computer and operating systems), and language- independent (readable in major programming languages) 3. Extensibility: the format requires a minimum set of fields to appropriately define a paleoclimate record, but allows for the database to grow organically as more records are added, or - equally important - as more metadata are added to existing records. 4. Citability: The format enables the automatic citation of peer- reviewed articles as well as data citations whenever a data record is being used for analysis, making due recognition of scientific work an automatic part and foundational principle of paleoclimate data analysis. 5. Ergonomy: The format will be easy to use, update and manage. This structure is designed to enable semantic searches, and is expected to help accelerate discovery in all workflows where paleoclimate data are being used. Practical steps towards the implementation of such a system at the community level are then discussed.; Preliminary ontology describing relationships between the data and meta-data fields of the Nurhati et al. [2011] climate record. Several fields are viewed as instances of larger classes (ProxyClass,Site,Reference), which would allow computers to perform operations on all records within a specific class (e.g. if the measurement type is δ18O , or if the proxy class is 'Tree Ring Width', or if the resolution is less than 3 months, etc). All records in such a database would be bound to each other by similar links, allowing machines to automatically process any form of query involving existing information. Such a design would also allow growth, by adding records and/or additional information about each record.

Developing a kidney and urinary pathway knowledge base

PubMed Central

2011-01-01

Background Chronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration. Results We present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney. Conclusions The KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself. Availability The KUPKB may be accessed via http://www.e-lico.eu/kupkb. PMID:21624162

Visual noise disrupts conceptual integration in reading.

PubMed

Gao, Xuefei; Stine-Morrow, Elizabeth A L; Noh, Soo Rim; Eskew, Rhea T

2011-02-01

The Effortfulness Hypothesis suggests that sensory impairment (either simulated or age-related) may decrease capacity for semantic integration in language comprehension. We directly tested this hypothesis by measuring resource allocation to different levels of processing during reading (i.e., word vs. semantic analysis). College students read three sets of passages word-by-word, one at each of three levels of dynamic visual noise. There was a reliable interaction between processing level and noise, such that visual noise increased resources allocated to word-level processing, at the cost of attention paid to semantic analysis. Recall of the most important ideas also decreased with increasing visual noise. Results suggest that sensory challenge can impair higher-level cognitive functions in learning from text, supporting the Effortfulness Hypothesis.

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.

Visual Pattern Analysis in Histopathology Images Using Bag of Features

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Caicedo, Juan C.; González, Fabio A.

This paper presents a framework to analyse visual patterns in a collection of medical images in a two stage procedure. First, a set of representative visual patterns from the image collection is obtained by constructing a visual-word dictionary under a bag-of-features approach. Second, an analysis of the relationships between visual patterns and semantic concepts in the image collection is performed. The most important visual patterns for each semantic concept are identified using correlation analysis. A matrix visualization of the structure and organization of the image collection is generated using a cluster analysis. The experimental evaluation was conducted on a histopathology image collection and results showed clear relationships between visual patterns and semantic concepts, that in addition, are of easy interpretation and understanding.

Combined semantic and similarity search in medical image databases

NASA Astrophysics Data System (ADS)

Seifert, Sascha; Thoma, Marisa; Stegmaier, Florian; Hammon, Matthias; Kramer, Martin; Huber, Martin; Kriegel, Hans-Peter; Cavallaro, Alexander; Comaniciu, Dorin

2011-03-01

The current diagnostic process at hospitals is mainly based on reviewing and comparing images coming from multiple time points and modalities in order to monitor disease progression over a period of time. However, for ambiguous cases the radiologist deeply relies on reference literature or second opinion. Although there is a vast amount of acquired images stored in PACS systems which could be reused for decision support, these data sets suffer from weak search capabilities. Thus, we present a search methodology which enables the physician to fulfill intelligent search scenarios on medical image databases combining ontology-based semantic and appearance-based similarity search. It enabled the elimination of 12% of the top ten hits which would arise without taking the semantic context into account.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

PubMed

Jung, Hyesil; Park, Hyeoun-Ae; Song, Tae-Min

2017-07-24

Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, "academic stresses" and "suicide" contributed negatively to the sentiment of adolescent depression. The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. ©Hyesil Jung, Hyeoun-Ae Park, Tae-Min Song. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.07.2017.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

PubMed Central

Jung, Hyesil; Song, Tae-Min

2017-01-01

Background Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. Objective The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. Methods The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. Results We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, “academic stresses” and “suicide” contributed negatively to the sentiment of adolescent depression. Conclusions The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. PMID:28739560

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

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

Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

NASA Astrophysics Data System (ADS)

Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

Analysis of Student Responses to Peer-Instruction Conceptual Questions Answered Using an Electronic Response System: Trends by Gender and Ethnicity

ERIC Educational Resources Information Center

Steer, David; McConnell, David; Gray, Kyle; Kortz, Karen; Liang, Xin

2009-01-01

This descriptive study investigated students' answers to geoscience conceptual questions answered using electronic personal response systems. Answer patterns were examined to evaluate the peer-instruction pedagogical approach in a large general education classroom setting. (Contains 3 figures and 2 tables.)

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

NASA Astrophysics Data System (ADS)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Levels of processing and picture memory: the physical superiority effect.

PubMed

Intraub, H; Nicklos, S

1985-04-01

Six experiments studied the effect of physical orienting questions (e.g., "Is this angular?") and semantic orienting questions (e.g., "Is this edible?") on memory for unrelated pictures at stimulus durations ranging from 125-2,000 ms. Results ran contrary to the semantic superiority "rule of thumb," which is based primarily on verbal memory experiments. Physical questions were associated with better free recall and cued recall of a diverse set of visual scenes (Experiments 1, 2, and 4). This occurred both when general and highly specific semantic questions were used (Experiments 1 and 2). Similar results were obtained when more simplistic visual stimuli--photographs of single objects--were used (Experiments 5 and 6). As in the case of the semantic superiority effect with words, the physical superiority effect for pictures was eliminated or reversed when the same physical questions were repeated throughout the session (Experiments 4 and 6). Conflicts with results of previous levels of processing experiments with words and nonverbal stimuli (e.g., faces) are explained in terms of the sensory-semantic model (Nelson, Reed, & McEvoy, 1977). Implications for picture memory research and the levels of processing viewpoint are discussed.

A Supramodal Neural Network for Speech and Gesture Semantics: An fMRI Study

PubMed Central

Weis, Susanne; Kircher, Tilo

2012-01-01

In a natural setting, speech is often accompanied by gestures. As language, speech-accompanying iconic gestures to some extent convey semantic information. However, if comprehension of the information contained in both the auditory and visual modality depends on same or different brain-networks is quite unknown. In this fMRI study, we aimed at identifying the cortical areas engaged in supramodal processing of semantic information. BOLD changes were recorded in 18 healthy right-handed male subjects watching video clips showing an actor who either performed speech (S, acoustic) or gestures (G, visual) in more (+) or less (−) meaningful varieties. In the experimental conditions familiar speech or isolated iconic gestures were presented; during the visual control condition the volunteers watched meaningless gestures (G−), while during the acoustic control condition a foreign language was presented (S−). The conjunction of the visual and acoustic semantic processing revealed activations extending from the left inferior frontal gyrus to the precentral gyrus, and included bilateral posterior temporal regions. We conclude that proclaiming this frontotemporal network the brain's core language system is to take too narrow a view. Our results rather indicate that these regions constitute a supramodal semantic processing network. PMID:23226488

Applying Semantic-based Probabilistic Context-Free Grammar to Medical Language Processing – A Preliminary Study on Parsing Medication Sentences

PubMed Central

Xu, Hua; AbdelRahman, Samir; Lu, Yanxin; Denny, Joshua C.; Doan, Son

2011-01-01

Semantic-based sublanguage grammars have been shown to be an efficient method for medical language processing. However, given the complexity of the medical domain, parsers using such grammars inevitably encounter ambiguous sentences, which could be interpreted by different groups of production rules and consequently result in two or more parse trees. One possible solution, which has not been extensively explored previously, is to augment productions in medical sublanguage grammars with probabilities to resolve the ambiguity. In this study, we associated probabilities with production rules in a semantic-based grammar for medication findings and evaluated its performance on reducing parsing ambiguity. Using the existing data set from 2009 i2b2 NLP (Natural Language Processing) challenge for medication extraction, we developed a semantic-based CFG (Context Free Grammar) for parsing medication sentences and manually created a Treebank of 4,564 medication sentences from discharge summaries. Using the Treebank, we derived a semantic-based PCFG (probabilistic Context Free Grammar) for parsing medication sentences. Our evaluation using a 10-fold cross validation showed that the PCFG parser dramatically improved parsing performance when compared to the CFG parser. PMID:21856440

The role of lexical variables in the visual recognition of Chinese characters: A megastudy analysis.

PubMed

Sze, Wei Ping; Yap, Melvin J; Rickard Liow, Susan J

2015-01-01

Logographic Chinese orthography partially represents both phonology and semantics. By capturing the online processing of a large pool of Chinese characters, we were able to examine the relative salience of specific lexical variables when this nonalphabetic script is read. Using a sample of native mainland Chinese speakers (N = 35), lexical decision latencies for 1560 single characters were collated into a database, before the effects of a comprehensive range of variables were explored. Hierarchical regression analyses determined the unique item-level variance explained by orthographic (frequency, stroke count), semantic (age of learning, imageability, number of meanings), and phonological (consistency, phonological frequency) factors. Orthographic and semantic variables, respectively, accounted for more collective variance than the phonological variables. Significant main effects were further observed for the individual orthographic and semantic predictors. These results are consistent with the idea that skilled readers tend to rely on orthographic and semantic information when processing visually presented characters. This megastudy approach marks an important extension to existing work on Chinese character recognition, which hitherto has relied on factorial designs. Collectively, the findings reported here represent a useful set of empirical constraints for future computational models of character recognition.

Inhibitory control gains from higher-order cognitive strategy training.

PubMed

Motes, Michael A; Gamino, Jacquelyn F; Chapman, Sandra B; Rao, Neena K; Maguire, Mandy J; Brier, Matthew R; Kraut, Michael A; Hart, John

2014-02-01

The present study examined the transfer of higher-order cognitive strategy training to inhibitory control. Middle school students enrolled in a comprehension- and reasoning-focused cognitive strategy training program and passive controls participated. The training program taught students a set of steps for inferring essential gist or themes from materials. Both before and after training or a comparable duration in the case of the passive controls, participants completed a semantically cued Go/No-Go task that was designed to assess the effects of depth of semantic processing on response inhibition and components of event-related potentials (ERP) related to response inhibition. Depth of semantic processing was manipulated by varying the level of semantic categorization required for response selection and inhibition. The SMART-trained group showed inhibitory control gains and changes in fronto-central P3 ERP amplitudes on inhibition trials; whereas, the control group did not. The results provide evidence of the transfer of higher-order cognitive strategy training to inhibitory control and modulation of ERPs associated with semantically cued inhibitory control. The findings are discussed in terms of implications for cognitive strategy training, models of cognitive abilities, and education. Published by Elsevier Inc.

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features

PubMed Central

Zhang, Yaoyun; Jiang, Min; Wang, Jingqi; Xu, Hua

2016-01-01

Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F1 measure of 71.41% achieved by the Berkeley parser. PMID:28269926

The N400 as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects

PubMed Central

Laszlo, Sarah; Federmeier, Kara D.

2010-01-01

Linking print with meaning tends to be divided into subprocesses, such as recognition of an input's lexical entry and subsequent access of semantics. However, recent results suggest that the set of semantic features activated by an input is broader than implied by a view wherein access serially follows recognition. EEG was collected from participants who viewed items varying in number and frequency of both orthographic neighbors and lexical associates. Regression analysis of single item ERPs replicated past findings, showing that N400 amplitudes are greater for items with more neighbors, and further revealed that N400 amplitudes increase for items with more lexical associates and with higher frequency neighbors or associates. Together, the data suggest that in the N400 time window semantic features of items broadly related to inputs are active, consistent with models in which semantic access takes place in parallel with stimulus recognition. PMID:20624252

Reply & Supply: Efficient crowdsourcing when workers do more than answer questions

PubMed Central

McAndrew, Thomas C.; Guseva, Elizaveta A.

2017-01-01

Crowdsourcing works by distributing many small tasks to large numbers of workers, yet the true potential of crowdsourcing lies in workers doing more than performing simple tasks—they can apply their experience and creativity to provide new and unexpected information to the crowdsourcer. One such case is when workers not only answer a crowdsourcer’s questions but also contribute new questions for subsequent crowd analysis, leading to a growing set of questions. This growth creates an inherent bias for early questions since a question introduced earlier by a worker can be answered by more subsequent workers than a question introduced later. Here we study how to perform efficient crowdsourcing with such growing question sets. By modeling question sets as networks of interrelated questions, we introduce algorithms to help curtail the growth bias by efficiently distributing workers between exploring new questions and addressing current questions. Experiments and simulations demonstrate that these algorithms can efficiently explore an unbounded set of questions without losing confidence in crowd answers. PMID:28806413

DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.

PubMed

Peng, Shengwen; You, Ronghui; Wang, Hongning; Zhai, Chengxiang; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the 'learning to rank' framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. The software is available upon request. zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

SADI, SHARE, and the in silico scientific method

PubMed Central

2010-01-01

Background The emergence and uptake of Semantic Web technologies by the Life Sciences provides exciting opportunities for exploring novel ways to conduct in silico science. Web Service Workflows are already becoming first-class objects in “the new way”, and serve as explicit, shareable, referenceable representations of how an experiment was done. In turn, Semantic Web Service projects aim to facilitate workflow construction by biological domain-experts such that workflows can be edited, re-purposed, and re-published by non-informaticians. However the aspects of the scientific method relating to explicit discourse, disagreement, and hypothesis generation have remained relatively impervious to new technologies. Results Here we present SADI and SHARE - a novel Semantic Web Service framework, and a reference implementation of its client libraries. Together, SADI and SHARE allow the semi- or fully-automatic discovery and pipelining of Semantic Web Services in response to ad hoc user queries. Conclusions The semantic behaviours exhibited by SADI and SHARE extend the functionalities provided by Description Logic Reasoners such that novel assertions can be automatically added to a data-set without logical reasoning, but rather by analytical or annotative services. This behaviour might be applied to achieve the “semantification” of those aspects of the in silico scientific method that are not yet supported by Semantic Web technologies. We support this suggestion using an example in the clinical research space. PMID:21210986

Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies

PubMed Central

Köhler, Sebastian; Schulz, Marcel H.; Krawitz, Peter; Bauer, Sebastian; Dölken, Sandra; Ott, Claus E.; Mundlos, Christine; Horn, Denise; Mundlos, Stefan; Robinson, Peter N.

2009-01-01

The differential diagnostic process attempts to identify candidate diseases that best explain a set of clinical features. This process can be complicated by the fact that the features can have varying degrees of specificity, as well as by the presence of features unrelated to the disease itself. Depending on the experience of the physician and the availability of laboratory tests, clinical abnormalities may be described in greater or lesser detail. We have adapted semantic similarity metrics to measure phenotypic similarity between queries and hereditary diseases annotated with the use of the Human Phenotype Ontology (HPO) and have developed a statistical model to assign p values to the resulting similarity scores, which can be used to rank the candidate diseases. We show that our approach outperforms simpler term-matching approaches that do not take the semantic interrelationships between terms into account. The advantage of our approach was greater for queries containing phenotypic noise or imprecise clinical descriptions. The semantic network defined by the HPO can be used to refine the differential diagnosis by suggesting clinical features that, if present, best differentiate among the candidate diagnoses. Thus, semantic similarity searches in ontologies represent a useful way of harnessing the semantic structure of human phenotypic abnormalities to help with the differential diagnosis. We have implemented our methods in a freely available web application for the field of human Mendelian disorders. PMID:19800049

Evidence for the activation of sensorimotor information during visual word recognition: the body-object interaction effect.

PubMed

Siakaluk, Paul D; Pexman, Penny M; Aguilera, Laura; Owen, William J; Sears, Christopher R

2008-01-01

We examined the effects of sensorimotor experience in two visual word recognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., mask) and a set of low BOI words (e.g., ship) were created, matched on imageability and concreteness. Facilitatory BOI effects were observed in lexical decision and phonological lexical decision tasks: responses were faster for high BOI words than for low BOI words. We discuss how our findings may be accounted for by (a) semantic feedback within the visual word recognition system, and (b) an embodied view of cognition (e.g., Barsalou's perceptual symbol systems theory), which proposes that semantic knowledge is grounded in sensorimotor interactions with the environment.

An fMRI study of semantic processing in men with schizophrenia

PubMed Central

Kubicki, M.; McCarley, R.W.; Nestor, P.G.; Huh, T.; Kikinis, R.; Shenton, M.E.; Wible, C.G.

2009-01-01

As a means toward understanding the neural bases of schizophrenic thought disturbance, we examined brain activation patterns in response to semantically and superficially encoded words in patients with schizophrenia. Nine male schizophrenic and 9 male control subjects were tested in a visual levels of processing (LOP) task first outside the magnet and then during the fMRI scanning procedures (using a different set of words). During the experiments visual words were presented under two conditions. Under the deep, semantic encoding condition, subjects made semantic judgments as to whether the words were abstract or concrete. Under the shallow, nonsemantic encoding condition, subjects made perceptual judgments of the font size (uppercase/lowercase) of the presented words. After performance of the behavioral task, a recognition test was used to assess the depth of processing effect, defined as better performance for semantically encoded words than for perceptually encoded words. For the scanned version only, the words for both conditions were repeated in order to assess repetition-priming effects. Reaction times were assessed in both testing scenarios. Both groups showed the expected depth of processing effect for recognition, and control subjects showed the expected increased activation of the left inferior prefrontal cortex (LIPC) under semantic encoding relative to perceptual encoding conditions as well as repetition priming for semantic conditions only. In contrast, schizophrenics showed similar patterns of fMRI activation regardless of condition. Most striking in relation to controls, patients showed decreased LIFC activation concurrent with increased left superior temporal gyrus activation for semantic encoding versus shallow encoding. Furthermore, schizophrenia subjects did not show the repetition priming effect, either behaviorally or as a decrease in LIPC activity. In patients with schizophrenia, LIFC underactivation and left superior temporal gyrus overactivation for semantically encoded words may reflect a disease-related disruption of a distributed frontal temporal network that is engaged in the representation and processing of meaning of words, text, and discourse and which may underlie schizophrenic thought disturbance. PMID:14683698

An fMRI study of semantic processing in men with schizophrenia.

PubMed

Kubicki, M; McCarley, R W; Nestor, P G; Huh, T; Kikinis, R; Shenton, M E; Wible, C G

2003-12-01

As a means toward understanding the neural bases of schizophrenic thought disturbance, we examined brain activation patterns in response to semantically and superficially encoded words in patients with schizophrenia. Nine male schizophrenic and 9 male control subjects were tested in a visual levels of processing (LOP) task first outside the magnet and then during the fMRI scanning procedures (using a different set of words). During the experiments visual words were presented under two conditions. Under the deep, semantic encoding condition, subjects made semantic judgments as to whether the words were abstract or concrete. Under the shallow, nonsemantic encoding condition, subjects made perceptual judgments of the font size (uppercase/lowercase) of the presented words. After performance of the behavioral task, a recognition test was used to assess the depth of processing effect, defined as better performance for semantically encoded words than for perceptually encoded words. For the scanned version only, the words for both conditions were repeated in order to assess repetition-priming effects. Reaction times were assessed in both testing scenarios. Both groups showed the expected depth of processing effect for recognition, and control subjects showed the expected increased activation of the left inferior prefrontal cortex (LIPC) under semantic encoding relative to perceptual encoding conditions as well as repetition priming for semantic conditions only. In contrast, schizophrenics showed similar patterns of fMRI activation regardless of condition. Most striking in relation to controls, patients showed decreased LIFC activation concurrent with increased left superior temporal gyrus activation for semantic encoding versus shallow encoding. Furthermore, schizophrenia subjects did not show the repetition priming effect, either behaviorally or as a decrease in LIPC activity. In patients with schizophrenia, LIFC underactivation and left superior temporal gyrus overactivation for semantically encoded words may reflect a disease-related disruption of a distributed frontal temporal network that is engaged in the representation and processing of meaning of words, text, and discourse and which may underlie schizophrenic thought disturbance.

Semantic Integration for Marine Science Interoperability Using Web Technologies

NASA Astrophysics Data System (ADS)

Rueda, C.; Bermudez, L.; Graybeal, J.; Isenor, A. W.

2008-12-01

The Marine Metadata Interoperability Project, MMI (http://marinemetadata.org) promotes the exchange, integration, and use of marine data through enhanced data publishing, discovery, documentation, and accessibility. A key effort is the definition of an Architectural Framework and Operational Concept for Semantic Interoperability (http://marinemetadata.org/sfc), which is complemented with the development of tools that realize critical use cases in semantic interoperability. In this presentation, we describe a set of such Semantic Web tools that allow performing important interoperability tasks, ranging from the creation of controlled vocabularies and the mapping of terms across multiple ontologies, to the online registration, storage, and search services needed to work with the ontologies (http://mmisw.org). This set of services uses Web standards and technologies, including Resource Description Framework (RDF), Web Ontology language (OWL), Web services, and toolkits for Rich Internet Application development. We will describe the following components: MMI Ontology Registry: The MMI Ontology Registry and Repository provides registry and storage services for ontologies. Entries in the registry are associated with projects defined by the registered users. Also, sophisticated search functions, for example according to metadata items and vocabulary terms, are provided. Client applications can submit search requests using the WC3 SPARQL Query Language for RDF. Voc2RDF: This component converts an ASCII comma-delimited set of terms and definitions into an RDF file. Voc2RDF facilitates the creation of controlled vocabularies by using a simple form-based user interface. Created vocabularies and their descriptive metadata can be submitted to the MMI Ontology Registry for versioning and community access. VINE: The Vocabulary Integration Environment component allows the user to map vocabulary terms across multiple ontologies. Various relationships can be established, for example exactMatch, narrowerThan, and subClassOf. VINE can compute inferred mappings based on the given associations. Attributes about each mapping, like comments and a confidence level, can also be included. VINE also supports registering and storing resulting mapping files in the Ontology Registry. The presentation will describe the application of semantic technologies in general, and our planned applications in particular, to solve data management problems in the marine and environmental sciences.

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.

Towards a framework for developing semantic relatedness reference standards.

PubMed

Pakhomov, Serguei V S; Pedersen, Ted; McInnes, Bridget; Melton, Genevieve B; Ruggieri, Alexander; Chute, Christopher G

2011-04-01

Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are not publicly available and the published information provided in manuscripts that evaluate computerized semantic relatedness measurement approaches is not sufficient to reproduce the results. Our proposed framework is based on the experiences of medical informatics and computational linguistics communities and addresses practical and theoretical issues with creating reference standards for semantic relatedness. We demonstrate the use of the framework on a pilot set of 101 medical term pairs rated for semantic relatedness by 13 medical coding experts. While the reliability of this particular reference standard is in the "moderate" range; we show that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers. We test two ontology-based measures of relatedness and provide both the reference standard containing individual ratings and the R program used to analyze the ratings as open-source. Currently, these resources are intended to be used to reproduce and compare results of studies involving computerized measures of semantic relatedness. Our framework may be extended to the development of reference standards in other research areas in medical informatics including automatic classification, information retrieval from medical records and vocabulary/ontology development. Copyright © 2010 Elsevier Inc. All rights reserved.

Fuzzy Versions of Epistemic and Deontic Logic

NASA Technical Reports Server (NTRS)

Gounder, Ramasamy S.; Esterline, Albert C.

1998-01-01

Epistemic and deontic logics are modal logics, respectively, of knowledge and of the normative concepts of obligation, permission, and prohibition. Epistemic logic is useful in formalizing systems of communicating processes and knowledge and belief in AI (Artificial Intelligence). Deontic logic is useful in computer science wherever we must distinguish between actual and ideal behavior, as in fault tolerance and database integrity constraints. We here discuss fuzzy versions of these logics. In the crisp versions, various axioms correspond to various properties of the structures used in defining the semantics of the logics. Thus, any axiomatic theory will be characterized not only by its axioms but also by the set of properties holding of the corresponding semantic structures. Fuzzy logic does not proceed with axiomatic systems, but fuzzy versions of the semantic properties exist and can be shown to correspond to some of the axioms for the crisp systems in special ways that support dependency networks among assertions in a modal domain. This in turn allows one to implement truth maintenance systems. For the technical development of epistemic logic, and for that of deontic logic. To our knowledge, we are the first to address fuzzy epistemic and fuzzy deontic logic explicitly and to consider the different systems and semantic properties available. We give the syntax and semantics of epistemic logic and discuss the correspondence between axioms of epistemic logic and properties of semantic structures. The same topics are covered for deontic logic. Fuzzy epistemic and fuzzy deontic logic discusses the relationship between axioms and semantic properties for these logics. Our results can be exploited in truth maintenance systems.

Semantic integration of audio-visual information of polyphonic characters in a sentence context: an event-related potential study.

PubMed

Liu, Hong; Zhang, Gaoyan; Liu, Baolin

2017-04-01

In the Chinese language, a polyphone is a kind of special character that has more than one pronunciation, with each pronunciation corresponding to a different meaning. Here, we aimed to reveal the cognitive processing of audio-visual information integration of polyphones in a sentence context using the event-related potential (ERP) method. Sentences ending with polyphones were presented to subjects simultaneously in both an auditory and a visual modality. Four experimental conditions were set in which the visual presentations were the same, but the pronunciations of the polyphones were: the correct pronunciation; another pronunciation of the polyphone; a semantically appropriate pronunciation but not the pronunciation of the polyphone; or a semantically inappropriate pronunciation but also not the pronunciation of the polyphone. The behavioral results demonstrated significant differences in response accuracies when judging the semantic meanings of the audio-visual sentences, which reflected the different demands on cognitive resources. The ERP results showed that in the early stage, abnormal pronunciations were represented by the amplitude of the P200 component. Interestingly, because the phonological information mediated access to the lexical semantics, the amplitude and latency of the N400 component changed linearly across conditions, which may reflect the gradually increased semantic mismatch in the four conditions when integrating the auditory pronunciation with the visual information. Moreover, the amplitude of the late positive shift (LPS) showed a significant correlation with the behavioral response accuracies, demonstrating that the LPS component reveals the demand of cognitive resources for monitoring and resolving semantic conflicts when integrating the audio-visual information.

The semantic Stroop effect: An ex-Gaussian analysis.

PubMed

White, Darcy; Risko, Evan F; Besner, Derek

2016-10-01

Previous analyses of the standard Stroop effect (which typically uses color words that form part of the response set) have documented effects on mean reaction times in hundreds of experiments in the literature. Less well known is the fact that ex-Gaussian analyses reveal that such effects are seen in (a) the mean of the normal distribution (mu), as well as in (b) the standard deviation of the normal distribution (sigma) and (c) the tail (tau). No ex-Gaussian analysis exists in the literature with respect to the semantically based Stroop effect (which contrasts incongruent color-associated words with, e.g., neutral controls). In the present experiments, we investigated whether the semantically based Stroop effect is also seen in the three ex-Gaussian parameters. Replicating previous reports, color naming was slower when the color was carried by an irrelevant (but incongruent) color-associated word (e.g., sky, tomato) than when the control items consisted of neutral words (e.g., keg, palace) in each of four experiments. An ex-Gaussian analysis revealed that this semantically based Stroop effect was restricted to the arithmetic mean and mu; no semantic Stroop effect was observed in tau. These data are consistent with the views (1) that there is a clear difference in the source of the semantic Stroop effect, as compared to the standard Stroop effect (evidenced by the presence vs. absence of an effect on tau), and (2) that interference associated with response competition on incongruent trials in tau is absent in the semantic Stroop effect.

Reliability in content analysis: The case of semantic feature norms classification.

PubMed

Bolognesi, Marianna; Pilgram, Roosmaryn; van den Heerik, Romy

2017-12-01

Semantic feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme-that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.

Answer Set Programming and Other Computing Paradigms

ERIC Educational Resources Information Center

Meng, Yunsong

2013-01-01

Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to…

Short Answers to Deep Questions: Supporting Teachers in Large-Class Settings

ERIC Educational Resources Information Center

McDonald, J.; Bird, R. J.; Zouaq, A.; Moskal, A. C. M.

2017-01-01

In large class settings, individualized student-teacher interaction is difficult. However, teaching interactions (e.g., formative feedback) are central to encouraging deep approaches to learning. While there has been progress in automatic short-answer grading, analysing student responses to support formative feedback at scale is arguably some way…

Gestural cue analysis in automated semantic miscommunication annotation

PubMed Central

Inoue, Masashi; Ogihara, Mitsunori; Hanada, Ryoko; Furuyama, Nobuhiro

2011-01-01

The automated annotation of conversational video by semantic miscommunication labels is a challenging topic. Although miscommunications are often obvious to the speakers as well as the observers, it is difficult for machines to detect them from the low-level features. We investigate the utility of gestural cues in this paper among various non-verbal features. Compared with gesture recognition tasks in human-computer interaction, this process is difficult due to the lack of understanding on which cues contribute to miscommunications and the implicitness of gestures. Nine simple gestural features are taken from gesture data, and both simple and complex classifiers are constructed using machine learning. The experimental results suggest that there is no single gestural feature that can predict or explain the occurrence of semantic miscommunication in our setting. PMID:23585724

Semantic Technologies and Bio-Ontologies.

PubMed

Gutierrez, Fernando

2017-01-01

As information available through data repositories constantly grows, the need for automated mechanisms for linking, querying, and sharing data has become a relevant factor both in research and industry. This situation is more evident in research fields such as the life sciences, where new experiments by different research groups are constantly generating new information regarding a wide variety of related study objects. However, current methods for representing information and knowledge are not suited for machine processing. The Semantic Technologies are a set of standards and protocols that intend to provide methods for representing and handling data that encourages reusability of information and is machine-readable. In this chapter, we will provide a brief introduction to Semantic Technologies, and how these protocols and standards have been incorporated into the life sciences to facilitate dissemination and access to information.

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

Spanish semantic feature production norms for 400 concrete concepts.

PubMed

Vivas, Jorge; Vivas, Leticia; Comesaña, Ana; Coni, Ana García; Vorano, Agostina

2017-06-01

Semantic feature production norms provide many quantitative measures of different feature and concept variables that are necessary to solve some debates surrounding the nature of the organization, both normal and pathological, of semantic memory. Despite the current existence of norms for different languages, there are still no published norms in Spanish. This article presents a new set of norms collected from 810 participants for 400 living and nonliving concepts among Spanish speakers. These norms consist of empirical collections of features that participants used to describe the concepts. Four files were elaborated: a concept-feature file, a concept-concept matrix, a feature-feature matrix, and a significantly correlated features file. We expect that these norms will be useful for researchers in the fields of experimental psychology, neuropsychology, and psycholinguistics.

Practical solutions to implementing "Born Semantic" data systems

NASA Astrophysics Data System (ADS)

Leadbetter, A.; Buck, J. J. H.; Stacey, P.

2015-12-01

The concept of data being "Born Semantic" has been proposed in recent years as a Semantic Web analogue to the idea of data being "born digital"[1], [2]. Within the "Born Semantic" concept, data are captured digitally and at a point close to the time of creation are annotated with markup terms from semantic web resources (controlled vocabularies, thesauri or ontologies). This allows heterogeneous data to be more easily ingested and amalgamated in near real-time due to the standards compliant annotation of the data. In taking the "Born Semantic" proposal from concept to operation, a number of difficulties have been encountered. For example, although there are recognised methods such as Header, Dictionary, Triples [3] for the compression, publication and dissemination of large volumes of triples these systems are not practical to deploy in the field on low-powered (both electrically and computationally) devices. Similarly, it is not practical for instruments to output fully formed semantically annotated data files if they are designed to be plugged into a modular system and the data to be centrally logged in the field as is the case on Argo floats and oceanographic gliders where internal bandwidth becomes an issue [2]. In light of these issues, this presentation will concentrate on pragmatic solutions being developed to the problem of generating Linked Data in near real-time systems. Specific examples from the European Commission SenseOCEAN project where Linked Data systems are being developed for autonomous underwater platforms, and from work being undertaken in the streaming of data from the Irish Galway Bay Cable Observatory initiative will be highlighted. Further, developments of a set of tools for the LogStash-ElasticSearch software ecosystem to allow the storing and retrieval of Linked Data will be introduced. References[1] A. Leadbetter & J. Fredericks, We have "born digital" - now what about "born semantic"?, European Geophysical Union General Assembly, 2014.[2] J. Buck & A. Leadbetter, Born semantic: linking data from sensors to users and balancing hardware limitations with data standards, European Geophysical Union General Assembly, 2015.[3] J. Fernandez et al., Binary RDF Representation for Publication and Exchange (HDT), Web Semantics 19:22-41, 2013.

Design and Applications of a GeoSemantic Framework for Integration of Data and Model Resources in Hydrologic Systems

NASA Astrophysics Data System (ADS)

Elag, M.; Kumar, P.

2016-12-01

Hydrologists today have to integrate resources such as data and models, which originate and reside in multiple autonomous and heterogeneous repositories over the Web. Several resource management systems have emerged within geoscience communities for sharing long-tail data, which are collected by individual or small research groups, and long-tail models, which are developed by scientists or small modeling communities. While these systems have increased the availability of resources within geoscience domains, deficiencies remain due to the heterogeneity in the methods, which are used to describe, encode, and publish information about resources over the Web. This heterogeneity limits our ability to access the right information in the right context so that it can be efficiently retrieved and understood without the Hydrologist's mediation. A primary challenge of the Web today is the lack of the semantic interoperability among the massive number of resources, which already exist and are continually being generated at rapid rates. To address this challenge, we have developed a decentralized GeoSemantic (GS) framework, which provides three sets of micro-web services to support (i) semantic annotation of resources, (ii) semantic alignment between the metadata of two resources, and (iii) semantic mediation among Standard Names. Here we present the design of the framework and demonstrate its application for semantic integration between data and models used in the IML-CZO. First we show how the IML-CZO data are annotated using the Semantic Annotation Services. Then we illustrate how the Resource Alignment Services and Knowledge Integration Services are used to create a semantic workflow among TopoFlow model, which is a spatially-distributed hydrologic model and the annotated data. Results of this work are (i) a demonstration of how the GS framework advances the integration of heterogeneous data and models of water-related disciplines by seamless handling of their semantic heterogeneity, (ii) an introduction of new paradigm for reusing existing and new standards as well as tools and models without the need of their implementation in the Cyberinfrastructures of water-related disciplines, and (iii) an investigation of a methodology by which distributed models can be coupled in a workflow using the GS services.

The effect of slight thinning of managed coniferous forest on landscape appreciation and psychological restoration

NASA Astrophysics Data System (ADS)

Takayama, Norimasa; Saito, Haruo; Fujiwara, Akio; Horiuchi, Masahiro

2017-12-01

We investigated the influence of slight thinning (percentage of woods: 16.6%, basal area: 9.3%) on landscape appreciation and the psychological restorative effect of an on-site setting by exposing respondents to an ordinarily managed coniferous woodland. The experiments were conducted in an experimental plot in the same coniferous woodland in May (unthinned) and October 2013 (thinned). The respondents were the same 15 individuals for both experiments. Respondents were individually exposed to the enclosed plot and the forest-view plot within the same tent for 15 min. In both sessions, respondents were required to answer three questionnaires measuring their mood (Profile of Mood States), emotion (Positive and Negative Affect Schedule), and feeling of restoration (Restorative Outcome Scale) to investigate the psychological restorative effect before and after the experiment. They completed two other questionnaires measuring appreciation for the environment (Semantic Differential) and the restorative properties of the environment (Perceived Restorativeness Scale) following the experiments. We first analyzed the difference in landscape appreciation between the unthinned and thinned conditions. We did not find any statistical difference in appreciation for the environment (Semantic Differential) or the restorative properties of the environment (Perceived Restorativeness Scale); rather, we found that weather conditions had a primary influence on landscape appreciation. With respect to the psychological restorative effect, a two-way repeated analysis of valiance (ANOVA) revealed significant main effects for a selection of indices, depending on the presence or absence of thinning. However, multiple comparison analyses revealed that these effects seemed to be due to the difference in the experimental experience rather than the presence or absence of thinning. In conclusion, the effect of the slight thinning of the managed coniferous forest was too weak to be reflected in the respondents' landscape appreciation or to exert a psychological restorative effect. Therefore, planners should consider stronger thinning as it is unlikely to result in serious damage to users' appreciation and may increase their landscape appreciation of coniferous woodland and enhance its psychological restorative effect.

BOSS: context-enhanced search for biomedical objects

PubMed Central

2012-01-01

Background There exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific "deep" search systems. The general-purpose search systems, such as PubMed, offer flexible query interface, but churn out a list of matching documents that users have to go through the results in order to find the answers to their queries. On the other hand, the "deep" search systems, such as PPI Finder and iHOP, return the precompiled results in a structured way. Their results, however, are often found only within some predefined contexts. In order to alleviate these problems, we introduce a new search engine, BOSS, Biomedical Object Search System. Methods Unlike the conventional search systems, BOSS indexes segments, rather than documents. A segment refers to a Maximal Coherent Semantic Unit (MCSU) such as phrase, clause or sentence that is semantically coherent in the given context (e.g., biomedical objects or their relations). For a user query, BOSS finds all matching segments, identifies the objects appearing in those segments, and aggregates the segments for each object. Finally, it returns the ranked list of the objects along with their matching segments. Results The working prototype of BOSS is available at http://boss.korea.ac.kr. The current version of BOSS has indexed abstracts of more than 20 million articles published during last 16 years from 1996 to 2011 across all science disciplines. Conclusion BOSS fills the gap between either ends of the spectrum by allowing users to pose context-free queries and by returning a structured set of results. Furthermore, BOSS exhibits the characteristic of good scalability, just as with conventional document search engines, because it is designed to use a standard document-indexing model with minimal modifications. Considering the features, BOSS notches up the technological level of traditional solutions for search on biomedical information. PMID:22595092

A semantic problem solving environment for integrative parasite research: identification of intervention targets for Trypanosoma cruzi.

PubMed

Parikh, Priti P; Minning, Todd A; Nguyen, Vinh; Lalithsena, Sarasi; Asiaee, Amir H; Sahoo, Satya S; Doshi, Prashant; Tarleton, Rick; Sheth, Amit P

2012-01-01

Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external databases. Reconciling the different forms of heterogeneity and effectively integrating data from disparate sources is a nontrivial task for biologists and requires a dedicated informatics infrastructure. Thus, we developed an integrated environment using Semantic Web technologies that may provide biologists the tools for managing and analyzing their data, without the need for acquiring in-depth computer science knowledge. We developed a semantic problem-solving environment (SPSE) that uses ontologies to integrate internal lab data with external resources in a Parasite Knowledge Base (PKB), which has the ability to query across these resources in a unified manner. The SPSE includes Web Ontology Language (OWL)-based ontologies, experimental data with its provenance information represented using the Resource Description Format (RDF), and a visual querying tool, Cuebee, that features integrated use of Web services. We demonstrate the use and benefit of SPSE using example queries for identifying gene knockout targets of Trypanosoma cruzi for vaccine development. Answers to these queries involve looking up multiple sources of data, linking them together and presenting the results. The SPSE facilitates parasitologists in leveraging the growing, but disparate, parasite data resources by offering an integrative platform that utilizes Semantic Web techniques, while keeping their workload increase minimal.

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.

A natural language interface plug-in for cooperative query answering in biological databases.

PubMed

Jamil, Hasan M

2012-06-11

One of the many unique features of biological databases is that the mere existence of a ground data item is not always a precondition for a query response. It may be argued that from a biologist's standpoint, queries are not always best posed using a structured language. By this we mean that approximate and flexible responses to natural language like queries are well suited for this domain. This is partly due to biologists' tendency to seek simpler interfaces and partly due to the fact that questions in biology involve high level concepts that are open to interpretations computed using sophisticated tools. In such highly interpretive environments, rigidly structured databases do not always perform well. In this paper, our goal is to propose a semantic correspondence plug-in to aid natural language query processing over arbitrary biological database schema with an aim to providing cooperative responses to queries tailored to users' interpretations. Natural language interfaces for databases are generally effective when they are tuned to the underlying database schema and its semantics. Therefore, changes in database schema become impossible to support, or a substantial reorganization cost must be absorbed to reflect any change. We leverage developments in natural language parsing, rule languages and ontologies, and data integration technologies to assemble a prototype query processor that is able to transform a natural language query into a semantically equivalent structured query over the database. We allow knowledge rules and their frequent modifications as part of the underlying database schema. The approach we adopt in our plug-in overcomes some of the serious limitations of many contemporary natural language interfaces, including support for schema modifications and independence from underlying database schema. The plug-in introduced in this paper is generic and facilitates connecting user selected natural language interfaces to arbitrary databases using a semantic description of the intended application. We demonstrate the feasibility of our approach with a practical example.

Explaining Variance in Comprehension for Students in a High-Poverty Setting

ERIC Educational Resources Information Center

Conradi, Kristin; Amendum, Steven J.; Liebfreund, Meghan D.

2016-01-01

This study examined the contributions of decoding, language, spelling, and motivation to the reading comprehension of elementary school readers in a high-poverty setting. Specifically, the research questions addressed whether and how the influences of word reading efficiency, semantic knowledge, reading self-concept, and spelling on reading…

Ground Operations Aerospace Language (GOAL) textbook

NASA Technical Reports Server (NTRS)

Dickison, L. R.

1973-01-01

The textbook provides a semantical explanation accompanying a complete set of GOAL syntax diagrams, system concepts, language component interaction, and general language concepts necessary for efficient language implementation/execution.

The Grounded Expertise Components Approach in the Novel Area of Cryptic Crossword Solving

PubMed Central

Friedlander, Kathryn J.; Fine, Philip A.

2016-01-01

This paper presents a relatively unexplored area of expertise research which focuses on the solving of British-style cryptic crossword puzzles. Unlike its American “straight-definition” counterparts, which are primarily semantically-cued retrieval tasks, the British cryptic crossword is an exercise in code-cracking detection work. Solvers learn to ignore the superficial “surface reading” of the clue, which is phrased to be deliberately misleading, and look instead for a grammatical set of coded instructions which, if executed precisely, will lead to the correct (and only) answer. Sample clues are set out to illustrate the task requirements and demands. Hypothesized aptitudes for the field might include high fluid intelligence, skill at quasi-algebraic puzzles, pattern matching, visuospatial manipulation, divergent thinking and breaking frame abilities. These skills are additional to the crystallized knowledge and word-retrieval demands which are also a feature of American crossword puzzles. The authors present results from an exploratory survey intended to identify the characteristics of the cryptic crossword solving population, and outline the impact of these results on the direction of their subsequent research. Survey results were strongly supportive of a number of hypothesized skill-sets and guided the selection of appropriate test content and research paradigms which formed the basis of an extensive research program to be reported elsewhere. The paper concludes by arguing the case for a more grounded approach to expertise studies, termed the Grounded Expertise Components Approach. In this, the design and scope of the empirical program flows from a detailed and objectively-based characterization of the research population at the very onset of the program. PMID:27199805

Opposing Effects of Semantic Diversity in Lexical and Semantic Relatedness Decisions

PubMed Central

2015-01-01

Semantic ambiguity has often been divided into 2 forms: homonymy, referring to words with 2 unrelated interpretations (e.g., bark), and polysemy, referring to words associated with a number of varying but semantically linked uses (e.g., twist). Typically, polysemous words are thought of as having a fixed number of discrete definitions, or “senses,” with each use of the word corresponding to one of its senses. In this study, we investigated an alternative conception of polysemy, based on the idea that polysemous variation in meaning is a continuous, graded phenomenon that occurs as a function of contextual variation in word usage. We quantified this contextual variation using semantic diversity (SemD), a corpus-based measure of the degree to which a particular word is used in a diverse set of linguistic contexts. In line with other approaches to polysemy, we found a reaction time (RT) advantage for high SemD words in lexical decision, which occurred for words of both high and low imageability. When participants made semantic relatedness decisions to word pairs, however, responses were slower to high SemD pairs, irrespective of whether these were related or unrelated. Again, this result emerged irrespective of the imageability of the word. The latter result diverges from previous findings using homonyms, in which ambiguity effects have only been found for related word pairs. We argue that participants were slower to respond to high SemD words because their high contextual variability resulted in noisy, underspecified semantic representations that were more difficult to compare with one another. We demonstrated this principle in a connectionist computational model that was trained to activate distributed semantic representations from orthographic inputs. Greater variability in the orthography-to-semantic mappings of high SemD words resulted in a lower degree of similarity for related pairs of this type. At the same time, the representations of high SemD unrelated pairs were less distinct from one another. In addition, the model demonstrated more rapid semantic activation for high SemD words, thought to underpin the processing advantage in lexical decision. These results support the view that polysemous variation in word meaning can be conceptualized in terms of graded variation in distributed semantic representations. PMID:25751041

Replacing Maladaptive Speech with Verbal Labeling Responses: An Analysis of Generalized Responding.

ERIC Educational Resources Information Center

Foxx, R. M.; And Others

1988-01-01

Three mentally handicapped students (aged 13, 36, and 40) with maladaptive speech received training to answer questions with verbal labels. The results of their cues-pause-point training showed that the students replaced their maladaptive speech with correct labels (answers) to questions in the training setting and three generalization settings.…

The Impact of Problem Sets on Student Learning

ERIC Educational Resources Information Center

Kim, Myeong Hwan; Cho, Moon-Heum; Leonard, Karen Moustafa

2012-01-01

The authors examined the role of problem sets on student learning in university microeconomics. A total of 126 students participated in the study in consecutive years. independent samples t test showed that students who were not given answer keys outperformed students who were given answer keys. Multiple regression analysis showed that, along with…

DeepMeSH: deep semantic representation for improving large-scale MeSH indexing

PubMed Central

Peng, Shengwen; You, Ronghui; Wang, Hongning; Zhai, Chengxiang; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the ‘learning to rank’ framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. Availability and Implementation: The software is available upon request. Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307646

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Unitary vs multiple semantics: PET studies of word and picture processing.

PubMed

Bright, P; Moss, H; Tyler, L K

2004-06-01

In this paper we examine a central issue in cognitive neuroscience: are there separate conceptual representations associated with different input modalities (e.g., Paivio, 1971, 1986; Warrington & Shallice, 1984) or do inputs from different modalities converge on to the same set of representations (e.g., Caramazza, Hillis, Rapp, & Romani, 1990; Lambon Ralph, Graham, Patterson, & Hodges, 1999; Rapp, Hillis, & Caramazza, 1993)? We present an analysis of four PET studies (three semantic categorisation tasks and one lexical decision task), two of which employ words as stimuli and two of which employ pictures. Using conjunction analyses, we found robust semantic activation, common to both input modalities in anterior and medial aspects of the left fusiform gyrus, left parahippocampal and perirhinal cortices, and left inferior frontal gyrus (BA 47). There were modality-specific activations in both temporal poles (words) and occipitotemporal cortices (pictures). We propose that the temporal poles are involved in processing both words and pictures, but their engagement might be primarily determined by the level of specificity at which an object is processed. Activation in posterior temporal regions associated with picture processing most likely reflects intermediate, pre-semantic stages of visual processing. Our data are most consistent with a hierarchically structured, unitary system of semantic representations for both verbal and visual modalities, subserved by anterior regions of the inferior temporal cortex.

A common type system for clinical natural language processing

PubMed Central

2013-01-01

Background One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. Results We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. Conclusions We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types. PMID:23286462

Event Congruency Enhances Episodic Memory Encoding through Semantic Elaboration and Relational Binding

PubMed Central

Staresina, Bernhard P.; Gray, James C.

2009-01-01

Behavioral research consistently shows that congruous events, that is, events whose constituent elements match along some specific dimension, are better remembered than incongruous events. Although it has been speculated that this “congruency subsequent memory effect” (cSME) results from enhanced semantic elaboration, empirical evidence for this account is lacking. Here, we report a set of behavioral and neuroimaging experiments demonstrating that congruous events engage regions along the left inferior frontal gyrus (LIFG)—consistently related to semantic elaboration—to a significantly greater degree than incongruous events, providing evidence in favor of this hypothesis. Critically, we additionally report 3 novel findings in relation to event congruency: First, congruous events yield superior memory not only for a given study item but also for associated source details. Second, the cSME is evident not only for events that matched a semantic context but also for those that matched a subjective aesthetic schema. Finally, functional magnetic resonance imaging brain/behavior correlation analysis reveals a strong link between 1) across-subject variation in the magnitude of the cSME and 2) differential right hippocampal activation, suggesting that episodic memory for congruous events is effectively bolstered by the extent to which semantic associations are generated and relationally integrated via LIFG-hippocampal–encoding mechanisms. PMID:18820289

Joint Attributes and Event Analysis for Multimedia Event Detection.

PubMed

Ma, Zhigang; Chang, Xiaojun; Xu, Zhongwen; Sebe, Nicu; Hauptmann, Alexander G

2017-06-15

Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos. To address this issue, we propose to learn semantic attributes from external videos using their semantic labels. We name them video attributes in this paper. In contrast with multimedia event videos, these external videos depict lower level contents such as objects, scenes, and actions. To harness video attributes, we propose an algorithm established on a correlation vector that correlates them to a target event. Consequently, we could incorporate video attributes latently as extra information into the event detector learnt from multimedia event videos in a joint framework. To validate our method, we perform experiments on the real-world large-scale TRECVID MED 2013 and 2014 data sets and compare our method with several state-of-the-art algorithms. The experiments show that our method is advantageous for MED.

Three more semantic serial position functions and a SIMPLE explanation.

PubMed

Kelley, Matthew R; Neath, Ian; Surprenant, Aimée M

2013-05-01

There are innumerable demonstrations of serial position functions-with characteristic primacy and recency effects-in episodic tasks, but there are only a handful of such demonstrations in semantic memory tasks, and those demonstrations have used only two types of stimuli. Here, we provide three more examples of serial position functions when recalling from semantic memory. Participants were asked to reconstruct the order of (1) two cartoon theme song lyrics, (2) the seven Harry Potter books, and (3) two sets of movies, and all three demonstrations yielded conventional-looking serial position functions with primacy and recency effects. The data were well-fit by SIMPLE, a local distinctiveness model of memory that was originally designed to account for serial position effects in short- and long-term episodic memory. According to SIMPLE, serial position functions in both episodic and semantic memory tasks arise from the same type of processing: Items that are more separated from their close neighbors in psychological space at the time of recall will be better remembered. We argue that currently available evidence suggests that serial position functions observed when recalling items that are presumably in semantic memory arise because of the same processes as those observed when recalling items that are presumably in episodic memory.

Leveraging Pattern Semantics for Extracting Entities in Enterprises

PubMed Central

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-01-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises (“Blue” refers to “Windows Blue”), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach. PMID:26705540

Leveraging Pattern Semantics for Extracting Entities in Enterprises.

PubMed

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-05-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises ("Blue" refers to "Windows Blue"), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach.

Semantic attributes for people's appearance description: an appearance modality for video surveillance applications

NASA Astrophysics Data System (ADS)

Frikha, Mayssa; Fendri, Emna; Hammami, Mohamed

2017-09-01

Using semantic attributes such as gender, clothes, and accessories to describe people's appearance is an appealing modeling method for video surveillance applications. We proposed a midlevel appearance signature based on extracting a list of nameable semantic attributes describing the body in uncontrolled acquisition conditions. Conventional approaches extract the same set of low-level features to learn the semantic classifiers uniformly. Their critical limitation is the inability to capture the dominant visual characteristics for each trait separately. The proposed approach consists of extracting low-level features in an attribute-adaptive way by automatically selecting the most relevant features for each attribute separately. Furthermore, relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations. We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions. These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes. Various experiments prove that carefully designed features for learning the visual characteristics for an attribute provide an improvement of the correct classification accuracy and a reduction of both spatial and temporal complexities against state-of-the-art approaches.

Tableau Calculus for the Logic of Comparative Similarity over Arbitrary Distance Spaces

NASA Astrophysics Data System (ADS)

Alenda, Régis; Olivetti, Nicola

The logic CSL (first introduced by Sheremet, Tishkovsky, Wolter and Zakharyaschev in 2005) allows one to reason about distance comparison and similarity comparison within a modal language. The logic can express assertions of the kind "A is closer/more similar to B than to C" and has a natural application to spatial reasoning, as well as to reasoning about concept similarity in ontologies. The semantics of CSL is defined in terms of models based on different classes of distance spaces and it generalizes the logic S4 u of topological spaces. In this paper we consider CSL defined over arbitrary distance spaces. The logic comprises a binary modality to represent comparative similarity and a unary modality to express the existence of the minimum of a set of distances. We first show that the semantics of CSL can be equivalently defined in terms of preferential models. As a consequence we obtain the finite model property of the logic with respect to its preferential semantic, a property that does not hold with respect to the original distance-space semantics. Next we present an analytic tableau calculus based on its preferential semantics. The calculus provides a decision procedure for the logic, its termination is obtained by imposing suitable blocking restrictions.

MPEG-7-based description infrastructure for an audiovisual content analysis and retrieval system

NASA Astrophysics Data System (ADS)

Bailer, Werner; Schallauer, Peter; Hausenblas, Michael; Thallinger, Georg

2005-01-01

We present a case study of establishing a description infrastructure for an audiovisual content-analysis and retrieval system. The description infrastructure consists of an internal metadata model and access tool for using it. Based on an analysis of requirements, we have selected, out of a set of candidates, MPEG-7 as the basis of our metadata model. The openness and generality of MPEG-7 allow using it in broad range of applications, but increase complexity and hinder interoperability. Profiling has been proposed as a solution, with the focus on selecting and constraining description tools. Semantic constraints are currently only described in textual form. Conformance in terms of semantics can thus not be evaluated automatically and mappings between different profiles can only be defined manually. As a solution, we propose an approach to formalize the semantic constraints of an MPEG-7 profile using a formal vocabulary expressed in OWL, which allows automated processing of semantic constraints. We have defined the Detailed Audiovisual Profile as the profile to be used in our metadata model and we show how some of the semantic constraints of this profile can be formulated using ontologies. To work practically with the metadata model, we have implemented a MPEG-7 library and a client/server document access infrastructure.

A common type system for clinical natural language processing.

PubMed

Wu, Stephen T; Kaggal, Vinod C; Dligach, Dmitriy; Masanz, James J; Chen, Pei; Becker, Lee; Chapman, Wendy W; Savova, Guergana K; Liu, Hongfang; Chute, Christopher G

2013-01-03

One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.

Biotea: RDFizing PubMed Central in support for the paper as an interface to the Web of Data

PubMed Central

2013-01-01

Background The World Wide Web has become a dissemination platform for scientific and non-scientific publications. However, most of the information remains locked up in discrete documents that are not always interconnected or machine-readable. The connectivity tissue provided by RDF technology has not yet been widely used to support the generation of self-describing, machine-readable documents. Results In this paper, we present our approach to the generation of self-describing machine-readable scholarly documents. We understand the scientific document as an entry point and interface to the Web of Data. We have semantically processed the full-text, open-access subset of PubMed Central. Our RDF model and resulting dataset make extensive use of existing ontologies and semantic enrichment services. We expose our model, services, prototype, and datasets at http://biotea.idiginfo.org/ Conclusions The semantic processing of biomedical literature presented in this paper embeds documents within the Web of Data and facilitates the execution of concept-based queries against the entire digital library. Our approach delivers a flexible and adaptable set of tools for metadata enrichment and semantic processing of biomedical documents. Our model delivers a semantically rich and highly interconnected dataset with self-describing content so that software can make effective use of it. PMID:23734622

C-Speak Aphasia Alternative Communication Program for People with Severe Aphasia: Importance of Executive Functioning and Semantic Knowledge

PubMed Central

Nicholas, Marjorie; Sinotte, Michele P.; Helm-Estabrooks, Nancy

2011-01-01

Learning how to use a computer-based communication system can be challenging for people with severe aphasia even if the system is not word-based. This study explored cognitive and linguistic factors relative to how they affected individual patients’ ability to communicate expressively using C-Speak Aphasia, (CSA), an alternative communication computer program that is primarily picture-based. Ten individuals with severe non-fluent aphasia received at least six months of training with CSA. To assess carryover of training, untrained functional communication tasks (i.e., answering autobiographical questions, describing pictures, making telephone calls, describing a short video, and two writing tasks) were repeatedly probed in two conditions: 1) using CSA in addition to natural forms of communication, and 2) using only natural forms of communication, e.g., speaking, writing, gesturing, drawing. Four of the ten participants communicated more information on selected probe tasks using CSA than they did without the computer. Response to treatment also was examined in relation to baseline measures of non-linguistic executive function skills, pictorial semantic abilities, and auditory comprehension. Only nonlinguistic executive function skills were significantly correlated with treatment response. PMID:21506045

Variation of SNOMED CT coding of clinical research concepts among coding experts.

PubMed

Andrews, James E; Richesson, Rachel L; Krischer, Jeffrey

2007-01-01

To compare consistency of coding among professional SNOMED CT coders representing three commercial providers of coding services when coding clinical research concepts with SNOMED CT. A sample of clinical research questions from case report forms (CRFs) generated by the NIH-funded Rare Disease Clinical Research Network (RDCRN) were sent to three coding companies with instructions to code the core concepts using SNOMED CT. The sample consisted of 319 question/answer pairs from 15 separate studies. The companies were asked to select SNOMED CT concepts (in any form, including post-coordinated) that capture the core concept(s) reflected in the question. Also, they were asked to state their level of certainty, as well as how precise they felt their coding was. Basic frequencies were calculated to determine raw level agreement among the companies and other descriptive information. Krippendorff's alpha was used to determine a statistical measure of agreement among the coding companies for several measures (semantic, certainty, and precision). No significant level of agreement among the experts was found. There is little semantic agreement in coding of clinical research data items across coders from 3 professional coding services, even using a very liberal definition of agreement.

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside

PubMed Central

2011-01-01

Background Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery. Results We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action. Conclusions This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. Availability TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql. PMID:21624155

Are Cantonese-speakers really descriptivists? Revisiting cross-cultural semantics.

PubMed

Lam, Barry

2010-05-01

In an article in Cognition [Machery, E., Mallon, R., Nichols, S., & Stich, S. (2004). Semantics cross-cultural style. Cognition, 92, B1-B12] present data which purports to show that East Asian Cantonese-speakers tend to have descriptivist intuitions about the referents of proper names, while Western English-speakers tend to have causal-historical intuitions about proper names. Machery et al. take this finding to support the view that some intuitions, the universality of which they claim is central to philosophical theories, vary according to cultural background. Machery et al. conclude from their findings that the philosophical methodology of consulting intuitions about hypothetical cases is flawed vis a vis the goal of determining truths about some philosophical domains like philosophical semantics. In the following study, three new vignettes in English were given to Western native English-speakers, and Cantonese translations were given to native Cantonese-speaking immigrants from a Cantonese community in Southern California. For all three vignettes, questions were given to elicit intuitions about the referent of a proper name and the truth-value of an uttered sentence containing a proper name. The results from this study reveal that East Asian Cantonese-speakers do not differ from Western English-speakers in ways that support Machery et al.'s conclusions. This new data concerning the intuitions of Cantonese-speakers raises questions about whether cross-cultural variation in answers to questions on certain vignettes reveal genuine differences in intuitions, or whether such differences stem from non-intuitional differences, such as differences in linguistic competence. Copyright 2009 Elsevier B.V. All rights reserved.

15 CFR 766.6 - Answer and demand for hearing.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) BUREAU OF INDUSTRY AND SECURITY, DEPARTMENT OF COMMERCE EXPORT ADMINISTRATION REGULATIONS ADMINISTRATIVE... respondent's defense or defenses. The answer must admit or deny specifically each separate allegation of the... defense or claim of mitigation. Any defense or partial defense not specifically set forth in the answer...

15 CFR 766.6 - Answer and demand for hearing.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) BUREAU OF INDUSTRY AND SECURITY, DEPARTMENT OF COMMERCE EXPORT ADMINISTRATION REGULATIONS ADMINISTRATIVE... respondent's defense or defenses. The answer must admit or deny specifically each separate allegation of the... defense or claim of mitigation. Any defense or partial defense not specifically set forth in the answer...

15 CFR 766.6 - Answer and demand for hearing.

Code of Federal Regulations, 2012 CFR

2012-01-01

...) BUREAU OF INDUSTRY AND SECURITY, DEPARTMENT OF COMMERCE EXPORT ADMINISTRATION REGULATIONS ADMINISTRATIVE... respondent's defense or defenses. The answer must admit or deny specifically each separate allegation of the... defense or claim of mitigation. Any defense or partial defense not specifically set forth in the answer...

15 CFR 766.6 - Answer and demand for hearing.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) BUREAU OF INDUSTRY AND SECURITY, DEPARTMENT OF COMMERCE EXPORT ADMINISTRATION REGULATIONS ADMINISTRATIVE... respondent's defense or defenses. The answer must admit or deny specifically each separate allegation of the... defense or claim of mitigation. Any defense or partial defense not specifically set forth in the answer...

15 CFR 766.6 - Answer and demand for hearing.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) BUREAU OF INDUSTRY AND SECURITY, DEPARTMENT OF COMMERCE EXPORT ADMINISTRATION REGULATIONS ADMINISTRATIVE... respondent's defense or defenses. The answer must admit or deny specifically each separate allegation of the... defense or claim of mitigation. Any defense or partial defense not specifically set forth in the answer...

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.

PubMed

Velupillai, S; Mowery, D; South, B R; Kvist, M; Dalianis, H

2015-08-13

We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis. We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers. Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications. There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.

Lexical selection in the semantically blocked cyclic naming task: the role of cognitive control and learning

PubMed Central

Crowther, Jason E.; Martin, Randi C.

2014-01-01

Studies of semantic interference in language production have provided evidence for a role of cognitive control mechanisms in regulating the activation of semantic competitors during naming. The present study investigated the relationship between individual differences in cognitive control abilities, for both younger and older adults, and the degree of semantic interference in a blocked cyclic naming task. We predicted that individuals with lower working memory capacity (as measured by word span), lesser ability to inhibit distracting responses (as measured by Stroop interference), and a lesser ability to resolve proactive interference (as measured by a recent negatives task) would show a greater increase in semantic interference in naming, with effects being larger for older adults. Instead, measures of cognitive control were found to relate to specific indices of semantic interference in the naming task, rather than overall degree of semantic interference, and few interactions with age were found, with younger and older adults performing similarly. The increase in naming latencies across naming trials within a cycle was negatively correlated with word span for both related and unrelated conditions, suggesting a strategy of narrowing response alternatives based upon memory for the set of item names. Evidence for a role of inhibition in response selection was obtained, as Stroop interference correlated positively with the change in naming latencies across cycles for the related, but not unrelated, condition. In contrast, recent negatives interference correlated negatively with the change in naming latencies across unrelated cycles, suggesting that individual differences in this tap the degree of strengthening of links in a lexical network based upon prior exposure. Results are discussed in terms of current models of lexical selection and consequences for word retrieval in more naturalistic production. PMID:24478675

Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis

PubMed Central

Mowery, D.; South, B. R.; Kvist, M.; Dalianis, H.

2015-01-01

Summary Objectives We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis. Methods We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers. Results Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications. Conclusions There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices. PMID:26293867

Towards a Semantic Web of Community, Content and Interactions

DTIC Science & Technology

2005-09-01

importance of setting goals and deadlines as a means to achieving progress on the nebulous road to a dissertation. Jim Herbsleb sparked my interest in...RDF, such as Turtle [Bec04], a text syntax for RDF, and N-Triples [GDB04]. 45 </dc:creator> <dc:title>The Semantic Web: An Introduction</dc:title...2):22–41, 1990. 2.2.2 [Bec04] Dave Beckett. Turtle –terse rdf triple language. http://www.ilrt.bris.ac.uk/discovery/2004/01/ turtle /, January 2004. 4

Next generation data harmonization

NASA Astrophysics Data System (ADS)

Armstrong, Chandler; Brown, Ryan M.; Chaves, Jillian; Czerniejewski, Adam; Del Vecchio, Justin; Perkins, Timothy K.; Rudnicki, Ron; Tauer, Greg

2015-05-01

Analysts are presented with a never ending stream of data sources. Often, subsets of data sources to solve problems are easily identified but the process to align data sets is time consuming. However, many semantic technologies do allow for fast harmonization of data to overcome these problems. These include ontologies that serve as alignment targets, visual tools and natural language processing that generate semantic graphs in terms of the ontologies, and analytics that leverage these graphs. This research reviews a developed prototype that employs all these approaches to perform analysis across disparate data sources documenting violent, extremist events.

The semantics of Chemical Markup Language (CML): dictionaries and conventions.

PubMed

Murray-Rust, Peter; Townsend, Joe A; Adams, Sam E; Phadungsukanan, Weerapong; Thomas, Jens

2011-10-14

The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs.

The semantics of Chemical Markup Language (CML): dictionaries and conventions

PubMed Central

2011-01-01

The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs. PMID:21999509

Anchoring in Numeric Judgments of Visual Stimuli

PubMed Central

Langeborg, Linda; Eriksson, Mårten

2016-01-01

This article investigates effects of anchoring in age estimation and estimation of quantities, two tasks which to different extents are based on visual stimuli. The results are compared to anchoring in answers to classic general knowledge questions that rely on semantic knowledge. Cognitive load was manipulated to explore possible differences between domains. Effects of source credibility, manipulated by differing instructions regarding the selection of anchor values (no information regarding anchor selection, information that the anchors are randomly generated or information that the anchors are answers from an expert) on anchoring were also investigated. Effects of anchoring were large for all types of judgments but were not affected by cognitive load or by source credibility in either one of the researched domains. A main effect of cognitive load on quantity estimations and main effects of source credibility in the two visually based domains indicate that the manipulations were efficient. Implications for theoretical explanations of anchoring are discussed. In particular, because anchoring did not interact with cognitive load, the results imply that the process behind anchoring in visual tasks is predominantly automatic and unconscious. PMID:26941684

Argument structure and the representation of abstract semantics.

PubMed

Rodríguez-Ferreiro, Javier; Andreu, Llorenç; Sanz-Torrent, Mònica

2014-01-01

According to the dual coding theory, differences in the ease of retrieval between concrete and abstract words are related to the exclusive dependence of abstract semantics on linguistic information. Argument structure can be considered a measure of the complexity of the linguistic contexts that accompany a verb. If the retrieval of abstract verbs relies more on the linguistic codes they are associated to, we could expect a larger effect of argument structure for the processing of abstract verbs. In this study, sets of length- and frequency-matched verbs including 40 intransitive verbs, 40 transitive verbs taking simple complements, and 40 transitive verbs taking sentential complements were presented in separate lexical and grammatical decision tasks. Half of the verbs were concrete and half were abstract. Similar results were obtained in the two tasks, with significant effects of imageability and transitivity. However, the interaction between these two variables was not significant. These results conflict with hypotheses assuming a stronger reliance of abstract semantics on linguistic codes. In contrast, our data are in line with theories that link the ease of retrieval with availability and robustness of semantic information.

Semantic Service Matchmaking in the ATM Domain Considering Infrastructure Capability Constraints

NASA Astrophysics Data System (ADS)

Moser, Thomas; Mordinyi, Richard; Sunindyo, Wikan Danar; Biffl, Stefan

In a service-oriented environment business processes flexibly build on software services provided by systems in a network. A key design challenge is the semantic matchmaking of business processes and software services in two steps: 1. Find for one business process the software services that meet or exceed the BP requirements; 2. Find for all business processes the software services that can be implemented within the capability constraints of the underlying network, which poses a major problem since even for small scenarios the solution space is typically very large. In this chapter we analyze requirements from mission-critical business processes in the Air Traffic Management (ATM) domain and introduce an approach for semi-automatic semantic matchmaking for software services, the “System-Wide Information Sharing” (SWIS) business process integration framework. A tool-supported semantic matchmaking process like SWIS can provide system designers and integrators with a set of promising software service candidates and therefore strongly reduces the human matching effort by focusing on a much smaller space of matchmaking candidates. We evaluate the feasibility of the SWIS approach in an industry use case from the ATM domain.

Neural correlates of three cognitive processes involved in theory of mind and discourse comprehension.

PubMed

Lin, Nan; Yang, Xiaohong; Li, Jing; Wang, Shaonan; Hua, Huimin; Ma, Yujun; Li, Xingshan

2018-04-01

Neuroimaging studies have found that theory of mind (ToM) and discourse comprehension involve similar brain regions. These brain regions may be associated with three cognitive components that are necessarily or frequently involved in ToM and discourse comprehension, including social concept representation and retrieval, domain-general semantic integration, and domain-specific integration of social semantic contents. Using fMRI, we investigated the neural correlates of these three cognitive components by exploring how discourse topic (social/nonsocial) and discourse processing period (ending/beginning) modulate brain activation in a discourse comprehension (and also ToM) task. Different sets of brain areas showed sensitivity to discourse topic, discourse processing period, and the interaction between them, respectively. The most novel finding was that the right temporoparietal junction and middle temporal gyrus showed sensitivity to discourse processing period only during social discourse comprehension, indicating that they selectively contribute to domain-specific semantic integration. Our finding indicates how different domains of semantic information are processed and integrated in the brain and provides new insights into the neural correlates of ToM and discourse comprehension.

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.

The semantics of prosody: acoustic and perceptual evidence of prosodic correlates to word meaning.

PubMed

Nygaard, Lynne C; Herold, Debora S; Namy, Laura L

2009-01-01

This investigation examined whether speakers produce reliable prosodic correlates to meaning across semantic domains and whether listeners use these cues to derive word meaning from novel words. Speakers were asked to produce phrases in infant-directed speech in which novel words were used to convey one of two meanings from a set of antonym pairs (e.g., big/small). Acoustic analyses revealed that some acoustic features were correlated with overall valence of the meaning. However, each word meaning also displayed a unique acoustic signature, and semantically related meanings elicited similar acoustic profiles. In two perceptual tests, listeners either attempted to identify the novel words with a matching meaning dimension (picture pair) or with mismatched meaning dimensions. Listeners inferred the meaning of the novel words significantly more often when prosody matched the word meaning choices than when prosody mismatched. These findings suggest that speech contains reliable prosodic markers to word meaning and that listeners use these prosodic cues to differentiate meanings. That prosody is semantic suggests a reconceptualization of traditional distinctions between linguistic and nonlinguistic properties of spoken language. Copyright © 2009 Cognitive Science Society, Inc.

Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis.

PubMed

Ye, Zheng; Doñamayor, Nuria; Münte, Thomas F

2014-02-01

A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context. To look into this issue, we conducted a functional magnetic resonance imaging (fMRI) study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis (ICA) and graph theoretical analysis (GTA). The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area (SMA), left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending. Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs. congruent endings. Copyright © 2012 Wiley Periodicals, Inc.

BioUSeR: a semantic-based tool for retrieving Life Science web resources driven by text-rich user requirements

PubMed Central

2013-01-01

Background Open metadata registries are a fundamental tool for researchers in the Life Sciences trying to locate resources. While most current registries assume that resources are annotated with well-structured metadata, evidence shows that most of the resource annotations simply consists of informal free text. This reality must be taken into account in order to develop effective techniques for resource discovery in Life Sciences. Results BioUSeR is a semantic-based tool aimed at retrieving Life Sciences resources described in free text. The retrieval process is driven by the user requirements, which consist of a target task and a set of facets of interest, both expressed in free text. BioUSeR is able to effectively exploit the available textual descriptions to find relevant resources by using semantic-aware techniques. Conclusions BioUSeR overcomes the limitations of the current registries thanks to: (i) rich specification of user information needs, (ii) use of semantics to manage textual descriptions, (iii) retrieval and ranking of resources based on user requirements. PMID:23635042

MESUR: USAGE-BASED METRICS OF SCHOLARLY IMPACT

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

BOLLEN, JOHAN; RODRIGUEZ, MARKO A.; VAN DE SOMPEL, HERBERT

2007-01-30

The evaluation of scholarly communication items is now largely a matter of expert opinion or metrics derived from citation data. Both approaches can fail to take into account the myriad of factors that shape scholarly impact. Usage data has emerged as a promising complement to existing methods o fassessment but the formal groundwork to reliably and validly apply usage-based metrics of schlolarly impact is lacking. The Andrew W. Mellon Foundation funded MESUR project constitutes a systematic effort to define, validate and cross-validate a range of usage-based metrics of schlolarly impact by creating a semantic model of the scholarly communication process.more » The constructed model will serve as the basis of a creating a large-scale semantic network that seamlessly relates citation, bibliographic and usage data from a variety of sources. A subsequent program that uses the established semantic network as a reference data set will determine the characteristics and semantics of a variety of usage-based metrics of schlolarly impact. This paper outlines the architecture and methodology adopted by the MESUR project and its future direction.« less

Adaptive and Context-Aware Reconciliation of Reactive and Pro-active Behavior in Evolving Systems

NASA Astrophysics Data System (ADS)

Trajcevski, Goce; Scheuermann, Peter

One distinct characteristics of the context-aware systems is their ability to react and adapt to the evolution of the environment, which is often a result of changes in the values of various (possibly correlated) attributes. Based on these changes, reactive systems typically take corrective actions, e.g., adjusting parameters in order to maintain the desired specifications of the system's state. Pro-active systems, on the other hand, may change the mode of interaction with the environment as well as the desired goals of the system. In this paper we describe our (ECA)2 paradigm for reactive behavior with proactive impact and we present our ongoing work and vision for a system that is capable of context-aware adaptation, while ensuring the maintenance of a set of desired behavioral policies. Our main focus is on developing a formalism that provides tools for expressing normal, as well as defeasible and/or exceptional specification. However, at the same time, we insist on a sound semantics and the capability of answering hypothetical "what-if" queries. Towards this end, we introduce the high-level language L_{ EAR} that can be used to describe the dynamics of the problem domain, specify triggers under the (ECA)2 paradigm, and reason about the consequences of the possible evolutions.

Integrating user profile in medical CBIR systems to answer perceptual similarity queries

NASA Astrophysics Data System (ADS)

Bugatti, Pedro H.; Kaster, Daniel S.; Ponciano-Silva, Marcelo; Traina, Agma J. M.; Traina, Caetano, Jr.

2011-03-01

Techniques for Content-Based Image Retrieval (CBIR) have been intensively explored due to the increase in the amount of captured images and the need of fast retrieval of them. The medical field is a specific example that generates a large flow of information, especially digital images employed for diagnosing. One issue that still remains unsolved deals with how to reach the perceptual similarity. That is, to achieve an effective retrieval, one must characterize and quantify the perceptual similarity regarding the specialist in the field. Therefore, the present paper was conceived to fill in this gap creating a consistent support to perform similarity queries over medical images, maintaining the semantics of a given query desired by the user. CBIR systems relying in relevance feedback techniques usually request the users to label relevant images. In this paper, we present a simple but highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The user profiles maintain the settings desired for each user, allowing tuning the similarity assessment, which encompasses dynamically changing the distance function employed through an interactive process. Experiments using computed tomography lung images show that the proposed approach is effective in capturing the users' perception.

The irreducible photon

NASA Astrophysics Data System (ADS)

Andrews, David L.

2009-08-01

In recent years it has become evident that the primary concept of the photon has multiple interpretations, with widely differing secondary connotations. Despite the all-pervasive nature of this concept in science, some of the ancillary properties with which the photon is attributed in certain areas of application sit uneasily alongside those invoked in other areas. Certainly the range of applications extends far beyond what was envisaged in the original conception, now entering subjects extending from elementary particle physics and cosmology through to spectroscopy, statistical mechanics and photochemistry. Addressing this diverse context invites the question: What is there, that it is possible to assert as incontrovertibly true about the photon? Which properties are non-controversial, if others are the subject of debate? This paper describes an attempt to answer these questions, establishing as far as possible an irreducible core of what can rightly be asserted about the photon, and setting aside some of what often is, but should never be so asserted. Some of the more bewildering difficulties and differences of interpretation owe their origin to careless descriptions, highlighting a need to guard semantic precision; although simplifications are frequently and naturally expedient for didactic purposes, they carry the risk of becoming indelible. Focusing on such issues, the aim is to identify how much or how little about the photon can be regarded as truly non-controversial.

The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation

PubMed Central

2011-01-01

Background The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. Description SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. Conclusions SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies. PMID:22024447

The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation.

PubMed

Wilkinson, Mark D; Vandervalk, Benjamin; McCarthy, Luke

2011-10-24

The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies.

Further Support for Changing Multiple-Choice Answers.

ERIC Educational Resources Information Center

Fabrey, Lawrence J.; Case, Susan M.

1985-01-01

The effect on test scores of changing answers to multiple-choice questions was studied and compared to earlier research. The current setting was a nationally administered, in-training, specialty examination for medical residents in obstetrics and gynecology. Both low and high scorers improved their scores when they changed answers. (SW)

77 FR 65463 - Filing of Privileged Materials and Answers to Motions

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-29

... Answers to Motions AGENCY: Federal Energy Regulatory Commission, DOE. ACTION: Final rule. SUMMARY: In this... time period for action. Instead, the Commission proposes to set the time for responding to such motions... Rules of Practice and Procedure, which establishes the timeline for filing answers to motions, to...

Connes' embedding problem and Tsirelson's problem

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

Junge, M.; Palazuelos, C.; Navascues, M.

2011-01-15

We show that Tsirelson's problem concerning the set of quantum correlations and Connes' embedding problem on finite approximations in von Neumann algebras (known to be equivalent to Kirchberg's QWEP conjecture) are essentially equivalent. Specifically, Tsirelson's problem asks whether the set of bipartite quantum correlations generated between tensor product separated systems is the same as the set of correlations between commuting C{sup *}-algebras. Connes' embedding problem asks whether any separable II{sub 1} factor is a subfactor of the ultrapower of the hyperfinite II{sub 1} factor. We show that an affirmative answer to Connes' question implies a positive answer to Tsirelson's. Conversely,more » a positive answer to a matrix valued version of Tsirelson's problem implies a positive one to Connes' problem.« less

Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks.

PubMed

Tang, Guoyu; Ni, Yuan; Wang, Keqiang; Yong, Qin

2018-01-01

The online patient question and answering (Q&A) system attracts an increasing amount of users in China. Patient will post their questions and wait for doctors' response. To avoid the lag time involved with the waiting and to reduce the workload on the doctors, a better method is to automatically retrieve the semantically equivalent question from the archive. We present a Generative Adversarial Networks (GAN) based approach to automatically retrieve patient question. We apply supervised deep learning based approaches to determine the similarity between patient questions. Then a GAN framework is used to fine-tune the pre-trained deep learning models. The experiment results show that fine-tuning by GAN can improve the performance.

Besides Precision & Recall: Exploring Alternative Approaches to Evaluating an Automatic Indexing Tool for MEDLINE

PubMed Central

Névéol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

Objective This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. Materials and methods The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Results Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. Conclusions The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis. PMID:17238409

Besides precision & recall: exploring alternative approaches to evaluating an automatic indexing tool for MEDLINE.

PubMed

Neveol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis.

Using Neural Networks to Generate Inferential Roles for Natural Language

PubMed Central

Blouw, Peter; Eliasmith, Chris

2018-01-01

Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences are likely to follow from a given sentence; these define the sentence's “inferential role.” We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition. PMID:29387031

Semantic and perceptual processing of number symbols: evidence from a cross-linguistic fMRI adaptation study.

PubMed

Holloway, Ian D; Battista, Christian; Vogel, Stephan E; Ansari, Daniel

2013-03-01

The ability to process the numerical magnitude of sets of items has been characterized in many animal species. Neuroimaging data have associated this ability to represent nonsymbolic numerical magnitudes (e.g., arrays of dots) with activity in the bilateral parietal lobes. Yet the quantitative abilities of humans are not limited to processing the numerical magnitude of nonsymbolic sets. Humans have used this quantitative sense as the foundation for symbolic systems for the representation of numerical magnitude. Although numerical symbol use is widespread in human cultures, the brain regions involved in processing of numerical symbols are just beginning to be understood. Here, we investigated the brain regions underlying the semantic and perceptual processing of numerical symbols. Specifically, we used an fMRI adaptation paradigm to examine the neural response to Hindu-Arabic numerals and Chinese numerical ideographs in a group of Chinese readers who could read both symbol types and a control group who could read only the numerals. Across groups, the Hindu-Arabic numerals exhibited ratio-dependent modulation in the left IPS. In contrast, numerical ideographs were associated with activation in the right IPS, exclusively in the Chinese readers. Furthermore, processing of the visual similarity of both digits and ideographs was associated with activation of the left fusiform gyrus. Using culture as an independent variable, we provide clear evidence for differences in the brain regions associated with the semantic and perceptual processing of numerical symbols. Additionally, we reveal a striking difference in the laterality of parietal activation between the semantic processing of the two symbols types.

Mining large heterogeneous data sets in drug discovery.

PubMed

Wild, David J

2009-10-01

Increasingly, effective drug discovery involves the searching and data mining of large volumes of information from many sources covering the domains of chemistry, biology and pharmacology amongst others. This has led to a proliferation of databases and data sources relevant to drug discovery. This paper provides a review of the publicly-available large-scale databases relevant to drug discovery, describes the kinds of data mining approaches that can be applied to them and discusses recent work in integrative data mining that looks for associations that pan multiple sources, including the use of Semantic Web techniques. The future of mining large data sets for drug discovery requires intelligent, semantic aggregation of information from all of the data sources described in this review, along with the application of advanced methods such as intelligent agents and inference engines in client applications.

Subliminal number priming within and across the visual and auditory modalities.

PubMed

Kouider, Sid; Dehaene, Stanislas

2009-01-01

Whether masked number priming involves a low-level sensorimotor route or an amodal semantic level of processing remains highly debated. Several alternative interpretations have been put forward, proposing either that masked number priming is solely a byproduct of practice with numbers, or that stimulus awareness was underestimated. In a series of four experiments, we studied whether repetition and congruity priming for numbers reliably extend to novel (i.e., unpracticed) stimuli and whether priming transfers from a visual prime to an auditory target, even when carefully controlling for stimulus awareness. While we consistently observed cross-modal priming, the generalization to novel stimuli was weaker and reached significance only when considering the whole set of experiments. We conclude that number priming does involve an amodal, semantic level of processing, but is also modulated by task settings.

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.

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.