Internally- and externally-driven network transitions as a basis for automatic and strategic processes in semantic priming: theory and experimental validation

PubMed Central

Lerner, Itamar; Shriki, Oren

2014-01-01

For the last four decades, semantic priming—the facilitation in recognition of a target word when it follows the presentation of a semantically related prime word—has been a central topic in research of human cognitive processing. Studies have drawn a complex picture of findings which demonstrated the sensitivity of this priming effect to a unique combination of variables, including, but not limited to, the type of relatedness between primes and targets, the prime-target Stimulus Onset Asynchrony (SOA), the relatedness proportion (RP) in the stimuli list and the specific task subjects are required to perform. Automatic processes depending on the activation patterns of semantic representations in memory and controlled strategies adapted by individuals when attempting to maximize their recognition performance have both been implicated in contributing to the results. Lately, we have published a new model of semantic priming that addresses the majority of these findings within one conceptual framework. In our model, semantic memory is depicted as an attractor neural network in which stochastic transitions from one stored pattern to another are continually taking place due to synaptic depression mechanisms. We have shown how such transitions, in combination with a reinforcement-learning rule that adjusts their pace, resemble the classic automatic and controlled processes involved in semantic priming and account for a great number of the findings in the literature. Here, we review the core findings of our model and present new simulations that show how similar principles of parameter-adjustments could account for additional data not addressed in our previous studies, such as the relation between expectancy and inhibition in priming, target frequency and target degradation effects. Finally, we describe two human experiments that validate several key predictions of the model. PMID:24795670

It Matters How Much You Talk: On the Automaticity of Affective Connotations of First and Second Language Words

ERIC Educational Resources Information Center

Degner, Juliane; Doycheva, Cveta; Wentura, Dirk

2012-01-01

We report the results of an affective priming study conducted with proficient sequential German and French bilinguals to assess automatic affective word processing in L1 and L2. Additionally, a semantic priming task was conducted in both languages. Whereas semantic priming effects occurred in L1 and L2, and significant affective priming effects…

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

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

PubMed

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

2009-06-01

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

Semantic Annotation of Computational Components

NASA Technical Reports Server (NTRS)

Vanderbilt, Peter; Mehrotra, Piyush

2004-01-01

This paper describes a methodology to specify machine-processable semantic descriptions of computational components to enable them to be shared and reused. A particular focus of this scheme is to enable automatic compositon of such components into simple work-flows.

On the interdependence of cognition and emotion

PubMed Central

Storbeck, Justin; Clore, Gerald L.

2008-01-01

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

Word Naming in the L1 and L2: A Dynamic Perspective on Automatization and the Degree of Semantic Involvement in Naming

PubMed Central

Plat, Rika; Lowie, Wander; de Bot, Kees

2018-01-01

Reaction time data have long been collected in order to gain insight into the underlying mechanisms involved in language processing. Means analyses often attempt to break down what factors relate to what portion of the total reaction time. From a dynamic systems theory perspective or an interaction dominant view of language processing, it is impossible to isolate discrete factors contributing to language processing, since these continually and interactively play a role. Non-linear analyses offer the tools to investigate the underlying process of language use in time, without having to isolate discrete factors. Patterns of variability in reaction time data may disclose the relative contribution of automatic (grapheme-to-phoneme conversion) processing and attention-demanding (semantic) processing. The presence of a fractal structure in the variability of a reaction time series indicates automaticity in the mental structures contributing to a task. A decorrelated pattern of variability will indicate a higher degree of attention-demanding processing. A focus on variability patterns allows us to examine the relative contribution of automatic and attention-demanding processing when a speaker is using the mother tongue (L1) or a second language (L2). A word naming task conducted in the L1 (Dutch) and L2 (English) shows L1 word processing to rely more on automatic spelling-to-sound conversion than L2 word processing. A word naming task with a semantic categorization subtask showed more reliance on attention-demanding semantic processing when using the L2. A comparison to L1 English data shows this was not only due to the amount of language use or language dominance, but also to the difference in orthographic depth between Dutch and English. An important implication of this finding is that when the same task is used to test and compare different languages, one cannot straightforwardly assume the same cognitive sub processes are involved to an equal degree using the same task in different languages. PMID:29403404

Contextual Modulation of N400 Amplitude to Lexically Ambiguous Words

ERIC Educational Resources Information Center

Titone, Debra A.; Salisbury, Dean F.

2004-01-01

Through much is known about the N400 component, an event-related EEG potential that is sensitive to semantic manipulations, it is unclear whether modulations of N400 amplitude reflect automatic processing, controlled processing, or both. We examined this issue using a semantic judgment task that manipulated local and global contextual cues. Word…

The spacing effect in intentional and incidental free recall by children and adults: Limits on the automaticity hypothesis.

PubMed

Toppino, Thomas C; Fearnow-Kenney, Melodie D; Kiepert, Marissa H; Teremula, Amanda C

2009-04-01

Preschoolers, elementary school children, and college students exhibited a spacing effect in the free recall of pictures when learning was intentional. When learning was incidental and a shallow processing task requiring little semantic processing was used during list presentation, young adults still exhibited a spacing effect, but children consistently failed to do so. Children, however, did manifest a spacing effect in incidental learning when an elaborate semantic processing task was used. These results limit the hypothesis that the spacing effect in free recall occurs automatically and constrain theoretical accounts of why the spacing between repetitions affects recall performance.

Electrophysiology reveals semantic priming at a short SOA irrespective of depth of prime processing.

PubMed

Küper, Kristina; Heil, Martin

2009-04-03

The otherwise robust behavioral semantic priming effect is reduced to the point of being absent when a letter search has to be performed on the prime word. As a result the automaticity of semantic activation has been called into question. It is unclear, however, in how far automatic processes are even measurable in the letter search priming paradigm as the prime task necessitates a long prime-probe stimulus-onset asynchrony (SOA). In a modified procedure, a short SOA can be realized by delaying the prime task response until after participants have made a lexical decision on the probe. While the absence of lexical decision priming has already been demonstrated in this design it seems premature to draw any definite conclusions from this purely behavioral result since event related potential (ERP) measures have been shown to be a more sensitive index of semantic activation. Using the modified paradigm we thus recorded ERP in addition to lexical decision times. Stimuli were presented at two different SOAs (240 ms vs. 840 ms) and participants performed either a grammatical discrimination (Experiment 1) or a letter search (Experiment 2) on the prime. Irrespective of prime task, the modulation of the N400, the ERP correlate of semantic activation, provided clear-cut evidence of semantic processing at the short SOA. Implications for theories of semantic activation as well as the constraints of the delayed prime task procedure are discussed.

Effect of normal aging and of Alzheimer's disease on, episodic memory.

PubMed

Le Moal, S; Reymann, J M; Thomas, V; Cattenoz, C; Lieury, A; Allain, H

1997-01-01

Performances of 12 patients with Alzheimer's disease (AD), 15 healthy elderly subjects and 20 young healthy volunteers were compared on two episodic memory tests. The first, a learning test of semantically related words, enabled an assessment of the effect of semantic relationships on word learning by controlling the encoding and retrieval processes. The second, a dual coding test, is about the assessment of automatic processes operating during drawings encoding. The results obtained demonstrated quantitative and qualitative differences between the population. Manifestations of episodic memory deficit in AD patients were shown not only by lower performance scores than in elderly controls, but also by the lack of any effect of semantic cues and the production of a large number of extra-list intrusions. Automatic processes underlying dual coding appear to be spared in AD, although more time is needed to process information than in young or elderly subjects. These findings confirm former data and emphasize the preservation of certain memory processes (dual coding) in AD which could be used in future therapeutic approaches.

Aging Influences the Neural Correlates of Lexical Decision but Not Automatic Semantic Priming

PubMed Central

Andersen, Anders H.; Jicha, Greg A.; Smith, Charles D.

2009-01-01

Human behavioral data indicate that older adults are slower to perform lexical decisions (LDs) than young adults but show similar reaction time gains when these decisions are primed semantically. The present study explored the functional neuroanatomic bases of these frequently observed behavioral findings. Young and older groups completed unprimed and primed LD tasks while functional magnetic resonance imaging (fMRI) was recorded, using a fully randomized trial design paralleling those used in behavioral research. Results from the unprimed task found that age-related slowing of LD was associated with decreased activation in perceptual extrastriate regions and increased activation in regions associated with higher level linguistic processes, including prefrontal cortex. In contrast to these age-related changes in brain activation, the older group showed a preserved pattern of fMRI decreases in inferior temporal cortex when LD was primed semantically. These findings provide evidence that older adults’ LD abilities benefit from contexts that reduce the need for frontally mediated strategic processes and capitalize on the continued sensitivity of inferior temporal cortex to automatic semantic processes in aging. PMID:19273460

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

PubMed

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

2018-01-01

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

Semantic Processing Impairment in Patients with Temporal Lobe Epilepsy

PubMed Central

Jaimes-Bautista, Amanda G.; Rodríguez-Camacho, Mario; Martínez-Juárez, Iris E.; Rodríguez-Agudelo, Yaneth

2015-01-01

The impairment in episodic memory system is the best-known cognitive deficit in patients with temporal lobe epilepsy (TLE). Recent studies have shown evidence of semantic disorders, but they have been less studied than episodic memory. The semantic dysfunction in TLE has various cognitive manifestations, such as the presence of language disorders characterized by defects in naming, verbal fluency, or remote semantic information retrieval, which affects the ability of patients to interact with their surroundings. This paper is a review of recent research about the consequences of TLE on semantic processing, considering neuropsychological, electrophysiological, and neuroimaging findings, as well as the functional role of the hippocampus in semantic processing. The evidence from these studies shows disturbance of semantic memory in patients with TLE and supports the theory of declarative memory of the hippocampus. Functional neuroimaging studies show an inefficient compensatory functional reorganization of semantic networks and electrophysiological studies show a lack of N400 effect that could indicate that the deficit in semantic processing in patients with TLE could be due to a failure in the mechanisms of automatic access to lexicon. PMID:26257956

The Influence of Working Memory Load on Semantic Priming

ERIC Educational Resources Information Center

Heyman, Tom; Van Rensbergen, Bram; Storms, Gert; Hutchison, Keith A.; De Deyne, Simon

2015-01-01

The present research examines the nature of the different processes that have been proposed to underlie semantic priming. Specifically, it has been argued that priming arises as a result of "automatic target activation" and/or the use of strategies like prospective "expectancy generation" and "retrospective semantic…

Are Automatic Conceptual Cores the Gold Standard of Semantic Processing? The Context-Dependence of Spatial Meaning in Grounded Congruency Effects

ERIC Educational Resources Information Center

Lebois, Lauren A. M.; Wilson-Mendenhall, Christine D.; Barsalou, Lawrence W.

2015-01-01

According to grounded cognition, words whose semantics contain sensory-motor features activate sensory-motor simulations, which, in turn, interact with spatial responses to produce grounded congruency effects (e.g., processing the spatial feature of "up" for sky should be faster for up vs. down responses). Growing evidence shows these…

Processing of Crawled Urban Imagery for Building Use Classification

NASA Astrophysics Data System (ADS)

Tutzauer, P.; Haala, N.

2017-05-01

Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

A novel architecture for information retrieval system based on semantic web

NASA Astrophysics Data System (ADS)

Zhang, Hui

2011-12-01

Nowadays, the web has enabled an explosive growth of information sharing (there are currently over 4 billion pages covering most areas of human endeavor) so that the web has faced a new challenge of information overhead. The challenge that is now before us is not only to help people locating relevant information precisely but also to access and aggregate a variety of information from different resources automatically. Current web document are in human-oriented formats and they are suitable for the presentation, but machines cannot understand the meaning of document. To address this issue, Berners-Lee proposed a concept of semantic web. With semantic web technology, web information can be understood and processed by machine. It provides new possibilities for automatic web information processing. A main problem of semantic web information retrieval is that when these is not enough knowledge to such information retrieval system, the system will return to a large of no sense result to uses due to a huge amount of information results. In this paper, we present the architecture of information based on semantic web. In addiction, our systems employ the inference Engine to check whether the query should pose to Keyword-based Search Engine or should pose to the Semantic Search Engine.

Application of Semantic Tagging to Generate Superimposed Information on a Digital Encyclopedia

NASA Astrophysics Data System (ADS)

Garrido, Piedad; Tramullas, Jesus; Martinez, Francisco J.

We can find in the literature several works regarding the automatic or semi-automatic processing of textual documents with historic information using free software technologies. However, more research work is needed to integrate the analysis of the context and provide coverage to the peculiarities of the Spanish language from a semantic point of view. This research work proposes a novel knowledge-based strategy based on combining subject-centric computing, a topic-oriented approach, and superimposed information. It subsequent combination with artificial intelligence techniques led to an automatic analysis after implementing a made-to-measure interpreted algorithm which, in turn, produced a good number of associations and events with 90% reliability.

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

PubMed

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

2012-10-01

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

Individual differences in automatic semantic priming.

PubMed

Andrews, Sally; Lo, Steson; Xia, Violet

2017-05-01

This research investigated whether masked semantic priming in a semantic categorization task that required classification of words as animals or nonanimals was modulated by individual differences in lexical proficiency. A sample of 89 skilled readers, assessed on reading comprehension, vocabulary and spelling ability, classified target words preceded by brief (50 ms) masked primes that were either congruent or incongruent with the category of the target. Congruent primes were also selected to be either high (e.g., hawk EAGLE, pistol RIFLE) or low (e.g., mole EAGLE, boots RIFLE) in semantic feature overlap with the target. "Overall proficiency," indexed by high performance on both a "semantic composite" measure of reading comprehension and vocabulary and a "spelling composite," was associated with stronger congruence priming from both high and low feature overlap primes for animal exemplars, but only predicted priming from low overlap primes for nonexemplars. Classification of high frequency nonexemplars was also significantly modulated by an independent "spelling-meaning" factor, indexed by the discrepancy between the semantic and spelling composites, because relatively higher scores on the semantic than the spelling composite were associated with stronger semantic priming. These findings show that higher lexical proficiency is associated with stronger evidence of automatic semantic priming and suggest that individual differences in lexical quality modulate the division of labor between orthographic and semantic processing in early lexical retrieval. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The Influence of Intention on Masked Priming: A Study with Semantic Classification of Words

ERIC Educational Resources Information Center

Eckstein, Doris; Perrig, Walter J.

2007-01-01

Unconscious perception is commonly described as a phenomenon that is not under intentional control and relies on automatic processes. We challenge this view by arguing that some automatic processes may indeed be under intentional control, which is implemented in task-sets that define how the task is to be performed. In consequence, those prime…

Automatic event recognition and anomaly detection with attribute grammar by learning scene semantics

NASA Astrophysics Data System (ADS)

Qi, Lin; Yao, Zhenyu; Li, Li; Dong, Junyu

2007-11-01

In this paper we present a novel framework for automatic event recognition and abnormal behavior detection with attribute grammar by learning scene semantics. This framework combines learning scene semantics by trajectory analysis and constructing attribute grammar-based event representation. The scene and event information is learned automatically. Abnormal behaviors that disobey scene semantics or event grammars rules are detected. By this method, an approach to understanding video scenes is achieved. Further more, with this prior knowledge, the accuracy of abnormal event detection is increased.

A Software Engineering Approach based on WebML and BPMN to the Mediation Scenario of the SWS Challenge

NASA Astrophysics Data System (ADS)

Brambilla, Marco; Ceri, Stefano; Valle, Emanuele Della; Facca, Federico M.; Tziviskou, Christina

Although Semantic Web Services are expected to produce a revolution in the development of Web-based systems, very few enterprise-wide design experiences are available; one of the main reasons is the lack of sound Software Engineering methods and tools for the deployment of Semantic Web applications. In this chapter, we present an approach to software development for the Semantic Web based on classical Software Engineering methods (i.e., formal business process development, computer-aided and component-based software design, and automatic code generation) and on semantic methods and tools (i.e., ontology engineering, semantic service annotation and discovery).

Automatic Short Essay Scoring Using Natural Language Processing to Extract Semantic Information in the Form of Propositions. CRESST Report 831

ERIC Educational Resources Information Center

Kerr, Deirdre; Mousavi, Hamid; Iseli, Markus R.

2013-01-01

The Common Core assessments emphasize short essay constructed-response items over multiple-choice items because they are more precise measures of understanding. However, such items are too costly and time consuming to be used in national assessments unless a way to score them automatically can be found. Current automatic essay-scoring techniques…

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Research in interactive scene analysis

NASA Technical Reports Server (NTRS)

Tenenbaum, J. M.; Garvey, T. D.; Weyl, S. A.; Wolf, H. C.

1975-01-01

An interactive scene interpretation system (ISIS) was developed as a tool for constructing and experimenting with man-machine and automatic scene analysis methods tailored for particular image domains. A recently developed region analysis subsystem based on the paradigm of Brice and Fennema is described. Using this subsystem a series of experiments was conducted to determine good criteria for initially partitioning a scene into atomic regions and for merging these regions into a final partition of the scene along object boundaries. Semantic (problem-dependent) knowledge is essential for complete, correct partitions of complex real-world scenes. An interactive approach to semantic scene segmentation was developed and demonstrated on both landscape and indoor scenes. This approach provides a reasonable methodology for segmenting scenes that cannot be processed completely automatically, and is a promising basis for a future automatic system. A program is described that can automatically generate strategies for finding specific objects in a scene based on manually designated pictorial examples.

A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications

PubMed Central

Cameron, Delroy; Bodenreider, Olivier; Yalamanchili, Hima; Danh, Tu; Vallabhaneni, Sreeram; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Rindflesch, Thomas C.

2014-01-01

Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil Hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson has been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson has been developed. Results Our methodology not only recovered the 3 associations commonly recognized as Swanson’s Hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s Hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology for semi- automatically recovering and decomposing Swanson’s RS-DFO Hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). These suggest that three critical aspects of LBD include: 1) the need for more expressive representations beyond Swanson’s ABC model; 2) an ability to accurately extract semantic information from text; and 3) the semantic integration of scientific literature with structured background knowledge. PMID:23026233

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

PubMed

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

2013-04-15

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

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

PubMed Central

2013-01-01

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

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

PubMed

Arch-Int, Ngamnij; Arch-Int, Somjit; Sonsilphong, Suphachoke; Wanchai, Paweena

2017-01-01

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration

PubMed Central

2017-01-01

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement. PMID:29065602

Automatic Item Generation via Frame Semantics: Natural Language Generation of Math Word Problems.

ERIC Educational Resources Information Center

Deane, Paul; Sheehan, Kathleen

This paper is an exploration of the conceptual issues that have arisen in the course of building a natural language generation (NLG) system for automatic test item generation. While natural language processing techniques are applicable to general verbal items, mathematics word problems are particularly tractable targets for natural language…

Automatic Semantic Orientation of Adjectives for Indonesian Language Using PMI-IR and Clustering

NASA Astrophysics Data System (ADS)

Riyanti, Dewi; Arif Bijaksana, M.; Adiwijaya

2018-03-01

We present our work in the area of sentiment analysis for Indonesian language. We focus on bulding automatic semantic orientation using available resources in Indonesian. In this research we used Indonesian corpus that contains 9 million words from kompas.txt and tempo.txt that manually tagged and annotated with of part-of-speech tagset. And then we construct a dataset by taking all the adjectives from the corpus, removing the adjective with no orientation. The set contained 923 adjective words. This systems will include several steps such as text pre-processing and clustering. The text pre-processing aims to increase the accuracy. And finally clustering method will classify each word to related sentiment which is positive or negative. With improvements to the text preprocessing, can be achieved 72% of accuracy.

Automatic Processing of Emotional Words in the Absence of Awareness: The Critical Role of P2

PubMed Central

Lei, Yi; Dou, Haoran; Liu, Qingming; Zhang, Wenhai; Zhang, Zhonglu; Li, Hong

2017-01-01

It has been long debated to what extent emotional words can be processed in the absence of awareness. Behavioral studies have shown that the meaning of emotional words can be accessed even without any awareness. However, functional magnetic resonance imaging studies have revealed that emotional words that are unconsciously presented do not activate the brain regions involved in semantic or emotional processing. To clarify this point, we used continuous flash suppression (CFS) and event-related potential (ERP) techniques to distinguish between semantic and emotional processing. In CFS, we successively flashed some Mondrian-style images into one participant's eye steadily, which suppressed the images projected to the other eye. Negative, neutral, and scrambled words were presented to 16 healthy participants for 500 ms. Whenever the participants saw the stimuli—in both visible and invisible conditions—they pressed specific keyboard buttons. Behavioral data revealed that there was no difference in reaction time to negative words and to neutral words in the invisible condition, although negative words were processed faster than neutral words in the visible condition. The ERP results showed that negative words elicited a larger P2 amplitude in the invisible condition than in the visible condition. The P2 component was enhanced for the neutral words compared with the scrambled words in the visible condition; however, the scrambled words elicited larger P2 amplitudes than the neutral words in the invisible condition. These results suggest that the emotional processing of words is more sensitive than semantic processing in the conscious condition. Semantic processing was found to be attenuated in the absence of awareness. Our findings indicate that P2 plays an important role in the unconscious processing of emotional words, which highlights the fact that emotional processing may be automatic and prioritized compared with semantic processing in the absence of awareness. PMID:28473785

Automatic Processing of Emotional Words in the Absence of Awareness: The Critical Role of P2.

PubMed

Lei, Yi; Dou, Haoran; Liu, Qingming; Zhang, Wenhai; Zhang, Zhonglu; Li, Hong

2017-01-01

It has been long debated to what extent emotional words can be processed in the absence of awareness. Behavioral studies have shown that the meaning of emotional words can be accessed even without any awareness. However, functional magnetic resonance imaging studies have revealed that emotional words that are unconsciously presented do not activate the brain regions involved in semantic or emotional processing. To clarify this point, we used continuous flash suppression (CFS) and event-related potential (ERP) techniques to distinguish between semantic and emotional processing. In CFS, we successively flashed some Mondrian-style images into one participant's eye steadily, which suppressed the images projected to the other eye. Negative, neutral, and scrambled words were presented to 16 healthy participants for 500 ms. Whenever the participants saw the stimuli-in both visible and invisible conditions-they pressed specific keyboard buttons. Behavioral data revealed that there was no difference in reaction time to negative words and to neutral words in the invisible condition, although negative words were processed faster than neutral words in the visible condition. The ERP results showed that negative words elicited a larger P2 amplitude in the invisible condition than in the visible condition. The P2 component was enhanced for the neutral words compared with the scrambled words in the visible condition; however, the scrambled words elicited larger P2 amplitudes than the neutral words in the invisible condition. These results suggest that the emotional processing of words is more sensitive than semantic processing in the conscious condition. Semantic processing was found to be attenuated in the absence of awareness. Our findings indicate that P2 plays an important role in the unconscious processing of emotional words, which highlights the fact that emotional processing may be automatic and prioritized compared with semantic processing in the absence of awareness.

Hybrid Semantic Analysis for Mapping Adverse Drug Reaction Mentions in Tweets to Medical Terminology.

PubMed

Emadzadeh, Ehsan; Sarker, Abeed; Nikfarjam, Azadeh; Gonzalez, Graciela

2017-01-01

Social networks, such as Twitter, have become important sources for active monitoring of user-reported adverse drug reactions (ADRs). Automatic extraction of ADR information can be crucial for healthcare providers, drug manufacturers, and consumers. However, because of the non-standard nature of social media language, automatically extracted ADR mentions need to be mapped to standard forms before they can be used by operational pharmacovigilance systems. We propose a modular natural language processing pipeline for mapping (normalizing) colloquial mentions of ADRs to their corresponding standardized identifiers. We seek to accomplish this task and enable customization of the pipeline so that distinct unlabeled free text resources can be incorporated to use the system for other normalization tasks. Our approach, which we call Hybrid Semantic Analysis (HSA), sequentially employs rule-based and semantic matching algorithms for mapping user-generated mentions to concept IDs in the Unified Medical Language System vocabulary. The semantic matching component of HSA is adaptive in nature and uses a regression model to combine various measures of semantic relatedness and resources to optimize normalization performance on the selected data source. On a publicly available corpus, our normalization method achieves 0.502 recall and 0.823 precision (F-measure: 0.624). Our proposed method outperforms a baseline based on latent semantic analysis and another that uses MetaMap.

On the Learning of Distractors during Controlled and Automatic Processing.

DTIC Science & Technology

1980-02-04

function of semantic, graphic and syntactic orienting tasks. Journal of Verbal Learning and Verbal Behavior, 1973, 12, 471-480. LaBerge , D. Attention...and the measurement of perceptual learning. Memory and Cognition, 1973, 1, 268-278. LaBerge , D. Acquisition of automatic processing in perceptual and...Univ A. Stevens , Holt Beranek & Newman, Cambridge, 1A D. Stone, SUY, Albany P. Suppes, Stanford Uuiv H. Swaminathan, Univ of Massachusetts K. Tatsuoka

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.

Semantic Modelling of Digital Forensic Evidence

NASA Astrophysics Data System (ADS)

Kahvedžić, Damir; Kechadi, Tahar

The reporting of digital investigation results are traditionally carried out in prose and in a large investigation may require successive communication of findings between different parties. Popular forensic suites aid in the reporting process by storing provenance and positional data but do not automatically encode why the evidence is considered important. In this paper we introduce an evidence management methodology to encode the semantic information of evidence. A structured vocabulary of terms, ontology, is used to model the results in a logical and predefined manner. The descriptions are application independent and automatically organised. The encoded descriptions aim to help the investigation in the task of report writing and evidence communication and can be used in addition to existing evidence management techniques.

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.

The numerical distance effect is task dependent.

PubMed

Goldfarb, Liat; Henik, Avishai; Rubinsten, Orly; Bloch-David, Yafit; Gertner, Limor

2011-11-01

Number comparison tasks produce a distance effect e.g., Moyer & Landauer (Nature 215: 1519-1520, 1967). It has been suggested that this effect supports the existence of semantic mental representations of numbers. In a matching task, a distance effect also appears, which suggests that the effect has an automatic semantic component. Recently, Cohen (Psychonomic Bulletin & Review 16: 332-336, 2009) suggested that in both automatic and intentional tasks, the distance effect might reflect not a semantic number representation, but a physical similarity between digits. The present article (1) compares the distance effect in the automatic matching task with that in the intentional number comparison task and suggests that, in the latter, the distance effect does include an additional semantic component; and (2) indicates that the distance effect in the standard automatic matching task is questionable and that its appearance in previous matching tasks was based on the specific analysis and design that were applied.

Automatic textual annotation of video news based on semantic visual object extraction

NASA Astrophysics Data System (ADS)

Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

2003-12-01

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

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.

SemanticSCo: A platform to support the semantic composition of services for gene expression analysis.

PubMed

Guardia, Gabriela D A; Ferreira Pires, Luís; da Silva, Eduardo G; de Farias, Cléver R G

2017-02-01

Gene expression studies often require the combined use of a number of analysis tools. However, manual integration of analysis tools can be cumbersome and error prone. To support a higher level of automation in the integration process, efforts have been made in the biomedical domain towards the development of semantic web services and supporting composition environments. Yet, most environments consider only the execution of simple service behaviours and requires users to focus on technical details of the composition process. We propose a novel approach to the semantic composition of gene expression analysis services that addresses the shortcomings of the existing solutions. Our approach includes an architecture designed to support the service composition process for gene expression analysis, and a flexible strategy for the (semi) automatic composition of semantic web services. Finally, we implement a supporting platform called SemanticSCo to realize the proposed composition approach and demonstrate its functionality by successfully reproducing a microarray study documented in the literature. The SemanticSCo platform provides support for the composition of RESTful web services semantically annotated using SAWSDL. Our platform also supports the definition of constraints/conditions regarding the order in which service operations should be invoked, thus enabling the definition of complex service behaviours. Our proposed solution for semantic web service composition takes into account the requirements of different stakeholders and addresses all phases of the service composition process. It also provides support for the definition of analysis workflows at a high-level of abstraction, thus enabling users to focus on biological research issues rather than on the technical details of the composition process. The SemanticSCo source code is available at https://github.com/usplssb/SemanticSCo. Copyright © 2017 Elsevier Inc. All rights reserved.

Semi-automatic semantic annotation of PubMed Queries: a study on quality, efficiency, satisfaction

PubMed Central

Névéol, Aurélie; Islamaj-Doğan, Rezarta; Lu, Zhiyong

2010-01-01

Information processing algorithms require significant amounts of annotated data for training and testing. The availability of such data is often hindered by the complexity and high cost of production. In this paper, we investigate the benefits of a state-of-the-art tool to help with the semantic annotation of a large set of biomedical information queries. Seven annotators were recruited to annotate a set of 10,000 PubMed® queries with 16 biomedical and bibliographic categories. About half of the queries were annotated from scratch, while the other half were automatically pre-annotated and manually corrected. The impact of the automatic pre-annotations was assessed on several aspects of the task: time, number of actions, annotator satisfaction, inter-annotator agreement, quality and number of the resulting annotations. The analysis of annotation results showed that the number of required hand annotations is 28.9% less when using pre-annotated results from automatic tools. As a result, the overall annotation time was substantially lower when pre-annotations were used, while inter-annotator agreement was significantly higher. In addition, there was no statistically significant difference in the semantic distribution or number of annotations produced when pre-annotations were used. The annotated query corpus is freely available to the research community. This study shows that automatic pre-annotations are found helpful by most annotators. Our experience suggests using an automatic tool to assist large-scale manual annotation projects. This helps speed-up the annotation time and improve annotation consistency while maintaining high quality of the final annotations. PMID:21094696

Linguistically informed digital fingerprints for text

NASA Astrophysics Data System (ADS)

Uzuner, Özlem

2006-02-01

Digital fingerprinting, watermarking, and tracking technologies have gained importance in the recent years in response to growing problems such as digital copyright infringement. While fingerprints and watermarks can be generated in many different ways, use of natural language processing for these purposes has so far been limited. Measuring similarity of literary works for automatic copyright infringement detection requires identifying and comparing creative expression of content in documents. In this paper, we present a linguistic approach to automatically fingerprinting novels based on their expression of content. We use natural language processing techniques to generate "expression fingerprints". These fingerprints consist of both syntactic and semantic elements of language, i.e., syntactic and semantic elements of expression. Our experiments indicate that syntactic and semantic elements of expression enable accurate identification of novels and their paraphrases, providing a significant improvement over techniques used in text classification literature for automatic copy recognition. We show that these elements of expression can be used to fingerprint, label, or watermark works; they represent features that are essential to the character of works and that remain fairly consistent in the works even when works are paraphrased. These features can be directly extracted from the contents of the works on demand and can be used to recognize works that would not be correctly identified either in the absence of pre-existing labels or by verbatim-copy detectors.

Exploiting semantics for sensor re-calibration in event detection systems

NASA Astrophysics Data System (ADS)

Vaisenberg, Ronen; Ji, Shengyue; Hore, Bijit; Mehrotra, Sharad; Venkatasubramanian, Nalini

2008-01-01

Event detection from a video stream is becoming an important and challenging task in surveillance and sentient systems. While computer vision has been extensively studied to solve different kinds of detection problems over time, it is still a hard problem and even in a controlled environment only simple events can be detected with a high degree of accuracy. Instead of struggling to improve event detection using image processing only, we bring in semantics to direct traditional image processing. Semantics are the underlying facts that hide beneath video frames, which can not be "seen" directly by image processing. In this work we demonstrate that time sequence semantics can be exploited to guide unsupervised re-calibration of the event detection system. We present an instantiation of our ideas by using an appliance as an example--Coffee Pot level detection based on video data--to show that semantics can guide the re-calibration of the detection model. This work exploits time sequence semantics to detect when re-calibration is required to automatically relearn a new detection model for the newly evolved system state and to resume monitoring with a higher rate of accuracy.

Stuck on semantics: Processing of irrelevant object-scene inconsistencies modulates ongoing gaze behavior.

PubMed

Cornelissen, Tim H W; Võ, Melissa L-H

2017-01-01

People have an amazing ability to identify objects and scenes with only a glimpse. How automatic is this scene and object identification? Are scene and object semantics-let alone their semantic congruity-processed to a degree that modulates ongoing gaze behavior even if they are irrelevant to the task at hand? Objects that do not fit the semantics of the scene (e.g., a toothbrush in an office) are typically fixated longer and more often than objects that are congruent with the scene context. In this study, we overlaid a letter T onto photographs of indoor scenes and instructed participants to search for it. Some of these background images contained scene-incongruent objects. Despite their lack of relevance to the search, we found that participants spent more time in total looking at semantically incongruent compared to congruent objects in the same position of the scene. Subsequent tests of explicit and implicit memory showed that participants did not remember many of the inconsistent objects and no more of the consistent objects. We argue that when we view natural environments, scene and object relationships are processed obligatorily, such that irrelevant semantic mismatches between scene and object identity can modulate ongoing eye-movement behavior.

Tracking lexical consolidation with ERPs: Lexical and semantic-priming effects on N400 and LPC responses to newly-learned words.

PubMed

Bakker, Iske; Takashima, Atsuko; van Hell, Janet G; Janzen, Gabriele; McQueen, James M

2015-12-01

Novel words can be recalled immediately and after little exposure, but require a post-learning consolidation period to show word-like behaviour such as lexical competition. This pattern is thought to reflect a qualitative shift from episodic to lexical representations. However, several studies have reported immediate effects of meaningful novel words on semantic processing, suggesting that integration of novel word meanings may not require consolidation. The current study synthesises and extends these findings by showing a dissociation between lexical and semantic effects on the electrophysiological (N400, LPC) response to novel words. The difference in N400 amplitude between novel and existing words (a lexical effect) decreased significantly after a 24-h consolidation period, providing novel support for the hypothesis that offline consolidation aids lexicalisation. In contrast, novel words preceded by semantically related primes elicited a more positive LPC response (a semantic-priming effect) both before and after consolidation, indicating that certain semantic effects can be observed even when words have not been fully lexicalised. We propose that novel meanings immediately start to contribute to semantic processing, but that the underlying neural processes may shift from strategic to more automatic with consolidation. Copyright © 2015 Elsevier Ltd. All rights reserved.

LinkEHR-Ed: a multi-reference model archetype editor based on formal semantics.

PubMed

Maldonado, José A; Moner, David; Boscá, Diego; Fernández-Breis, Jesualdo T; Angulo, Carlos; Robles, Montserrat

2009-08-01

To develop a powerful archetype editing framework capable of handling multiple reference models and oriented towards the semantic description and standardization of legacy data. The main prerequisite for implementing tools providing enhanced support for archetypes is the clear specification of archetype semantics. We propose a formalization of the definition section of archetypes based on types over tree-structured data. It covers the specialization of archetypes, the relationship between reference models and archetypes and conformance of data instances to archetypes. LinkEHR-Ed, a visual archetype editor based on the former formalization with advanced processing capabilities that supports multiple reference models, the editing and semantic validation of archetypes, the specification of mappings to data sources, and the automatic generation of data transformation scripts, is developed. LinkEHR-Ed is a useful tool for building, processing and validating archetypes based on any reference model.

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

PubMed

Soshi, Takahiro; Nakajima, Heizo; Hagiwara, Hiroko

2016-10-01

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

Linking consistency with object/thread semantics - An approach to robust computation

NASA Technical Reports Server (NTRS)

Chen, Raymond C.; Dasgupta, Partha

1989-01-01

This paper presents an object/thread based paradigm that links data consistency with object/thread semantics. The paradigm can be used to achieve a wide range of consistency semantics from strict atomic transactions to standard process semantics. The paradigm supports three types of data consistency. Object programmers indicate the type of consistency desired on a per-operation basis and the system performs automatic concurrency control and recovery management to ensure that those consistency requirements are met. This allows programmers to customize consistency and recovery on a per-application basis without having to supply complicated, custom recovery management schemes. The paradigm allows robust and nonrobust computation to operate concurrently on the same data in a well defined manner. The operating system needs to support only one vehicle of computation - the thread.

Semantic Analysis of Email Using Domain Ontologies and WordNet

NASA Technical Reports Server (NTRS)

Berrios, Daniel C.; Keller, Richard M.

2005-01-01

The problem of capturing and accessing knowledge in paper form has been supplanted by a problem of providing structure to vast amounts of electronic information. Systems that can construct semantic links for natural language documents like email messages automatically will be a crucial element of semantic email tools. We have designed an information extraction process that can leverage the knowledge already contained in an existing semantic web, recognizing references in email to existing nodes in a network of ontology instances by using linguistic knowledge and knowledge of the structure of the semantic web. We developed a heuristic score that uses several forms of evidence to detect references in email to existing nodes in the Semanticorganizer repository's network. While these scores cannot directly support automated probabilistic inference, they can be used to rank nodes by relevance and link those deemed most relevant to email messages.

Tools for a Document Image Utility.

ERIC Educational Resources Information Center

Krishnamoorthy, M.; And Others

1993-01-01

Describes a project conducted at Rensselaer Polytechnic Institute (New York) that developed methods for automatically subdividing pages from technical journals into smaller semantic units for transmission, display, and further processing in an electronic environment. Topics discussed include optical scanning and image compression, digital image…

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.

Left ventral occipitotemporal activation during orthographic and semantic processing of auditory words.

PubMed

Ludersdorfer, Philipp; Wimmer, Heinz; Richlan, Fabio; Schurz, Matthias; Hutzler, Florian; Kronbichler, Martin

2016-01-01

The present fMRI study investigated the hypothesis that activation of the left ventral occipitotemporal cortex (vOT) in response to auditory words can be attributed to lexical orthographic rather than lexico-semantic processing. To this end, we presented auditory words in both an orthographic ("three or four letter word?") and a semantic ("living or nonliving?") task. In addition, a auditory control condition presented tones in a pitch evaluation task. The results showed that the left vOT exhibited higher activation for orthographic relative to semantic processing of auditory words with a peak in the posterior part of vOT. Comparisons to the auditory control condition revealed that orthographic processing of auditory words elicited activation in a large vOT cluster. In contrast, activation for semantic processing was only weak and restricted to the middle part vOT. We interpret our findings as speaking for orthographic processing in left vOT. In particular, we suggest that activation in left middle vOT can be attributed to accessing orthographic whole-word representations. While activation of such representations was experimentally ascertained in the orthographic task, it might have also occurred automatically in the semantic task. Activation in the more posterior vOT region, on the other hand, may reflect the generation of explicit images of word-specific letter sequences required by the orthographic but not the semantic task. In addition, based on cross-modal suppression, the finding of marked deactivations in response to the auditory tones is taken to reflect the visual nature of representations and processes in left vOT. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Unconscious automatic brain activation of acoustic and action-related conceptual features during masked repetition priming.

PubMed

Trumpp, Natalie M; Traub, Felix; Pulvermüller, Friedemann; Kiefer, Markus

2014-02-01

Classical theories of semantic memory assume that concepts are represented in a unitary amodal memory system. In challenging this classical view, pure or hybrid modality-specific theories propose that conceptual representations are grounded in the sensory-motor brain areas, which typically process sensory and action-related information. Although neuroimaging studies provided evidence for a functional-anatomical link between conceptual processing of sensory or action-related features and the sensory-motor brain systems, it has been argued that aspects of such sensory-motor activation may not directly reflect conceptual processing but rather strategic imagery or postconceptual elaboration. In the present ERP study, we investigated masked effects of acoustic and action-related conceptual features to probe unconscious automatic conceptual processing in isolation. Subliminal feature-specific ERP effects at frontocentral electrodes were observed, which differed with regard to polarity, topography, and underlying brain electrical sources in congruency with earlier findings under conscious viewing conditions. These findings suggest that conceptual acoustic and action representations can also be unconsciously accessed, thereby excluding any postconceptual strategic processes. This study therefore further substantiates a grounding of conceptual and semantic processing in action and perception.

Synonym extraction and abbreviation expansion with ensembles of semantic spaces.

PubMed

Henriksson, Aron; Moen, Hans; Skeppstedt, Maria; Daudaravičius, Vidas; Duneld, Martin

2014-02-05

Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. A combination of two distributional models - Random Indexing and Random Permutation - employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora - a corpus of clinical text and a corpus of medical journal articles - further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. This study demonstrates that ensembles of semantic spaces can yield improved performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. This notion, which merits further exploration, allows different distributional models - with different model parameters - and different types of corpora to be combined, potentially allowing enhanced performance to be obtained on a wide range of natural language processing tasks.

Synonym extraction and abbreviation expansion with ensembles of semantic spaces

PubMed Central

2014-01-01

Background Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. Results A combination of two distributional models – Random Indexing and Random Permutation – employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora – a corpus of clinical text and a corpus of medical journal articles – further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. Conclusions This study demonstrates that ensembles of semantic spaces can yield improved performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. This notion, which merits further exploration, allows different distributional models – with different model parameters – and different types of corpora to be combined, potentially allowing enhanced performance to be obtained on a wide range of natural language processing tasks. PMID:24499679

Automatic processing of spoken dialogue in the home hemodialysis domain.

PubMed

Lacson, Ronilda; Barzilay, Regina

2005-01-01

Spoken medical dialogue is a valuable source of information, and it forms a foundation for diagnosis, prevention and therapeutic management. However, understanding even a perfect transcript of spoken dialogue is challenging for humans because of the lack of structure and the verbosity of dialogues. This work presents a first step towards automatic analysis of spoken medical dialogue. The backbone of our approach is an abstraction of a dialogue into a sequence of semantic categories. This abstraction uncovers structure in informal, verbose conversation between a caregiver and a patient, thereby facilitating automatic processing of dialogue content. Our method induces this structure based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). This work demonstrates the feasibility of automatically processing spoken medical dialogue.

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

PubMed

Arguello Casteleiro, Mercedes; Maseda Fernandez, Diego; Demetriou, George; Read, Warren; Fernandez Prieto, Maria Jesus; Des Diz, Julio; Nenadic, Goran; Keane, John; Stevens, Robert

2017-01-01

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.

Centre-surround inhibition is a general aspect of famous-person recognition: evidence from negative semantic priming from clearly visible primes.

PubMed

Stone, Anna

2012-05-01

A centre-surround attentional mechanism was proposed by Carr and Dagenbach (Journal of Experimental Psychology: Learning, Memory, and Cognition 16: 341-350, 1990) to account for their observations of negative semantic priming from hard-to-perceive primes. Their mechanism cannot account for the observation of negative semantic priming when primes are clearly visible. Three experiments (Ns = 30, 46, and 30) used a familiarity decision with names of famous people, preceded by a prime name with the same occupation as the target or with a different occupation. Negative semantic priming was observed at a 150- or 200-ms SOA, with positive priming at shorter (50-ms) and longer (1,000-ms) SOAs. In Experiment 3, we verified that the primes were easily recognisable in the priming task at an SOA that yielded negative semantic priming, which cannot be predicted by the original centre-surround mechanism. A modified version is proposed that explains transiently negative semantic priming by proposing that centre-surround inhibition is a normal, automatically invoked aspect of the semantic processing of visually presented famous names.

Modulation of Automatic Semantic Priming by Feature-Specific Attention Allocation

ERIC Educational Resources Information Center

Spruyt, Adriaan; De Houwer, Jan; Hermans, Dirk

2009-01-01

We argue that the semantic analysis of task-irrelevant stimuli is modulated by feature-specific attention allocation. In line with this hypothesis, we found semantic priming of pronunciation responses to depend upon the extent to which participants focused their attention upon specific semantic stimulus dimensions. In Experiment 1, we examined the…

Automatic semantic encoding in verbal short-term memory: evidence from the concreteness effect.

PubMed

Campoy, Guillermo; Castellà, Judit; Provencio, Violeta; Hitch, Graham J; Baddeley, Alan D

2015-01-01

The concreteness effect in verbal short-term memory (STM) tasks is assumed to be a consequence of semantic encoding in STM, with immediate recall of concrete words benefiting from richer semantic representations. We used the concreteness effect to test the hypothesis that semantic encoding in standard verbal STM tasks is a consequence of controlled, attention-demanding mechanisms of strategic semantic retrieval and encoding. Experiment 1 analysed the effect of presentation rate, with slow presentations being assumed to benefit strategic, time-dependent semantic encoding. Experiments 2 and 3 provided a more direct test of the strategic hypothesis by introducing three different concurrent attention-demanding tasks. Although Experiment 1 showed a larger concreteness effect with slow presentations, the following two experiments yielded strong evidence against the strategic hypothesis. Limiting available attention resources by concurrent tasks reduced global memory performance, but the concreteness effect was equivalent to that found in control conditions. We conclude that semantic effects in STM result from automatic semantic encoding and provide tentative explanations for the interaction between the concreteness effect and the presentation rate.

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…

Utilizing Linked Open Data Sources for Automatic Generation of Semantic Metadata

NASA Astrophysics Data System (ADS)

Nummiaho, Antti; Vainikainen, Sari; Melin, Magnus

In this paper we present an application that can be used to automatically generate semantic metadata for tags given as simple keywords. The application that we have implemented in Java programming language creates the semantic metadata by linking the tags to concepts in different semantic knowledge bases (CrunchBase, DBpedia, Freebase, KOKO, Opencyc, Umbel and/or WordNet). The steps that our application takes in doing so include detecting possible languages, finding spelling suggestions and finding meanings from amongst the proper nouns and common nouns separately. Currently, our application supports English, Finnish and Swedish words, but other languages could be included easily if the required lexical tools (spellcheckers, etc.) are available. The created semantic metadata can be of great use in, e.g., finding and combining similar contents, creating recommendations and targeting advertisements.

CASAS: A tool for composing automatically and semantically astrophysical services

NASA Astrophysics Data System (ADS)

Louge, T.; Karray, M. H.; Archimède, B.; Knödlseder, J.

2017-07-01

Multiple astronomical datasets are available through internet and the astrophysical Distributed Computing Infrastructure (DCI) called Virtual Observatory (VO). Some scientific workflow technologies exist for retrieving and combining data from those sources. However selection of relevant services, automation of the workflows composition and the lack of user-friendly platforms remain a concern. This paper presents CASAS, a tool for semantic web services composition in astrophysics. This tool proposes automatic composition of astrophysical web services and brings a semantics-based, automatic composition of workflows. It widens the services choice and eases the use of heterogeneous services. Semantic web services composition relies on ontologies for elaborating the services composition; this work is based on Astrophysical Services ONtology (ASON). ASON had its structure mostly inherited from the VO services capacities. Nevertheless, our approach is not limited to the VO and brings VO plus non-VO services together without the need for premade recipes. CASAS is available for use through a simple web interface.

Ontology Alignment Architecture for Semantic Sensor Web Integration

PubMed Central

Fernandez, Susel; Marsa-Maestre, Ivan; Velasco, Juan R.; Alarcos, Bernardo

2013-01-01

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall. PMID:24051523

Ontology alignment architecture for semantic sensor Web integration.

PubMed

Fernandez, Susel; Marsa-Maestre, Ivan; Velasco, Juan R; Alarcos, Bernardo

2013-09-18

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall.

English Complex Verb Constructions: Identification and Inference

ERIC Educational Resources Information Center

Tu, Yuancheng

2012-01-01

The fundamental problem faced by automatic text understanding in Natural Language Processing (NLP) is to identify semantically related pieces of text and integrate them together to compute the meaning of the whole text. However, the principle of compositionality runs into trouble very quickly when real language is examined with its frequent…

Dynamic generation of a table of contents with consumer-friendly labels.

PubMed

Miller, Trudi; Leroy, Gondy; Wood, Elizabeth

2006-01-01

Consumers increasingly look to the Internet for health information, but available resources are too difficult for the majority to understand. Interactive tables of contents (TOC) can help consumers access health information by providing an easy to understand structure. Using natural language processing and the Unified Medical Language System (UMLS), we have automatically generated TOCs for consumer health information. The TOC are categorized according to consumer-friendly labels for the UMLS semantic types and semantic groups. Categorizing phrases by semantic types is significantly more correct and relevant. Greater correctness and relevance was achieved with documents that are difficult to read than those at an easier reading level. Pruning TOCs to use categories that consumers favor further increases relevancy and correctness while reducing structural complexity.

Semantics-enabled service discovery framework in the SIMDAT pharma grid.

PubMed

Qu, Cangtao; Zimmermann, Falk; Kumpf, Kai; Kamuzinzi, Richard; Ledent, Valérie; Herzog, Robert

2008-03-01

We present the design and implementation of a semantics-enabled service discovery framework in the data Grids for process and product development using numerical simulation and knowledge discovery (SIMDAT) Pharma Grid, an industry-oriented Grid environment for integrating thousands of Grid-enabled biological data services and analysis services. The framework consists of three major components: the Web ontology language (OWL)-description logic (DL)-based biological domain ontology, OWL Web service ontology (OWL-S)-based service annotation, and semantic matchmaker based on the ontology reasoning. Built upon the framework, workflow technologies are extensively exploited in the SIMDAT to assist biologists in (semi)automatically performing in silico experiments. We present a typical usage scenario through the case study of a biological workflow: IXodus.

Common and Segregated Neural Substrates for Automatic Conceptual and Affective Priming as Revealed by Event-Related Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Liu, Hongyan; Hu, Zhiguo; Peng, Danling; Yang, Yanhui; Li, Kuncheng

2010-01-01

The brain activity associated with automatic semantic priming has been extensively studied. Thus far there has been no prior study that directly contrasts the neural mechanisms of semantic and affective priming. The present study employed event-related fMRI to examine the common and distinct neural bases underlying conceptual and affective priming…

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

Associative and semantic priming effects occur at very short stimulus-onset asynchronies in lexical decision and naming.

PubMed

Perea, M; Gotor, A

1997-02-01

Prior research has found significant associative/semantic priming effects at very short stimulus-onset asynchronies (SOAs) in experimental tasks such as lexical decision, but not in naming tasks (however, see Lukatela and Turvey, 1994). In this paper, the time course of associative priming effects was analyzed a several very short SOAs (33, 50, and 67 ms), using the masked priming paradigm (Forster and Davis, 1984), both in lexical decision (Experiment 1) and naming (Experiment 2). The results show small--but significant--associative priming effects in both tasks. Additionally, using the masked priming procedure at the 67 ms SOA. Experiments 3 and 4, shows facilitatory priming effects for both associatively and semantically (unassociated) related pairs in lexical decision and naming tasks. That is, automatic priming can be semantic. Taken together our data appear to support interactive models of word recognition in which semantic activation may influence the early stages of word processing.

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.

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.

Coupling a regional warning system to a semantic engine on online news for enhancing landslide prediction

NASA Astrophysics Data System (ADS)

Battistini, Alessandro; Rosi, Ascanio; Segoni, Samuele; Catani, Filippo; Casagli, Nicola

2017-04-01

Landslide inventories are basic data for large scale landslide modelling, e.g. they are needed to calibrate and validate rainfall thresholds, physically based models and early warning systems. The setting up of landslide inventories with traditional methods (e.g. remote sensing, field surveys and manual retrieval of data from technical reports and local newspapers) is time consuming. The objective of this work is to automatically set up a landslide inventory using a state-of-the art semantic engine based on data mining on online news (Battistini et al., 2013) and to evaluate if the automatically generated inventory can be used to validate a regional scale landslide warning system based on rainfall-thresholds. The semantic engine scanned internet news in real time in a 50 months test period. At the end of the process, an inventory of approximately 900 landslides was set up for the Tuscany region (23,000 km2, Italy). The inventory was compared with the outputs of the regional landslide early warning system based on rainfall thresholds, and a good correspondence was found: e.g. 84% of the events reported in the news is correctly identified by the model. In addition, the cases of not correspondence were forwarded to the rainfall threshold developers, which used these inputs to update some of the thresholds. On the basis of the results obtained, we conclude that automatic validation of landslide models using geolocalized landslide events feedback is possible. The source of data for validation can be obtained directly from the internet channel using an appropriate semantic engine. We also automated the validation procedure, which is based on a comparison between forecasts and reported events. We verified that our approach can be automatically used for a near real time validation of the warning system and for a semi-automatic update of the rainfall thresholds, which could lead to an improvement of the forecasting effectiveness of the warning system. In the near future, the proposed procedure could operate in continuous time and could allow for a periodic update of landslide hazard models and landslide early warning systems with minimum human intervention. References: Battistini, A., Segoni, S., Manzo, G., Catani, F., Casagli, N. (2013). Web data mining for automatic inventory of geohazards at national scale. Applied Geography, 43, 147-158.

Automatic Text Structuring and Summarization.

ERIC Educational Resources Information Center

Salton, Gerard; And Others

1997-01-01

Discussion of the use of information retrieval techniques for automatic generation of semantic hypertext links focuses on automatic text summarization. Topics include World Wide Web links, text segmentation, and evaluation of text summarization by comparing automatically generated abstracts with manually prepared abstracts. (Author/LRW)

Enhancing acronym/abbreviation knowledge bases with semantic information.

PubMed

Torii, Manabu; Liu, Hongfang

2007-10-11

In the biomedical domain, a terminology knowledge base that associates acronyms/abbreviations (denoted as SFs) with the definitions (denoted as LFs) is highly needed. For the construction such terminology knowledge base, we investigate the feasibility to build a system automatically assigning semantic categories to LFs extracted from text. Given a collection of pairs (SF,LF) derived from text, we i) assess the coverage of LFs and pairs (SF,LF) in the UMLS and justify the need of a semantic category assignment system; and ii) automatically derive name phrases annotated with semantic category and construct a system using machine learning. Utilizing ADAM, an existing collection of (SF,LF) pairs extracted from MEDLINE, our system achieved an f-measure of 87% when assigning eight UMLS-based semantic groups to LFs. The system has been incorporated into a web interface which integrates SF knowledge from multiple SF knowledge bases. Web site: http://gauss.dbb.georgetown.edu/liblab/SFThesurus.

Automatic processing of semantic relations in fMRI: neural activation during semantic priming of taxonomic and thematic categories.

PubMed

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

2008-07-07

Most current models of knowledge organization are based on hierarchical or taxonomic categories (animals, tools). Another important organizational pattern is thematic categorization, i.e. categories held together by external relations, a unifying scene or event (car and garage). The goal of this study was to compare the neural correlates of these categories under automatic processing conditions that minimize strategic influences. We used fMRI to examine neural correlates of semantic priming for category members with a short stimulus onset asynchrony (SOA) of 200 ms as subjects performed a lexical decision task. Four experimental conditions were compared: thematically related words (car-garage); taxonomically related (car-bus); unrelated (car-spoon); non-word trials (car-derf). We found faster reaction times for related than for unrelated prime-target pairs for both thematic and taxonomic categories. However, the size of the thematic priming effect was greater than that of the taxonomic. The imaging data showed signal changes for the taxonomic priming effects in the right precuneus, postcentral gyrus, middle frontal and superior frontal gyri and thematic priming effects in the right middle frontal gyrus and anterior cingulate. The contrast of neural priming effects showed larger signal changes in the right precuneus associated with the taxonomic but not with thematic priming response. We suggest that the greater involvement of precuneus in the processing of taxonomic relations indicates their reduced salience in the knowledge structure compared to more prominent thematic relations.

A neotropical Miocene pollen database employing image-based search and semantic modeling.

PubMed

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W; Jaramillo, Carlos; Shyu, Chi-Ren

2014-08-01

Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

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.

Early Visual Word Processing Is Flexible: Evidence from Spatiotemporal Brain Dynamics.

PubMed

Chen, Yuanyuan; Davis, Matthew H; Pulvermüller, Friedemann; Hauk, Olaf

2015-09-01

Visual word recognition is often described as automatic, but the functional locus of top-down effects is still a matter of debate. Do task demands modulate how information is retrieved, or only how it is used? We used EEG/MEG recordings to assess whether, when, and how task contexts modify early retrieval of specific psycholinguistic information in occipitotemporal cortex, an area likely to contribute to early stages of visual word processing. Using a parametric approach, we analyzed the spatiotemporal response patterns of occipitotemporal cortex for orthographic, lexical, and semantic variables in three psycholinguistic tasks: silent reading, lexical decision, and semantic decision. Task modulation of word frequency and imageability effects occurred simultaneously in ventral occipitotemporal regions-in the vicinity of the putative visual word form area-around 160 msec, following task effects on orthographic typicality around 100 msec. Frequency and typicality also produced task-independent effects in anterior temporal lobe regions after 200 msec. The early task modulation for several specific psycholinguistic variables indicates that occipitotemporal areas integrate perceptual input with prior knowledge in a task-dependent manner. Still, later task-independent effects in anterior temporal lobes suggest that word recognition eventually leads to retrieval of semantic information irrespective of task demands. We conclude that even a highly overlearned visual task like word recognition should be described as flexible rather than automatic.

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…

Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory

NASA Astrophysics Data System (ADS)

Soilán, Mario; Riveiro, Belén; Martínez-Sánchez, Joaquín; Arias, Pedro

2016-04-01

Nowadays, mobile laser scanning has become a valid technology for infrastructure inspection. This technology permits collecting accurate 3D point clouds of urban and road environments and the geometric and semantic analysis of data became an active research topic in the last years. This paper focuses on the detection of vertical traffic signs in 3D point clouds acquired by a LYNX Mobile Mapper system, comprised of laser scanning and RGB cameras. Each traffic sign is automatically detected in the LiDAR point cloud, and its main geometric parameters can be automatically extracted, therefore aiding the inventory process. Furthermore, the 3D position of traffic signs are reprojected on the 2D images, which are spatially and temporally synced with the point cloud. Image analysis allows for recognizing the traffic sign semantics using machine learning approaches. The presented method was tested in road and urban scenarios in Galicia (Spain). The recall results for traffic sign detection are close to 98%, and existing false positives can be easily filtered after point cloud projection. Finally, the lack of a large, publicly available Spanish traffic sign database is pointed out.

Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Bassier, M.; Vergauwen, M.; Van Genechten, B.

2017-08-01

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

Bridging the semantic gap in sports

NASA Astrophysics Data System (ADS)

Li, Baoxin; Errico, James; Pan, Hao; Sezan, M. Ibrahim

2003-01-01

One of the major challenges facing current media management systems and the related applications is the so-called "semantic gap" between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs. The framework is based on a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the semantic gap by providing rudimentary semantic information obtained through media analysis. We further propose a novel approach, which makes use of independently generated rich textual metadata, to fill the gap completely through synchronization of the information-laden textual data with the basic event segments. An MPEG-7 compliant prototype browsing system has been implemented to demonstrate semantic retrieval and summarization of sports video.

The shared neural basis of music and language.

PubMed

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

2017-08-15

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

To predict or not to predict: influences of task and strategy on the processing of semantic relations.

PubMed

Roehm, Dietmar; Bornkessel-Schlesewsky, Ina; Rösler, Frank; Schlesewsky, Matthias

2007-08-01

We report a series of event-related potential experiments designed to dissociate the functionally distinct processes involved in the comprehension of highly restricted lexical-semantic relations (antonyms). We sought to differentiate between influences of semantic relatedness (which are independent of the experimental setting) and processes related to predictability (which differ as a function of the experimental environment). To this end, we conducted three ERP studies contrasting the processing of antonym relations (black-white) with that of related (black-yellow) and unrelated (black-nice) word pairs. Whereas the lexical-semantic manipulation was kept constant across experiments, the experimental environment and the task demands varied: Experiment 1 presented the word pairs in a sentence context of the form The opposite of X is Y and used a sensicality judgment. Experiment 2 used a word pair presentation mode and a lexical decision task. Experiment 3 also examined word pairs, but with an antonymy judgment task. All three experiments revealed a graded N400 response (unrelated > related > antonyms), thus supporting the assumption that semantic associations are processed automatically. In addition, the experiments revealed that, in highly constrained task environments, the N400 gradation occurs simultaneously with a P300 effect for the antonym condition, thus leading to the superficial impression of an extremely "reduced" N400 for antonym pairs. Comparisons across experiments and participant groups revealed that the P300 effect is not only a function of stimulus constraints (i.e., sentence context) and experimental task, but that it is also crucially influenced by individual processing strategies used to achieve successful task performance.

Do different perceptual task sets modulate electrophysiological correlates of masked visuomotor priming? Attention to shape and color put to the test.

PubMed

Zovko, Monika; Kiefer, Markus

2013-02-01

According to classical theories, automatic processes operate independently of attention. Recent evidence, however, shows that masked visuomotor priming, an example of an automatic process, depends on attention to visual form versus semantics. In a continuation of this approach, we probed feature-specific attention within the perceptual domain and tested in two event-related potential (ERP) studies whether masked visuomotor priming in a shape decision task specifically depends on attentional sensitization of visual pathways for shape in contrast to color. Prior to the masked priming procedure, a shape or a color decision task served to induce corresponding task sets. ERP analyses revealed visuomotor priming effects over the occipitoparietal scalp only after the shape, but not after the color induction task. Thus, top-down control coordinates automatic processing streams in congruency with higher-level goals even at a fine-grained level. Copyright © 2012 Society for Psychophysiological Research.

Link Correlated Military Data for Better Decision Support

DTIC Science & Technology

2011-06-01

automatically translated into URI based links, thus can greatly reduce man power cost on software development. 3 Linked Data Technique Tim Berners - Lee ...Linked Data - while Linked Data is usually considered as part of Semantic Web, or “the Semantic Web done right” as described by Tim himself - has been...Required data of automatic link construction mechanism on more kinds of correlations. References [1] B. L. Tim , “The next Web of open, linked data

Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

ERIC Educational Resources Information Center

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2012-01-01

Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified…

Orthographic versus semantic matching in visual search for words within lists.

PubMed

Léger, Laure; Rouet, Jean-François; Ros, Christine; Vibert, Nicolas

2012-03-01

An eye-tracking experiment was performed to assess the influence of orthographic and semantic distractor words on visual search for words within lists. The target word (e.g., "raven") was either shown to participants before the search (literal search) or defined by its semantic category (e.g., "bird", categorical search). In both cases, the type of words included in the list affected visual search times and eye movement patterns. In the literal condition, the presence of orthographic distractors sharing initial and final letters with the target word strongly increased search times. Indeed, the orthographic distractors attracted participants' gaze and were fixated for longer times than other words in the list. The presence of semantic distractors related to the target word also increased search times, which suggests that significant automatic semantic processing of nontarget words took place. In the categorical condition, semantic distractors were expected to have a greater impact on the search task. As expected, the presence in the list of semantic associates of the target word led to target selection errors. However, semantic distractors did not significantly increase search times any more, whereas orthographic distractors still did. Hence, the visual characteristics of nontarget words can be strong predictors of the efficiency of visual search even when the exact target word is unknown. The respective impacts of orthographic and semantic distractors depended more on the characteristics of lists than on the nature of the search task.

RysannMD: A biomedical semantic annotator balancing speed and accuracy.

PubMed

Cuzzola, John; Jovanović, Jelena; Bagheri, Ebrahim

2017-07-01

Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text. The biomedical community has already presented several solid automated semantic annotators. However, the existing tools are either strong in their disambiguation capacity, i.e., the ability to identify the correct biomedical concept for a given piece of text among several candidate concepts, or they excel in their processing time, i.e., work very efficiently, but none of the semantic annotation tools reported in the literature has both of these qualities. In this paper, we present RysannMD (Ryerson Semantic Annotator for Medical Domain), a biomedical semantic annotation tool that strikes a balance between processing time and performance while disambiguating biomedical terms. In other words, RysannMD provides reasonable disambiguation performance when choosing the right sense for a biomedical term in a given context, and does that in a reasonable time. To examine how RysannMD stands with respect to the state of the art biomedical semantic annotators, we have conducted a series of experiments using standard benchmarking corpora, including both gold and silver standards, and four modern biomedical semantic annotators, namely cTAKES, MetaMap, NOBLE Coder, and Neji. The annotators were compared with respect to the quality of the produced annotations measured against gold and silver standards using precision, recall, and F 1 measure and speed, i.e., processing time. In the experiments, RysannMD achieved the best median F 1 measure across the benchmarking corpora, independent of the standard used (silver/gold), biomedical subdomain, and document size. In terms of the annotation speed, RysannMD scored the second best median processing time across all the experiments. The obtained results indicate that RysannMD offers the best performance among the examined semantic annotators when both quality of annotation and speed are considered simultaneously. Copyright © 2017 Elsevier Inc. All rights reserved.

Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2012-01-01

Localist models of spreading activation (SA) and models assuming distributed-representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In the present study we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assumes a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations we demonstrated how findings that challenged attractor-based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments. PMID:23094718

Interoperable cross-domain semantic and geospatial framework for automatic change detection

NASA Astrophysics Data System (ADS)

Kuo, Chiao-Ling; Hong, Jung-Hong

2016-01-01

With the increasingly diverse types of geospatial data established over the last few decades, semantic interoperability in integrated applications has attracted much interest in the field of Geographic Information System (GIS). This paper proposes a new strategy and framework to process cross-domain geodata at the semantic level. This framework leverages the semantic equivalence of concepts between domains through bridge ontology and facilitates the integrated use of different domain data, which has been long considered as an essential superiority of GIS, but is impeded by the lack of understanding about the semantics implicitly hidden in the data. We choose the task of change detection to demonstrate how the introduction of ontology concept can effectively make the integration possible. We analyze the common properties of geodata and change detection factors, then construct rules and summarize possible change scenario for making final decisions. The use of topographic map data to detect changes in land use shows promising success, as far as the improvement of efficiency and level of automation is concerned. We believe the ontology-oriented approach will enable a new way for data integration across different domains from the perspective of semantic interoperability, and even open a new dimensionality for the future GIS.

Automatic Domain Adaptation of Word Sense Disambiguation Based on Sublanguage Semantic Schemata Applied to Clinical Narrative

ERIC Educational Resources Information Center

Patterson, Olga

2012-01-01

Domain adaptation of natural language processing systems is challenging because it requires human expertise. While manual effort is effective in creating a high quality knowledge base, it is expensive and time consuming. Clinical text adds another layer of complexity to the task due to privacy and confidentiality restrictions that hinder the…

Masked Associative/Semantic Priming Effects across Languages with Highly Proficient Bilinguals

ERIC Educational Resources Information Center

Perea, Manuel; Dunabeitia, Jon Andoni; Carreiras, Manuel

2008-01-01

One key issue for models of bilingual memory is to what degree the semantic representation from one of the languages is shared with the other language. In the present paper, we examine whether there is an early, automatic semantic priming effect across languages for noncognates with highly proficient (Basque/Spanish) bilinguals. Experiment 1 was a…

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.

Towards Automatic Semantic Labelling of 3D City Models

NASA Astrophysics Data System (ADS)

Rook, M.; Biljecki, F.; Diakité, A. A.

2016-10-01

The lack of semantic information in many 3D city models is a considerable limiting factor in their use, as a lot of applications rely on semantics. Such information is not always available, since it is not collected at all times, it might be lost due to data transformation, or its lack may be caused by non-interoperability in data integration from other sources. This research is a first step in creating an automatic workflow that semantically labels plain 3D city model represented by a soup of polygons, with semantic and thematic information, as defined in the CityGML standard. The first step involves the reconstruction of the topology, which is used in a region growing algorithm that clusters upward facing adjacent triangles. Heuristic rules, embedded in a decision tree, are used to compute a likeliness score for these regions that either represent the ground (terrain) or a RoofSurface. Regions with a high likeliness score, to one of the two classes, are used to create a decision space, which is used in a support vector machine (SVM). Next, topological relations are utilised to select seeds that function as a start in a region growing algorithm, to create regions of triangles of other semantic classes. The topological relationships of the regions are used in the aggregation of the thematic building features. Finally, the level of detail is detected to generate the correct output in CityGML. The results show an accuracy between 85 % and 99 % in the automatic semantic labelling on four different test datasets. The paper is concluded by indicating problems and difficulties implying the next steps in the research.

A neotropical Miocene pollen database employing image-based search and semantic modeling1

PubMed Central

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W.; Jaramillo, Carlos; Shyu, Chi-Ren

2014-01-01

• Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Discussion: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery. PMID:25202648

Oscillatory brain dynamics associated with the automatic processing of emotion in words.

PubMed

Wang, Lin; Bastiaansen, Marcel

2014-10-01

This study examines the automaticity of processing the emotional aspects of words, and characterizes the oscillatory brain dynamics that accompany this automatic processing. Participants read emotionally negative, neutral and positive nouns while performing a color detection task in which only perceptual-level analysis was required. Event-related potentials and time frequency representations were computed from the concurrently measured EEG. Negative words elicited a larger P2 and a larger late positivity than positive and neutral words, indicating deeper semantic/evaluative processing of negative words. In addition, sustained alpha power suppressions were found for the emotional compared to neutral words, in the time range from 500 to 1000ms post-stimulus. These results suggest that sustained attention was allocated to the emotional words, whereas the attention allocated to the neutral words was released after an initial analysis. This seems to hold even when the emotional content of the words is task-irrelevant. Copyright © 2014 Elsevier Inc. All rights reserved.

Conscious control over the content of unconscious cognition.

PubMed

Kunde, Wilfried; Kiesel, Andrea; Hoffmann, Joachim

2003-06-01

Visual stimuli (primes) presented too briefly to be consciously identified can nevertheless affect responses to subsequent stimuli - an instance of unconscious cognition. There is a lively debate as to whether such priming effects originate from unconscious semantic processing of the primes or from reactivation of learned motor responses that conscious stimuli afford during preceding practice. In four experiments we demonstrate that unconscious stimuli owe their impact neither to automatic semantic categorization nor to memory traces of preceding stimulus-response episodes, but to their match with pre-specified cognitive action-trigger conditions. The intentional creation of such triggers allows actors to control the way unconscious stimuli bias their behaviour.

Semantic Enhancement for Enterprise Data Management

NASA Astrophysics Data System (ADS)

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

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

Processing visual words with numbers: electrophysiological evidence for semantic activation.

PubMed

Lien, Mei-Ching; Allen, Philip; Martin, Nicole

2014-08-01

Perea, Duñabeitia, and Carreiras (Journal of Experimental Psychology: Human Perception and Performance 34:237-241, 2008) found that LEET stimuli, formed by a mixture of digits and letters (e.g., T4BL3 instead of TABLE), produced priming effects similar to those for regular words. This finding led them to conclude that LEET stimuli automatically activate lexical information. In the present study, we examined whether semantic activation occurs for LEET stimuli by using an electrophysiological measure called the N400 effect. The N400 effect, also known as the mismatch negativity, reflects detection of a mismatch between a word and the current semantic context. This N400 effect could occur only if the LEET stimulus had been identified and processed semantically. Participants determined whether a stimulus (word or LEET) was related to a given category (e.g., APPLE or 4PPL3 belongs to the category "fruit," but TABLE or T4BL3 does not). We found that LEET stimuli produced an N400 effect similar in magnitude to that for regular uppercase words, suggesting that LEET stimuli can access meaning in a manner similar to words presented in consistent uppercase letters.

Recognising discourse causality triggers in the biomedical domain.

PubMed

Mihăilă, Claudiu; Ananiadou, Sophia

2013-12-01

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.

Semi Automatic Ontology Instantiation in the domain of Risk Management

NASA Astrophysics Data System (ADS)

Makki, Jawad; Alquier, Anne-Marie; Prince, Violaine

One of the challenging tasks in the context of Ontological Engineering is to automatically or semi-automatically support the process of Ontology Learning and Ontology Population from semi-structured documents (texts). In this paper we describe a Semi-Automatic Ontology Instantiation method from natural language text, in the domain of Risk Management. This method is composed from three steps 1 ) Annotation with part-of-speech tags, 2) Semantic Relation Instances Extraction, 3) Ontology instantiation process. It's based on combined NLP techniques using human intervention between steps 2 and 3 for control and validation. Since it heavily relies on linguistic knowledge it is not domain dependent which is a good feature for portability between the different fields of risk management application. The proposed methodology uses the ontology of the PRIMA1 project (supported by the European community) as a Generic Domain Ontology and populates it via an available corpus. A first validation of the approach is done through an experiment with Chemical Fact Sheets from Environmental Protection Agency2.

Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Babacan, K.; Chen, L.; Sohn, G.

2017-11-01

As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

PubMed

Sen Sarma, Moushumi; Arcoleo, David; Khetani, Radhika S; Chee, Brant; Ling, Xu; He, Xin; Jiang, Jing; Mei, Qiaozhu; Zhai, ChengXiang; Schatz, Bruce

2011-07-01

With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments can be analyzed to extract concepts that are computational equivalents of a classification such as Gene Ontology, yielding discriminating concepts that differentiate gene mentions from other mentions. The functions of individual genes can be summarized from sentences in biological literature, to produce results resembling a model organism database entry that is automatically computed. Statistical frequency analysis based on literature phrase extraction generates offline semantic indexes to support these gene function services. The website with BeeSpace Navigator is free and open to all; there is no login requirement at www.beespace.illinois.edu for version 4. Materials from the 2010 BeeSpace Software Training Workshop are available at www.beespace.illinois.edu/bstwmaterials.php.

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.

a Conceptual Framework for Indoor Mapping by Using Grammars

NASA Astrophysics Data System (ADS)

Hu, X.; Fan, H.; Zipf, A.; Shang, J.; Gu, F.

2017-09-01

Maps are the foundation of indoor location-based services. Many automatic indoor mapping approaches have been proposed, but they rely highly on sensor data, such as point clouds and users' location traces. To address this issue, this paper presents a conceptual framework to represent the layout principle of research buildings by using grammars. This framework can benefit the indoor mapping process by improving the accuracy of generated maps and by dramatically reducing the volume of the sensor data required by traditional reconstruction approaches. In addition, we try to present more details of partial core modules of the framework. An example using the proposed framework is given to show the generation process of a semantic map. This framework is part of an ongoing research for the development of an approach for reconstructing semantic maps.

An ERP investigation of conditional reasoning with emotional and neutral contents.

PubMed

Blanchette, Isabelle; El-Deredy, Wael

2014-11-01

In two experiments we investigate conditional reasoning using event-related potentials (ERPs). Our goal was to examine the time course of inference making in two conditional forms, one logically valid (Modus Ponens, MP) and one logically invalid (Affirming the Consequent, AC). We focus particularly on the involvement of semantically-based inferential processes potentially marked by modulations of the N400. We also compared reasoning about emotional and neutral contents with separate sets of stimuli of differing linguistic complexity across the two experiments. Both MP and AC modulated the N400 component, suggesting the involvement of a semantically-based inferential mechanism common across different logical forms, content types, and linguistic features of the problems. Emotion did not have an effect on early components, and did not interact with components related to inference making. There was a main effect of emotion in the 800-1050 ms time window, consistent with an effect on sustained attention. The results suggest that conditional reasoning is not a purely formal process but that it importantly implicates semantic processing, and that the effect of emotion on reasoning does not primarily operate through a modulation of early automatic stages of information processing. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Retrieval, automaticity, vocabulary elaboration, orthography (RAVE-O): a comprehensive, fluency-based reading intervention program.

PubMed

Wolf, M; Miller, L; Donnelly, K

2000-01-01

The most important implication of the double-deficit hypothesis (Wolf & Bowers, in this issue) concerns a new emphasis on fluency and automaticity in intervention for children with developmental reading disabilities. The RAVE-O (Retrieval, Automaticity, Vocabulary Elaboration, Orthography) program is an experimental, fluency-based approach to reading intervention that is designed to accompany a phonological analysis program. In an effort to address multiple possible sources of dysfluency in readers with disabilities, the program involves comprehensive emphases both on fluency in word attack, word identification, and comprehension and on automaticity in underlying componential processes (e.g., phonological, orthographic, semantic, and lexical retrieval skills). The goals, theoretical principles, and applied activities of the RAVE-O curriculum are described with particular stress on facilitating the development of rapid orthographic pattern recognition and on changing children's attitudes toward language.

An Experiment in Scientific Code Semantic Analysis

NASA Technical Reports Server (NTRS)

Stewart, Mark E. M.

1998-01-01

This paper concerns a procedure that analyzes aspects of the meaning or semantics of scientific and engineering code. This procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and parsing this annotated code using multiple, distributed expert parsers. These semantic parser are designed to recognize formulae in different disciplines including physical and mathematical formulae and geometrical position in a numerical scheme. The parsers will automatically recognize and document some static, semantic concepts and locate some program semantic errors. Results are shown for a subroutine test case and a collection of combustion code routines. This ability to locate some semantic errors and document semantic concepts in scientific and engineering code should reduce the time, risk, and effort of developing and using these codes.

Writing in dyslexia: product and process.

PubMed

Morken, Frøydis; Helland, Turid

2013-08-01

Research on dyslexia has largely centred on reading. The aim of this study was to assess the writing of 13 children with and 28 without dyslexia at age 11 years. A programme for keystroke logging was used to allow recording of typing activity as the children performed a sentence dictation task. Five sentences were read aloud twice each. The task was to type the sentence as correctly as possible, with no time constraints. The data were analysed from a product (spelling, grammar and semantics) and process (transcription fluency and revisions) perspective, using repeated measures ANOVA and t-tests to investigate group differences. Furthermore, the data were correlated with measures of rapid automatic naming and working memory. Results showed that the group with dyslexia revised their texts as much as the typical group, but they used more time, and the result was poorer. Moreover, rapid automatic naming correlated with transcription fluency, and working memory correlated with the number of semantic errors. This shows that dyslexia is generally not an issue of effort and that cognitive skills that are known to be important for reading also affect writing. Copyright © 2013 John Wiley & Sons, Ltd.

Extending Automatic Parallelization to Optimize High-Level Abstractions for Multicore

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

Liao, C; Quinlan, D J; Willcock, J J

2008-12-12

Automatic introduction of OpenMP for sequential applications has attracted significant attention recently because of the proliferation of multicore processors and the simplicity of using OpenMP to express parallelism for shared-memory systems. However, most previous research has only focused on C and Fortran applications operating on primitive data types. C++ applications using high-level abstractions, such as STL containers and complex user-defined types, are largely ignored due to the lack of research compilers that are readily able to recognize high-level object-oriented abstractions and leverage their associated semantics. In this paper, we automatically parallelize C++ applications using ROSE, a multiple-language source-to-source compiler infrastructuremore » which preserves the high-level abstractions and gives us access to their semantics. Several representative parallelization candidate kernels are used to explore semantic-aware parallelization strategies for high-level abstractions, combined with extended compiler analyses. Those kernels include an array-base computation loop, a loop with task-level parallelism, and a domain-specific tree traversal. Our work extends the applicability of automatic parallelization to modern applications using high-level abstractions and exposes more opportunities to take advantage of multicore processors.« less

Progress in The Semantic Analysis of Scientific Code

NASA Technical Reports Server (NTRS)

Stewart, Mark

2000-01-01

This paper concerns a procedure that analyzes aspects of the meaning or semantics of scientific and engineering code. This procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and parsing this annotated code using multiple, independent expert parsers. These semantic parsers encode domain knowledge and recognize formulae in different disciplines including physics, numerical methods, mathematics, and geometry. The parsers will automatically recognize and document some static, semantic concepts and help locate some program semantic errors. These techniques may apply to a wider range of scientific codes. If so, the techniques could reduce the time, risk, and effort required to develop and modify scientific codes.

The STP (Solar-Terrestrial Physics) Semantic Web based on the RSS1.0 and the RDF

NASA Astrophysics Data System (ADS)

Kubo, T.; Murata, K. T.; Kimura, E.; Ishikura, S.; Shinohara, I.; Kasaba, Y.; Watari, S.; Matsuoka, D.

2006-12-01

In the Solar-Terrestrial Physics (STP), it is pointed out that circulation and utilization of observation data among researchers are insufficient. To archive interdisciplinary researches, we need to overcome this circulation and utilization problems. Under such a background, authors' group has developed a world-wide database that manages meta-data of satellite and ground-based observation data files. It is noted that retrieving meta-data from the observation data and registering them to database have been carried out by hand so far. Our goal is to establish the STP Semantic Web. The Semantic Web provides a common framework that allows a variety of data shared and reused across applications, enterprises, and communities. We also expect that the secondary information related with observations, such as event information and associated news, are also shared over the networks. The most fundamental issue on the establishment is who generates, manages and provides meta-data in the Semantic Web. We developed an automatic meta-data collection system for the observation data using the RSS (RDF Site Summary) 1.0. The RSS1.0 is one of the XML-based markup languages based on the RDF (Resource Description Framework), which is designed for syndicating news and contents of news-like sites. The RSS1.0 is used to describe the STP meta-data, such as data file name, file server address and observation date. To describe the meta-data of the STP beyond RSS1.0 vocabulary, we defined original vocabularies for the STP resources using the RDF Schema. The RDF describes technical terms on the STP along with the Dublin Core Metadata Element Set, which is standard for cross-domain information resource descriptions. Researchers' information on the STP by FOAF, which is known as an RDF/XML vocabulary, creates a machine-readable metadata describing people. Using the RSS1.0 as a meta-data distribution method, the workflow from retrieving meta-data to registering them into the database is automated. This technique is applied for several database systems, such as the DARTS database system and NICT Space Weather Report Service. The DARTS is a science database managed by ISAS/JAXA in Japan. We succeeded in generating and collecting the meta-data automatically for the CDF (Common data Format) data, such as Reimei satellite data, provided by the DARTS. We also create an RDF service for space weather report and real-time global MHD simulation 3D data provided by the NICT. Our Semantic Web system works as follows: The RSS1.0 documents generated on the data sites (ISAS and NICT) are automatically collected by a meta-data collection agent. The RDF documents are registered and the agent extracts meta-data to store them in the Sesame, which is an open source RDF database with support for RDF Schema inferencing and querying. The RDF database provides advanced retrieval processing that has considered property and relation. Finally, the STP Semantic Web provides automatic processing or high level search for the data which are not only for observation data but for space weather news, physical events, technical terms and researches information related to the STP.

Information Pre-Processing using Domain Meta-Ontology and Rule Learning System

NASA Astrophysics Data System (ADS)

Ranganathan, Girish R.; Biletskiy, Yevgen

Around the globe, extraordinary amounts of documents are being created by Enterprises and by users outside these Enterprises. The documents created in the Enterprises constitute the main focus of the present chapter. These documents are used to perform numerous amounts of machine processing. While using thesedocuments for machine processing, lack of semantics of the information in these documents may cause misinterpretation of the information, thereby inhibiting the productiveness of computer assisted analytical work. Hence, it would be profitable to the Enterprises if they use well defined domain ontologies which will serve as rich source(s) of semantics for the information in the documents. These domain ontologies can be created manually, semi-automatically or fully automatically. The focus of this chapter is to propose an intermediate solution which will enable relatively easy creation of these domain ontologies. The process of extracting and capturing domain ontologies from these voluminous documents requires extensive involvement of domain experts and application of methods of ontology learning that are substantially labor intensive; therefore, some intermediate solutions which would assist in capturing domain ontologies must be developed. This chapter proposes a solution in this direction which involves building a meta-ontology that will serve as an intermediate information source for the main domain ontology. This chapter proposes a solution in this direction which involves building a meta-ontology as a rapid approach in conceptualizing a domain of interest from huge amount of source documents. This meta-ontology can be populated by ontological concepts, attributes and relations from documents, and then refined in order to form better domain ontology either through automatic ontology learning methods or some other relevant ontology building approach.

Semantic Priming from Letter-Searched Primes Occurs for Low- but Not High-Frequency Targets: Automatic Semantic Access May Not Be a Myth

ERIC Educational Resources Information Center

Tse, Chi-Shing; Neely, James H.

2007-01-01

Letter-search (LS) within a prime often eliminates semantic priming. In 2 lexical decision experiments, the authors found that priming from LS primes occurred for low-frequency (LF) but not high-frequency (HF) targets whether the target's word frequency was manipulated between or within participants and whether the prime-target pairs were…

A Semantic Analysis Method for Scientific and Engineering Code

NASA Technical Reports Server (NTRS)

Stewart, Mark E. M.

1998-01-01

This paper develops a procedure to statically analyze aspects of the meaning or semantics of scientific and engineering code. The analysis involves adding semantic declarations to a user's code and parsing this semantic knowledge with the original code using multiple expert parsers. These semantic parsers are designed to recognize formulae in different disciplines including physical and mathematical formulae and geometrical position in a numerical scheme. In practice, a user would submit code with semantic declarations of primitive variables to the analysis procedure, and its semantic parsers would automatically recognize and document some static, semantic concepts and locate some program semantic errors. A prototype implementation of this analysis procedure is demonstrated. Further, the relationship between the fundamental algebraic manipulations of equations and the parsing of expressions is explained. This ability to locate some semantic errors and document semantic concepts in scientific and engineering code should reduce the time, risk, and effort of developing and using these codes.

Executive control over unconscious cognition: attentional sensitization of unconscious information processing

PubMed Central

Kiefer, Markus

2012-01-01

Unconscious priming is a prototypical example of an automatic process, which is initiated without deliberate intention. Classical theories of automaticity assume that such unconscious automatic processes occur in a purely bottom-up driven fashion independent of executive control mechanisms. In contrast to these classical theories, our attentional sensitization model of unconscious information processing proposes that unconscious processing is susceptible to executive control and is only elicited if the cognitive system is configured accordingly. It is assumed that unconscious processing depends on attentional amplification of task-congruent processing pathways as a function of task sets. This article provides an overview of the latest research on executive control influences on unconscious information processing. I introduce refined theories of automaticity with a particular focus on the attentional sensitization model of unconscious cognition which is specifically developed to account for various attentional influences on different types of unconscious information processing. In support of the attentional sensitization model, empirical evidence is reviewed demonstrating executive control influences on unconscious cognition in the domains of visuo-motor and semantic processing: subliminal priming depends on attentional resources, is susceptible to stimulus expectations and is influenced by action intentions and task sets. This suggests that even unconscious processing is flexible and context-dependent as a function of higher-level executive control settings. I discuss that the assumption of attentional sensitization of unconscious information processing can accommodate conflicting findings regarding the automaticity of processes in many areas of cognition and emotion. This theoretical view has the potential to stimulate future research on executive control of unconscious processing in healthy and clinical populations. PMID:22470329

Executive control over unconscious cognition: attentional sensitization of unconscious information processing.

PubMed

Kiefer, Markus

2012-01-01

Unconscious priming is a prototypical example of an automatic process, which is initiated without deliberate intention. Classical theories of automaticity assume that such unconscious automatic processes occur in a purely bottom-up driven fashion independent of executive control mechanisms. In contrast to these classical theories, our attentional sensitization model of unconscious information processing proposes that unconscious processing is susceptible to executive control and is only elicited if the cognitive system is configured accordingly. It is assumed that unconscious processing depends on attentional amplification of task-congruent processing pathways as a function of task sets. This article provides an overview of the latest research on executive control influences on unconscious information processing. I introduce refined theories of automaticity with a particular focus on the attentional sensitization model of unconscious cognition which is specifically developed to account for various attentional influences on different types of unconscious information processing. In support of the attentional sensitization model, empirical evidence is reviewed demonstrating executive control influences on unconscious cognition in the domains of visuo-motor and semantic processing: subliminal priming depends on attentional resources, is susceptible to stimulus expectations and is influenced by action intentions and task sets. This suggests that even unconscious processing is flexible and context-dependent as a function of higher-level executive control settings. I discuss that the assumption of attentional sensitization of unconscious information processing can accommodate conflicting findings regarding the automaticity of processes in many areas of cognition and emotion. This theoretical view has the potential to stimulate future research on executive control of unconscious processing in healthy and clinical populations.

Symbolic, Nonsymbolic and Conceptual: An Across-Notation Study on the Space Mapping of Numerals.

PubMed

Zhang, Yu; You, Xuqun; Zhu, Rongjuan

2016-07-01

Previous studies suggested that there are interconnections between two numeral modalities of symbolic notation and nonsymbolic notation (array of dots), differences and similarities of the processing, and representation of the two modalities have both been found in previous research. However, whether there are differences between the spatial representation and numeral-space mapping of the two numeral modalities of symbolic notation and nonsymbolic notation is still uninvestigated. The present study aims to examine whether there are differences between the spatial representation and numeral-space mapping of the two numeral modalities of symbolic notation and nonsymbolic notation; especially how zero, as both a symbolic magnitude numeral and a nonsymbolic conceptual numeral, mapping onto space; and if the mapping happens automatically at an early stage of the numeral information processing. Results of the two experiments demonstrate that the low-level processing of symbolic numerals including zero and nonsymbolic numerals except zero can mapping onto space, whereas the low-level processing of nonsymbolic zero as a semantic conceptual numeral cannot mapping onto space, which indicating the specialty of zero in the numeral domain. The present study indicates that the processing of non-semantic numerals can mapping onto space, whereas semantic conceptual numerals cannot mapping onto space. © The Author(s) 2016.

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

PubMed Central

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

2015-01-01

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

Building the Knowledge Base to Support the Automatic Animation Generation of Chinese Traditional Architecture

NASA Astrophysics Data System (ADS)

Wei, Gongjin; Bai, Weijing; Yin, Meifang; Zhang, Songmao

We present a practice of applying the Semantic Web technologies in the domain of Chinese traditional architecture. A knowledge base consisting of one ontology and four rule bases is built to support the automatic generation of animations that demonstrate the construction of various Chinese timber structures based on the user's input. Different Semantic Web formalisms are used, e.g., OWL DL, SWRL and Jess, to capture the domain knowledge, including the wooden components needed for a given building, construction sequence, and the 3D size and position of every piece of wood. Our experience in exploiting the current Semantic Web technologies in real-world application systems indicates their prominent advantages (such as the reasoning facilities and modeling tools) as well as the limitations (such as low efficiency).

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

PubMed

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

2014-01-01

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

Domain Specific Language Support for Exascale. Final Project Report

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

Baden, Scott

The project developed a domain specific translator enable legacy MPI source code to tolerate communication delays, which are increasing over time due to technological factors. The translator performs source-to-source translation that incorporates semantic information into the translation process. The output of the translator is a C program runs as a data driven program, and uses an existing run time to overlap communication automatically

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

PubMed

Luo, Jiebo; Boutell, Matthew

2005-05-01

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

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.

Effects of semantic relatedness on age-related associative memory deficits: the role of theta oscillations.

PubMed

Crespo-Garcia, Maite; Cantero, Jose L; Atienza, Mercedes

2012-07-16

Growing evidence suggests that age-related deficits in associative memory are alleviated when the to-be-associated items are semantically related. Here we investigate whether this beneficial effect of semantic relatedness is paralleled by spatio-temporal changes in cortical EEG dynamics during incidental encoding. Young and older adults were presented with faces at a particular spatial location preceded by a biographical cue that was either semantically related or unrelated. As expected, automatic encoding of face-location associations benefited from semantic relatedness in the two groups of age. This effect correlated with increased power of theta oscillations over medial and anterior lateral regions of the prefrontal cortex (PFC) and lateral regions of the posterior parietal cortex (PPC) in both groups. But better-performing elders also showed increased brain-behavior correlation in the theta band over the right inferior frontal gyrus (IFG) as compared to young adults. Semantic relatedness was, however, insufficient to fully eliminate age-related differences in associative memory. In line with this finding, poorer-performing elders relative to young adults showed significant reductions of theta power in the left IFG that were further predictive of behavioral impairment in the recognition task. All together, these results suggest that older adults benefit less than young adults from executive processes during encoding mainly due to neural inefficiency over regions of the left ventrolateral prefrontal cortex (VLPFC). But this associative deficit may be partially compensated for by engaging preexistent semantic knowledge, which likely leads to an efficient recruitment of attentional and integration processes supported by the left PPC and left anterior PFC respectively, together with neural compensatory mechanisms governed by the right VLPFC. Copyright © 2012 Elsevier Inc. All rights reserved.

Deriving a probabilistic syntacto-semantic grammar for biomedicine based on domain-specific terminologies

PubMed Central

Fan, Jung-Wei; Friedman, Carol

2011-01-01

Biomedical natural language processing (BioNLP) is a useful technique that unlocks valuable information stored in textual data for practice and/or research. Syntactic parsing is a critical component of BioNLP applications that rely on correctly determining the sentence and phrase structure of free text. In addition to dealing with the vast amount of domain-specific terms, a robust biomedical parser needs to model the semantic grammar to obtain viable syntactic structures. With either a rule-based or corpus-based approach, the grammar engineering process requires substantial time and knowledge from experts, and does not always yield a semantically transferable grammar. To reduce the human effort and to promote semantic transferability, we propose an automated method for deriving a probabilistic grammar based on a training corpus consisting of concept strings and semantic classes from the Unified Medical Language System (UMLS), a comprehensive terminology resource widely used by the community. The grammar is designed to specify noun phrases only due to the nominal nature of the majority of biomedical terminological concepts. Evaluated on manually parsed clinical notes, the derived grammar achieved a recall of 0.644, precision of 0.737, and average cross-bracketing of 0.61, which demonstrated better performance than a control grammar with the semantic information removed. Error analysis revealed shortcomings that could be addressed to improve performance. The results indicated the feasibility of an approach which automatically incorporates terminology semantics in the building of an operational grammar. Although the current performance of the unsupervised solution does not adequately replace manual engineering, we believe once the performance issues are addressed, it could serve as an aide in a semi-supervised solution. PMID:21549857

Event-related potentials reveal task-dependence and inter-individual differences in negation processing during silent listening and explicit truth-value evaluation.

PubMed

Herbert, C; Kissler, J

2014-09-26

In sentences such as dogs cannot fly/bark, evaluation of the truth-value of the sentence is assumed to appear after the negation has been integrated into the sentence structure. Moreover negation processing and truth-value processing are considered effortful processes, whereas processing of the semantic relatedness of the words within sentences is thought to occur automatically. In the present study, modulation of event-related brain potentials (N400 and late positive potential, LPP) was investigated during an implicit task (silent listening) and active truth-value evaluation to test these theoretical assumptions and determine if truth-value evaluation will be modulated by the way participants processed the negated information implicitly prior to truth-value verification. Participants first listened to negated sentences and then evaluated these sentences for their truth-value in an active evaluation task. During passive listening, the LPP was generally more pronounced for targets in false negative (FN) than true negative (TN) sentences, indicating enhanced attention allocation to semantically-related but false targets. N400 modulation by truth-value (FN>TN) was observed in 11 out of 24 participants. However, during active evaluation, processing of semantically-unrelated but true targets (TN) elicited larger N400 and LPP amplitudes as well as a pronounced frontal negativity. This pattern was particularly prominent in those 11 individuals, whose N400 modulation during silent listening indicated that they were more sensitive to violations of the truth-value than to semantic priming effects. The results provide evidence for implicit truth-value processing during silent listening of negated sentences and for task dependence related to inter-individual differences in implicit negation processing. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System.

PubMed

Storck, Michael; Krumm, Rainer; Dugas, Martin

2016-01-01

Medical documentation is applied in various settings including patient care and clinical research. Since procedures of medical documentation are heterogeneous and developed further, secondary use of medical data is complicated. Development of medical forms, merging of data from different sources and meta-analyses of different data sets are currently a predominantly manual process and therefore difficult and cumbersome. Available applications to automate these processes are limited. In particular, tools to compare multiple documentation forms are missing. The objective of this work is to design, implement and evaluate the new system ODMSummary for comparison of multiple forms with a high number of semantically annotated data elements and a high level of usability. System requirements are the capability to summarize and compare a set of forms, enable to estimate the documentation effort, track changes in different versions of forms and find comparable items in different forms. Forms are provided in Operational Data Model format with semantic annotations from the Unified Medical Language System. 12 medical experts were invited to participate in a 3-phase evaluation of the tool regarding usability. ODMSummary (available at https://odmtoolbox.uni-muenster.de/summary/summary.html) provides a structured overview of multiple forms and their documentation fields. This comparison enables medical experts to assess multiple forms or whole datasets for secondary use. System usability was optimized based on expert feedback. The evaluation demonstrates that feedback from domain experts is needed to identify usability issues. In conclusion, this work shows that automatic comparison of multiple forms is feasible and the results are usable for medical experts.

Semantic Pattern Analysis for Verbal Fluency Based Assessment of Neurological Disorders

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

Sukumar, Sreenivas R; Ainsworth, Keela C; Brown, Tyler C

In this paper, we present preliminary results of semantic pattern analysis of verbal fluency tests used for assessing cognitive psychological and neuropsychological disorders. We posit that recent advances in semantic reasoning and artificial intelligence can be combined to create a standardized computer-aided diagnosis tool to automatically evaluate and interpret verbal fluency tests. Towards that goal, we derive novel semantic similarity (phonetic, phonemic and conceptual) metrics and present the predictive capability of these metrics on a de-identified dataset of participants with and without neurological disorders.

Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task.

PubMed

König, Alexandra; Linz, Nicklas; Tröger, Johannes; Wolters, Maria; Alexandersson, Jan; Robert, Phillipe

2018-06-08

Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline. © 2018 S. Karger AG, Basel.

Text Mining the History of Medicine.

PubMed

Thompson, Paul; Batista-Navarro, Riza Theresa; Kontonatsios, Georgios; Carter, Jacob; Toon, Elizabeth; McNaught, John; Timmermann, Carsten; Worboys, Michael; Ananiadou, Sophia

2016-01-01

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform.

Text Mining the History of Medicine

PubMed Central

Thompson, Paul; Batista-Navarro, Riza Theresa; Kontonatsios, Georgios; Carter, Jacob; Toon, Elizabeth; McNaught, John; Timmermann, Carsten; Worboys, Michael; Ananiadou, Sophia

2016-01-01

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform. PMID:26734936

Mechanisms of masked priming: a meta-analysis.

PubMed

Van den Bussche, Eva; Van den Noortgate, Wim; Reynvoet, Bert

2009-05-01

The extent to which unconscious information can influence behavior has been a topic of considerable debate throughout the history of psychology. A frequently used method for studying subliminal processing is the masked priming paradigm. The authors focused on studies in which this paradigm was used. Their aim was twofold: first, to assess the magnitude of subliminal priming across the literature and to determine whether subliminal primes are processed semantically, and second, to examine potential moderators of priming effects. The authors found significant priming in their analyses, indicating that unconsciously presented information can influence behavior. Furthermore, priming was observed under circumstances in which a nonsemantic interpretation could not fully explain the effects, suggesting that subliminally presented information can be processed semantically. Nonetheless, the nonsemantic processing of primes is enhanced and priming effects are boosted when the experimental context allows the formation of automatic stimulus-response mappings. This quantitative review also revealed several moderators that influence the strength of priming. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Effects of Divided Attention at Retrieval on Conceptual Implicit Memory

PubMed Central

Prull, Matthew W.; Lawless, Courtney; Marshall, Helen M.; Sherman, Annabella T. K.

2016-01-01

This study investigated whether conceptual implicit memory is sensitive to process-specific interference at the time of retrieval. Participants performed the implicit memory test of category exemplar generation (CEG; Experiments 1 and 3), or the matched explicit memory test of category-cued recall (Experiment 2), both of which are conceptually driven memory tasks, under one of two divided attention (DA) conditions in which participants simultaneously performed a distracting task. The distracting task was either syllable judgments (dissimilar processes), or semantic judgments (similar processes) on unrelated words. Compared to full attention (FA) in which no distracting task was performed, DA had no effect on CEG priming overall, but reduced category-cued recall similarly regardless of distractor task. Analyses of distractor task performance also revealed differences between implicit and explicit memory retrieval. The evidence suggests that, whereas explicit memory retrieval requires attentional resources and is disrupted by semantic and phonological distracting tasks, conceptual implicit memory is automatic and unaffected even when distractor and memory tasks involve similar processes. PMID:26834678

Effects of Divided Attention at Retrieval on Conceptual Implicit Memory.

PubMed

Prull, Matthew W; Lawless, Courtney; Marshall, Helen M; Sherman, Annabella T K

2016-01-01

This study investigated whether conceptual implicit memory is sensitive to process-specific interference at the time of retrieval. Participants performed the implicit memory test of category exemplar generation (CEG; Experiments 1 and 3), or the matched explicit memory test of category-cued recall (Experiment 2), both of which are conceptually driven memory tasks, under one of two divided attention (DA) conditions in which participants simultaneously performed a distracting task. The distracting task was either syllable judgments (dissimilar processes), or semantic judgments (similar processes) on unrelated words. Compared to full attention (FA) in which no distracting task was performed, DA had no effect on CEG priming overall, but reduced category-cued recall similarly regardless of distractor task. Analyses of distractor task performance also revealed differences between implicit and explicit memory retrieval. The evidence suggests that, whereas explicit memory retrieval requires attentional resources and is disrupted by semantic and phonological distracting tasks, conceptual implicit memory is automatic and unaffected even when distractor and memory tasks involve similar processes.

The Contributions of Vocabulary and Letter Writing Automaticity to Word Reading and Spelling for Kindergartners

ERIC Educational Resources Information Center

Kim, Young-Suk; Al Otaiba, Stephanie; Puranik, Cynthia; Folsom, Jessica Sidler; Gruelich, Luana

2014-01-01

In the present study we examined the relation between alphabet knowledge fluency (letter names and sounds) and letter writing automaticity, and unique relations of letter writing automaticity and semantic knowledge (i.e., vocabulary) to word reading and spelling over and above code-related skills such as phonological awareness and alphabet…

Resolving Quasi-Synonym Relationships in Automatic Thesaurus Construction Using Fuzzy Rough Sets and an Inverse Term Frequency Similarity Function

ERIC Educational Resources Information Center

Davault, Julius M., III.

2009-01-01

One of the problems associated with automatic thesaurus construction is with determining the semantic relationship between word pairs. Quasi-synonyms provide a type of equivalence relationship: words are similar only for purposes of information retrieval. Determining such relationships in a thesaurus is hard to achieve automatically. The term…

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

PubMed

Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

2017-03-01

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

Semantic Information Extraction of Lanes Based on Onboard Camera Videos

NASA Astrophysics Data System (ADS)

Tang, L.; Deng, T.; Ren, C.

2018-04-01

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

On a High-Performance VLSI Solution to Database Problems.

DTIC Science & Technology

1981-08-01

offer such attractive features as automatic verification and. maintenance of semantic integrity, usage of views as abstraction and authorization...course, is the waste of too much potential resource. The global database may contain information for many different users and applications. In processing...working on, this may cause no damage at all, but some waste of space. Therefore one solution may be perhaps to do nothing to prevent its occurrence

Recognizable or Not: Towards Image Semantic Quality Assessment for Compression

NASA Astrophysics Data System (ADS)

Liu, Dong; Wang, Dandan; Li, Houqiang

2017-12-01

Traditionally, image compression was optimized for the pixel-wise fidelity or the perceptual quality of the compressed images given a bit-rate budget. But recently, compressed images are more and more utilized for automatic semantic analysis tasks such as recognition and retrieval. For these tasks, we argue that the optimization target of compression is no longer perceptual quality, but the utility of the compressed images in the given automatic semantic analysis task. Accordingly, we propose to evaluate the quality of the compressed images neither at pixel level nor at perceptual level, but at semantic level. In this paper, we make preliminary efforts towards image semantic quality assessment (ISQA), focusing on the task of optical character recognition (OCR) from compressed images. We propose a full-reference ISQA measure by comparing the features extracted from text regions of original and compressed images. We then propose to integrate the ISQA measure into an image compression scheme. Experimental results show that our proposed ISQA measure is much better than PSNR and SSIM in evaluating the semantic quality of compressed images; accordingly, adopting our ISQA measure to optimize compression for OCR leads to significant bit-rate saving compared to using PSNR or SSIM. Moreover, we perform subjective test about text recognition from compressed images, and observe that our ISQA measure has high consistency with subjective recognizability. Our work explores new dimensions in image quality assessment, and demonstrates promising direction to achieve higher compression ratio for specific semantic analysis tasks.

Functional MRI evidence for the decline of word retrieval and generation during normal aging.

PubMed

Baciu, M; Boudiaf, N; Cousin, E; Perrone-Bertolotti, M; Pichat, C; Fournet, N; Chainay, H; Lamalle, L; Krainik, A

2016-02-01

This fMRI study aimed to explore the effect of normal aging on word retrieval and generation. The question addressed is whether lexical production decline is determined by a direct mechanism, which concerns the language operations or is rather indirectly induced by a decline of executive functions. Indeed, the main hypothesis was that normal aging does not induce loss of lexical knowledge, but there is only a general slowdown in retrieval mechanisms involved in lexical processing, due to possible decline of the executive functions. We used three tasks (verbal fluency, object naming, and semantic categorization). Two groups of participants were tested (Young, Y and Aged, A), without cognitive and psychiatric impairment and showing similar levels of vocabulary. Neuropsychological testing revealed that older participants had lower executive function scores, longer processing speeds, and tended to have lower verbal fluency scores. Additionally, older participants showed higher scores for verbal automatisms and overlearned information. In terms of behavioral data, older participants performed as accurate as younger adults, but they were significantly slower for the semantic categorization and were less fluent for verbal fluency task. Functional MRI analyses suggested that older adults did not simply activate fewer brain regions involved in word production, but they actually showed an atypical pattern of activation. Significant correlations between the BOLD (Blood Oxygen Level Dependent) signal of aging-related (A > Y) regions and cognitive scores suggested that this atypical pattern of the activation may reveal several compensatory mechanisms (a) to overcome the slowdown in retrieval, due to the decline of executive functions and processing speed and (b) to inhibit verbal automatic processes. The BOLD signal measured in some other aging-dependent regions did not correlate with the behavioral and neuropsychological scores, and the overactivation of these uncorrelated regions would simply reveal dedifferentiation that occurs with aging. Altogether, our results suggest that normal aging is associated with a more difficult access to lexico-semantic operations and representations by a slowdown in executive functions, without any conceptual loss.

Context-rich semantic framework for effective data-to-decisions in coalition networks

NASA Astrophysics Data System (ADS)

Grueneberg, Keith; de Mel, Geeth; Braines, Dave; Wang, Xiping; Calo, Seraphin; Pham, Tien

2013-05-01

In a coalition context, data fusion involves combining of soft (e.g., field reports, intelligence reports) and hard (e.g., acoustic, imagery) sensory data such that the resulting output is better than what it would have been if the data are taken individually. However, due to the lack of explicit semantics attached with such data, it is difficult to automatically disseminate and put the right contextual data in the hands of the decision makers. In order to understand the data, explicit meaning needs to be added by means of categorizing and/or classifying the data in relationship to each other from base reference sources. In this paper, we present a semantic framework that provides automated mechanisms to expose real-time raw data effectively by presenting appropriate information needed for a given situation so that an informed decision could be made effectively. The system utilizes controlled natural language capabilities provided by the ITA (International Technology Alliance) Controlled English (CE) toolkit to provide a human-friendly semantic representation of messages so that the messages can be directly processed in human/machine hybrid environments. The Real-time Semantic Enrichment (RTSE) service adds relevant contextual information to raw data streams from domain knowledge bases using declarative rules. The rules define how the added semantics and context information are derived and stored in a semantic knowledge base. The software framework exposes contextual information from a variety of hard and soft data sources in a fast, reliable manner so that an informed decision can be made using semantic queries in intelligent software systems.

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

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

PubMed

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

2015-05-01

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

Integrating Semantic Information in Metadata Descriptions for a Geoscience-wide Resource Inventory.

NASA Astrophysics Data System (ADS)

Zaslavsky, I.; Richard, S. M.; Gupta, A.; Valentine, D.; Whitenack, T.; Ozyurt, I. B.; Grethe, J. S.; Schachne, A.

2016-12-01

Integrating semantic information into legacy metadata catalogs is a challenging issue and so far has been mostly done on a limited scale. We present experience of CINERGI (Community Inventory of Earthcube Resources for Geoscience Interoperability), an NSF Earthcube Building Block project, in creating a large cross-disciplinary catalog of geoscience information resources to enable cross-domain discovery. The project developed a pipeline for automatically augmenting resource metadata, in particular generating keywords that describe metadata documents harvested from multiple geoscience information repositories or contributed by geoscientists through various channels including surveys and domain resource inventories. The pipeline examines available metadata descriptions using text parsing, vocabulary management and semantic annotation and graph navigation services of GeoSciGraph. GeoSciGraph, in turn, relies on a large cross-domain ontology of geoscience terms, which bridges several independently developed ontologies or taxonomies including SWEET, ENVO, YAGO, GeoSciML, GCMD, SWO, and CHEBI. The ontology content enables automatic extraction of keywords reflecting science domains, equipment used, geospatial features, measured properties, methods, processes, etc. We specifically focus on issues of cross-domain geoscience ontology creation, resolving several types of semantic conflicts among component ontologies or vocabularies, and constructing and managing facets for improved data discovery and navigation. The ontology and keyword generation rules are iteratively improved as pipeline results are presented to data managers for selective manual curation via a CINERGI Annotator user interface. We present lessons learned from applying CINERGI metadata augmentation pipeline to a number of federal agency and academic data registries, in the context of several use cases that require data discovery and integration across multiple earth science data catalogs of varying quality and completeness. The inventory is accessible at http://cinergi.sdsc.edu, and the CINERGI project web page is http://earthcube.org/group/cinergi

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.

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

PubMed

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

2017-02-20

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

Generating Poetry Title Based on Semantic Relevance with Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Li, Z.; Niu, K.; He, Z. Q.

2017-09-01

Several approaches have been proposed to automatically generate Chinese classical poetry (CCP) in the past few years, but automatically generating the title of CCP is still a difficult problem. The difficulties are mainly reflected in two aspects. First, the words used in CCP are very different from modern Chinese words and there are no valid word segmentation tools. Second, the semantic relevance of characters in CCP not only exists in one sentence but also exists between the same positions of adjacent sentences, which is hard to grasp by the traditional text summarization models. In this paper, we propose an encoder-decoder model for generating the title of CCP. Our model encoder is a convolutional neural network (CNN) with two kinds of filters. To capture the commonly used words in one sentence, one kind of filters covers two characters horizontally at each step. The other covers two characters vertically at each step and can grasp the semantic relevance of characters between adjacent sentences. Experimental results show that our model is better than several other related models and can capture the semantic relevance of CCP more accurately.

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

PubMed

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

2016-06-03

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

ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System

PubMed Central

Krumm, Rainer; Dugas, Martin

2016-01-01

Introduction Medical documentation is applied in various settings including patient care and clinical research. Since procedures of medical documentation are heterogeneous and developed further, secondary use of medical data is complicated. Development of medical forms, merging of data from different sources and meta-analyses of different data sets are currently a predominantly manual process and therefore difficult and cumbersome. Available applications to automate these processes are limited. In particular, tools to compare multiple documentation forms are missing. The objective of this work is to design, implement and evaluate the new system ODMSummary for comparison of multiple forms with a high number of semantically annotated data elements and a high level of usability. Methods System requirements are the capability to summarize and compare a set of forms, enable to estimate the documentation effort, track changes in different versions of forms and find comparable items in different forms. Forms are provided in Operational Data Model format with semantic annotations from the Unified Medical Language System. 12 medical experts were invited to participate in a 3-phase evaluation of the tool regarding usability. Results ODMSummary (available at https://odmtoolbox.uni-muenster.de/summary/summary.html) provides a structured overview of multiple forms and their documentation fields. This comparison enables medical experts to assess multiple forms or whole datasets for secondary use. System usability was optimized based on expert feedback. Discussion The evaluation demonstrates that feedback from domain experts is needed to identify usability issues. In conclusion, this work shows that automatic comparison of multiple forms is feasible and the results are usable for medical experts. PMID:27736972

Automatic Construction of 3D Basic-Semantic Models of Inhabited Interiors Using Laser Scanners and RFID Sensors

PubMed Central

Valero, Enrique; Adan, Antonio; Cerrada, Carlos

2012-01-01

This paper is focused on the automatic construction of 3D basic-semantic models of inhabited interiors using laser scanners with the help of RFID technologies. This is an innovative approach, in whose field scarce publications exist. The general strategy consists of carrying out a selective and sequential segmentation from the cloud of points by means of different algorithms which depend on the information that the RFID tags provide. The identification of basic elements of the scene, such as walls, floor, ceiling, windows, doors, tables, chairs and cabinets, and the positioning of their corresponding models can then be calculated. The fusion of both technologies thus allows a simplified 3D semantic indoor model to be obtained. This method has been tested in real scenes under difficult clutter and occlusion conditions, and has yielded promising results. PMID:22778609

Task-Dependent Masked Priming Effects in Visual Word Recognition

PubMed Central

Kinoshita, Sachiko; Norris, Dennis

2012-01-01

A method used widely to study the first 250 ms of visual word recognition is masked priming: These studies have yielded a rich set of data concerning the processes involved in recognizing letters and words. In these studies, there is an implicit assumption that the early processes in word recognition tapped by masked priming are automatic, and masked priming effects should therefore be invariant across tasks. Contrary to this assumption, masked priming effects are modulated by the task goal: For example, only word targets show priming in the lexical decision task, but both words and non-words do in the same-different task; semantic priming effects are generally weak in the lexical decision task but are robust in the semantic categorization task. We explain how such task dependence arises within the Bayesian Reader account of masked priming (Norris and Kinoshita, 2008), and how the task dissociations can be used to understand the early processes in lexical access. PMID:22675316

Masked priming and ERPs dissociate maturation of orthographic and semantic components of visual word recognition in children

PubMed Central

Eddy, Marianna D.; Grainger, Jonathan; Holcomb, Phillip J.; Mitra, Priya; Gabrieli, John D. E.

2014-01-01

This study examined the time-course of reading single words in children and adults using masked repetition priming and the recording of event-related potentials. The N250 and N400 repetition priming effects were used to characterize form- and meaning-level processing, respectively. Children had larger amplitude N250 effects than adults for both shorter and longer duration primes. Children did not differ from adults on the N400 effect. The difference on the N250 suggests that automaticity for form processing is still maturing in children relative to adults, while the lack of differentiation on the N400 effect suggests that meaning processing is relatively mature by late childhood. The overall similarity in the children’s repetition priming effects to adults’ effects is in line with theories of reading acquisition, according to which children rapidly transition to an orthographic strategy for fast access to semantic information from print. PMID:24313638

Using Stream Features for Instant Document Filtering

DTIC Science & Technology

2012-11-01

expansion and qual- ity indicators in searching microblog posts. Advances in Information Retrieval, pages 362–367, 2011. [12] N. Naveed, T. Gottron, J ...16] G Salton and C Buckley. Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5):513–523, 1988. [17...Overview of the TREC-2012 Microblog Track. In trec.nist.gov. NIST. [19] Michael J Welch, Uri Schonfeld, Dan He, and Junghoo Cho. Topical semantics of

Emotional words can be embodied or disembodied: the role of superficial vs. deep types of processing

PubMed Central

Abbassi, Ensie; Blanchette, Isabelle; Ansaldo, Ana I.; Ghassemzadeh, Habib; Joanette, Yves

2015-01-01

Emotional words are processed rapidly and automatically in the left hemisphere (LH) and slowly, with the involvement of attention, in the right hemisphere (RH). This review aims to find the reason for this difference and suggests that emotional words can be processed superficially or deeply due to the involvement of the linguistic and imagery systems, respectively. During superficial processing, emotional words likely make connections only with semantically associated words in the LH. This part of the process is automatic and may be sufficient for the purpose of language processing. Deep processing, in contrast, seems to involve conceptual information and imagery of a word’s perceptual and emotional properties using autobiographical memory contents. Imagery and the involvement of autobiographical memory likely differentiate between emotional and neutral word processing and explain the salient role of the RH in emotional word processing. It is concluded that the level of emotional word processing in the RH should be deeper than in the LH and, thus, it is conceivable that the slow mode of processing adds certain qualities to the output. PMID:26217288

Emotional words can be embodied or disembodied: the role of superficial vs. deep types of processing.

PubMed

Abbassi, Ensie; Blanchette, Isabelle; Ansaldo, Ana I; Ghassemzadeh, Habib; Joanette, Yves

2015-01-01

Emotional words are processed rapidly and automatically in the left hemisphere (LH) and slowly, with the involvement of attention, in the right hemisphere (RH). This review aims to find the reason for this difference and suggests that emotional words can be processed superficially or deeply due to the involvement of the linguistic and imagery systems, respectively. During superficial processing, emotional words likely make connections only with semantically associated words in the LH. This part of the process is automatic and may be sufficient for the purpose of language processing. Deep processing, in contrast, seems to involve conceptual information and imagery of a word's perceptual and emotional properties using autobiographical memory contents. Imagery and the involvement of autobiographical memory likely differentiate between emotional and neutral word processing and explain the salient role of the RH in emotional word processing. It is concluded that the level of emotional word processing in the RH should be deeper than in the LH and, thus, it is conceivable that the slow mode of processing adds certain qualities to the output.

Automatically Grading Customer Confidence in a Formal Specification.

ERIC Educational Resources Information Center

Shukur, Zarina; Burke, Edmund; Foxley, Eric

1999-01-01

Describes an automatic grading system for a formal methods computer science course that is able to evaluate a formal specification written in the Z language. Quality is measured by considering first, specification correctness (syntax, semantics, and satisfaction of customer requirements), and second, specification maintainability (comparison of…

Semi-supervised word polarity identification in resource-lean languages.

PubMed

Dehdarbehbahani, Iman; Shakery, Azadeh; Faili, Heshaam

2014-10-01

Sentiment words, as fundamental constitutive parts of subjective sentences, have a substantial effect on analysis of opinions, emotions and beliefs. Most of the proposed methods for identifying the semantic orientations of words exploit rich linguistic resources such as WordNet, subjectivity corpora, or polarity tagged words. Shortage of such linguistic resources in resource-lean languages affects the performance of word polarity identification in these languages. In this paper, we present a method which exploits a language with rich subjectivity analysis resources (English) to identify the polarity of words in a resource-lean foreign language. The English WordNet and a sparse foreign WordNet infrastructure are used to create a heterogeneous, multilingual and weighted semantic network. To identify the semantic orientation of foreign words, a random walk based method is applied to the semantic network along with a set of automatically weighted English positive and negative seeds. In a post-processing phase, synonym and antonym relations in the foreign WordNet are used to filter the random walk results. Our experiments on English and Persian languages show that the proposed method can outperform state-of-the-art word polarity identification methods in both languages. Copyright © 2014 Elsevier Ltd. All rights reserved.

A semantic model for multimodal data mining in healthcare information systems.

PubMed

Iakovidis, Dimitris; Smailis, Christos

2012-01-01

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.

Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems.

PubMed

Zerrouki, Taha; Balla, Amar

2017-04-01

Arabic diacritics are often missed in Arabic scripts. This feature is a handicap for new learner to read َArabic, text to speech conversion systems, reading and semantic analysis of Arabic texts. The automatic diacritization systems are the best solution to handle this issue. But such automation needs resources as diactritized texts to train and evaluate such systems. In this paper, we describe our corpus of Arabic diacritized texts. This corpus is called Tashkeela. It can be used as a linguistic resource tool for natural language processing such as automatic diacritics systems, dis-ambiguity mechanism, features and data extraction. The corpus is freely available, it contains 75 million of fully vocalized words mainly 97 books from classical and modern Arabic language. The corpus is collected from manually vocalized texts using web crawling process.

A CityGML extension for traffic-sign objects that guides the automatic processing of data collected using Mobile Mapping technology

NASA Astrophysics Data System (ADS)

Varela-González, M.; Riveiro, B.; Arias-Sánchez, P.; González-Jorge, H.; Martínez-Sánchez, J.

2014-11-01

The rapid evolution of integral schemes, accounting for geometric and semantic data, has been importantly motivated by the advances in the last decade in mobile laser scanning technology; automation in data processing has also recently influenced the expansion of the new model concepts. This paper reviews some important issues involved in the new paradigms of city 3D modelling: an interoperable schema for city 3D modelling (cityGML) and mobile mapping technology to provide the features that composing the city model. This paper focuses in traffic signs, discussing their characterization using cityGML in order to ease the implementation of LiDAR technology in road management software, as well as analysing some limitations of the current technology in the labour of automatic detection and classification.

Modeling and formal representation of geospatial knowledge for the Geospatial Semantic Web

NASA Astrophysics Data System (ADS)

Huang, Hong; Gong, Jianya

2008-12-01

GML can only achieve geospatial interoperation at syntactic level. However, it is necessary to resolve difference of spatial cognition in the first place in most occasions, so ontology was introduced to describe geospatial information and services. But it is obviously difficult and improper to let users to find, match and compose services, especially in some occasions there are complicated business logics. Currently, with the gradual introduction of Semantic Web technology (e.g., OWL, SWRL), the focus of the interoperation of geospatial information has shifted from syntactic level to Semantic and even automatic, intelligent level. In this way, Geospatial Semantic Web (GSM) can be put forward as an augmentation to the Semantic Web that additionally includes geospatial abstractions as well as related reasoning, representation and query mechanisms. To advance the implementation of GSM, we first attempt to construct the mechanism of modeling and formal representation of geospatial knowledge, which are also two mostly foundational phases in knowledge engineering (KE). Our attitude in this paper is quite pragmatical: we argue that geospatial context is a formal model of the discriminate environment characters of geospatial knowledge, and the derivation, understanding and using of geospatial knowledge are located in geospatial context. Therefore, first, we put forward a primitive hierarchy of geospatial knowledge referencing first order logic, formal ontologies, rules and GML. Second, a metamodel of geospatial context is proposed and we use the modeling methods and representation languages of formal ontologies to process geospatial context. Thirdly, we extend Web Process Service (WPS) to be compatible with local DLL for geoprocessing and possess inference capability based on OWL.

Automatic Semantic Activation of Embedded Words: Is There a ''Hat'' in ''That''

ERIC Educational Resources Information Center

Bowers, J.S.; Davis, C.J.; Hanley, D.A.

2005-01-01

Participants semantically categorized target words that contain subsets (Experiment 1; e.g., target=hatch, subset=hat) or that are parts of supersets (Experiment 2; e.g., target=bee, superset=beer). In both experiments, the targets were categorized in a congruent condition (in which the subset-superset was associated with the same response, e.g.,…

GenieTutor: A Computer Assisted Second-Language Learning System Based on Semantic and Grammar Correctness Evaluations

ERIC Educational Resources Information Center

Kwon, Oh-Woog; Lee, Kiyoung; Kim, Young-Kil; Lee, Yunkeun

2015-01-01

This paper introduces a Dialog-Based Computer-Assisted second-Language Learning (DB-CALL) system using semantic and grammar correctness evaluations and the results of its experiment. While the system dialogues with English learners about a given topic, it automatically evaluates the grammar and content properness of their English utterances, then…

User Evaluation of Automatically Generated Semantic Hypertext Links in a Heavily Used Procedural Manual.

ERIC Educational Resources Information Center

Tebbutt, John

1999-01-01

Discusses efforts at National Institute of Standards and Technology (NIST) to construct an information discovery tool through the fusion of hypertext and information retrieval that works by parsing a contiguous document base into smaller documents and inserting semantic links between them. Also presents a case study that evaluated user reactions.…

CAD system for automatic analysis of CT perfusion maps

NASA Astrophysics Data System (ADS)

Hachaj, T.; Ogiela, M. R.

2011-03-01

In this article, authors present novel algorithms developed for the computer-assisted diagnosis (CAD) system for analysis of dynamic brain perfusion, computer tomography (CT) maps, cerebral blood flow (CBF), and cerebral blood volume (CBV). Those methods perform both quantitative analysis [detection and measurement and description with brain anatomy atlas (AA) of potential asymmetries/lesions] and qualitative analysis (semantic interpretation of visualized symptoms). The semantic interpretation (decision about type of lesion: ischemic/hemorrhagic, is the brain tissue at risk of infraction or not) of visualized symptoms is done by, so-called, cognitive inference processes allowing for reasoning on character of pathological regions based on specialist image knowledge. The whole system is implemented in.NET platform (C# programming language) and can be used on any standard PC computer with.NET framework installed.

Wide coverage biomedical event extraction using multiple partially overlapping corpora

PubMed Central

2013-01-01

Background Biomedical events are key to understanding physiological processes and disease, and wide coverage extraction is required for comprehensive automatic analysis of statements describing biomedical systems in the literature. In turn, the training and evaluation of extraction methods requires manually annotated corpora. However, as manual annotation is time-consuming and expensive, any single event-annotated corpus can only cover a limited number of semantic types. Although combined use of several such corpora could potentially allow an extraction system to achieve broad semantic coverage, there has been little research into learning from multiple corpora with partially overlapping semantic annotation scopes. Results We propose a method for learning from multiple corpora with partial semantic annotation overlap, and implement this method to improve our existing event extraction system, EventMine. An evaluation using seven event annotated corpora, including 65 event types in total, shows that learning from overlapping corpora can produce a single, corpus-independent, wide coverage extraction system that outperforms systems trained on single corpora and exceeds previously reported results on two established event extraction tasks from the BioNLP Shared Task 2011. Conclusions The proposed method allows the training of a wide-coverage, state-of-the-art event extraction system from multiple corpora with partial semantic annotation overlap. The resulting single model makes broad-coverage extraction straightforward in practice by removing the need to either select a subset of compatible corpora or semantic types, or to merge results from several models trained on different individual corpora. Multi-corpus learning also allows annotation efforts to focus on covering additional semantic types, rather than aiming for exhaustive coverage in any single annotation effort, or extending the coverage of semantic types annotated in existing corpora. PMID:23731785

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

PubMed

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts.

PubMed

Fu, Kun; Jin, Junqi; Cui, Runpeng; Sha, Fei; Zhang, Changshui

2017-12-01

Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sentences. In this paper, we propose an image captioning system that exploits the parallel structures between images and sentences. In our model, the process of generating the next word, given the previously generated ones, is aligned with the visual perception experience where the attention shifts among the visual regions-such transitions impose a thread of ordering in visual perception. This alignment characterizes the flow of latent meaning, which encodes what is semantically shared by both the visual scene and the text description. Our system also makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image. The contexts adapt language models for word generation to specific scene types. We benchmark our system and contrast to published results on several popular datasets, using both automatic evaluation metrics and human evaluation. We show that either region-based attention or scene-specific contexts improves systems without those components. Furthermore, combining these two modeling ingredients attains the state-of-the-art performance.

[Linked Data as a tool in the nutrition domain].

PubMed

Míguez Pérez, R; Santos Gago, J M; Alonso Rorís, V M; Álvarez Sabucedo, L M; Mikic Fonte, F A

2012-01-01

Currently, there is a huge amount of information available on Internet that can neither be interpreted nor used by software agents. This fact poses a serious drawback to the potential of tools that deal with data on the current Web. Nevertheless, in recent times, advances in the domain of Semantic Web make possible the development of a new generation of smart applications capable of creating added-value services for the final user. This work shows the technical challenges that must be faced in the area of nutrition in order to transform one or several oldfashion sources of raw data into a web repository based on semantic technologies and linked with external and publicly available data on Internet. This approach makes possible for automatic tools to operate on the top of this information providing new functionalities highly interesting in the domain of public health, such as the automatic generation of menus for children or intelligent dietetic assistants, among others. This article explains the process to create such information support applying the guidelines of the Linked Data initiative and provides insights into the use of tools to make the most of this technology for its adoption in related use cases and environments.

Social priming of dyslexia and reduction of the Stroop effect: what component of the Stroop effect is actually reduced?

PubMed

Augustinova, Maria; Ferrand, Ludovic

2014-03-01

Recently, Goldfarb, Aisenberg, and Henik (2011) showed that in a manual format of the Stroop task, dyslexia priming eliminates the normal magnitude of the interference-based Stroop-like findings otherwise exhibited by individuals participating in such research. Goldfarb et al. (2011) consequently concluded that the effect of word reading in a Stroop task (i.e., one automatic behavior) can be effectively controlled through an automatic instruction "do not read" (i.e., another automatic behavior). The present study further investigated these ideas by examining when and how dyslexia priming controls different processes involved in a Stroop task. To this end, the original finding was first replicated (Experiment 1) and subsequently extended to the vocal (instead of manual) response modality to examine whether previously reported eliminations of the Stroop effect persist with this response format (i.e., format producing larger Stroop effects). Since past work (e.g., Augustinova & Ferrand, 2012a; Brown, Joneleit et al., 2002; Ferrand & Augustinova, 2013) had suggested that various interventions were likely to reduce (rather than eliminate) the interference-based Stroop-like findings with vocal responses, a further aim of these experiments was to identify the component of these findings that dyslexia priming actually reduces. To this end, the effects of this intervention were examined in a more fine-grained variant of the Stroop task that distinguished between interference resulting from task-irrelevant processes involved in computing the lexical and semantic representations of the word (i.e., a written distractor to ignore) and task-relevant processes involved in the selection of a response (i.e., a color target to name) that are both involved in this task. In line with our past work (e.g., Augustinova & Ferrand, 2012a; Ferrand & Augustinova, 2013), the results of two experiments (Experiments 2 and 3) showed that in the vocal format, dyslexia priming reduces but does not eliminate the normal magnitude of the interference-based Stroop-like findings and that this reduction is solely due to the control of processes involved in the selection of a response (i.e., a color target to name) - processes that are known to be controllable in this format (Ferrand & Augustinova, 2013). Given that in this format, dyslexia priming had no effect on task-irrelevant processes involved in computing the lexical and semantic representations of a written distractor to be ignored - processes that are known to be automatic - further implications for the control of automatic processes via dyslexia priming are considered and an interpretation in terms of a unitary control mechanism for both the manual and vocal formats is proposed. Copyright © 2013 Elsevier B.V. All rights reserved.

Emotion regulation, attention to emotion, and the ventral attentional network

PubMed Central

Viviani, Roberto

2013-01-01

Accounts of the effect of emotional information on behavioral response and current models of emotion regulation are based on two opposed but interacting processes: automatic bottom-up processes (triggered by emotionally arousing stimuli) and top-down control processes (mapped to prefrontal cortical areas). Data on the existence of a third attentional network operating without recourse to limited-capacity processes but influencing response raise the issue of how it is integrated in emotion regulation. We summarize here data from attention to emotion, voluntary emotion regulation, and on the origin of biases against negative content suggesting that the ventral network is modulated by exposure to emotional stimuli when the task does not constrain the handling of emotional content. In the parietal lobes, preferential activation of ventral areas associated with “bottom-up” attention by ventral network theorists is strongest in studies of cognitive reappraisal. In conditions when no explicit instruction is given to change one's response to emotional stimuli, control of emotionally arousing stimuli is observed without concomitant activation of the dorsal attentional network, replaced by a shift of activation toward ventral areas. In contrast, in studies where emotional stimuli are placed in the role of distracter, the observed deactivation of these ventral semantic association areas is consistent with the existence of proactive control on the role emotional representations are allowed to take in generating response. It is here argued that attentional orienting mechanisms located in the ventral network constitute an intermediate kind of process, with features only partially in common with effortful and automatic processes, which plays an important role in handling emotion by conveying the influence of semantic networks, with which the ventral network is co-localized. Current neuroimaging work in emotion regulation has neglected this system by focusing on a bottom-up/top-down dichotomy of attentional control. PMID:24223546

User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models

DTIC Science & Technology

2013-01-01

Conference on, pages 3677–3680. IEEE, 2011. [13] W. Zhang and J. Kosecka. Image based localization in urban environments. In 3D Data Processing...non- urban environments such as deserts. Our system generates synthetic skyline views from a DEM and extracts stable concavity-based features from these...fine as 100m2. 1. Introduction Automatic geolocation of imagery has many exciting use cases. For example, such a tool could semantically orga- nize

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

PubMed Central

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

2017-01-01

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

Supporting Semantic Annotation of Educational Content by Automatic Extraction of Hierarchical Domain Relationships

ERIC Educational Resources Information Center

Vrablecová, Petra; Šimko, Marián

2016-01-01

The domain model is an essential part of an adaptive learning system. For each educational course, it involves educational content and semantics, which is also viewed as a form of conceptual metadata about educational content. Due to the size of a domain model, manual domain model creation is a challenging and demanding task for teachers or…

Impact of Machine-Translated Text on Entity and Relationship Extraction

DTIC Science & Technology

2014-12-01

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

Automatic Generation of Mashups for Personalized Commerce in Digital TV by Semantic Reasoning

NASA Astrophysics Data System (ADS)

Blanco-Fernández, Yolanda; López-Nores, Martín; Pazos-Arias, José J.; Martín-Vicente, Manuela I.

The evolution of information technologies is consolidating recommender systems as essential tools in e-commerce. To date, these systems have focused on discovering the items that best match the preferences, interests and needs of individual users, to end up listing those items by decreasing relevance in some menus. In this paper, we propose extending the current scope of recommender systems to better support trading activities, by automatically generating interactive applications that provide the users with personalized commercial functionalities related to the selected items. We explore this idea in the context of Digital TV advertising, with a system that brings together semantic reasoning techniques and new architectural solutions for web services and mashups.

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.

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

PubMed Central

Wang, Zhenzhu; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm. PMID:28421125

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning.

PubMed

Zhou, Wei; Wu, Chengdong; Chen, Dali; Wang, Zhenzhu; Yi, Yugen; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data

NASA Astrophysics Data System (ADS)

Zhang, Xiuyuan; Du, Shihong; Wang, Qiao

2017-10-01

As the basic units of urban areas, functional zones are essential for city planning and management, but functional-zone maps are hardly available in most cities, as traditional urban investigations focus mainly on land-cover objects instead of functional zones. As a result, an automatic/semi-automatic method for mapping urban functional zones is highly required. Hierarchical semantic cognition (HSC) is presented in this study, and serves as a general cognition structure for recognizing urban functional zones. Unlike traditional classification methods, the HSC relies on geographic cognition and considers four semantic layers, i.e., visual features, object categories, spatial object patterns, and zone functions, as well as their hierarchical relations. Here, we used HSC to classify functional zones in Beijing with a very-high-resolution (VHR) satellite image and point-of-interest (POI) data. Experimental results indicate that this method can produce more accurate results than Support Vector Machine (SVM) and Latent Dirichlet Allocation (LDA) with a larger overall accuracy of 90.8%. Additionally, the contributions of diverse semantic layers are quantified: the object-category layer is the most important and makes 54% contribution to functional-zone classification; while, other semantic layers are less important but their contributions cannot be ignored. Consequently, the presented HSC is effective in classifying urban functional zones, and can further support urban planning and management.

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.

An Experiment in Scientific Program Understanding

NASA Technical Reports Server (NTRS)

Stewart, Mark E. M.; Owen, Karl (Technical Monitor)

2000-01-01

This paper concerns a procedure that analyzes aspects of the meaning or semantics of scientific and engineering code. This procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and parsing this annotated code using multiple, independent expert parsers. These semantic parsers encode domain knowledge and recognize formulae in different disciplines including physics, numerical methods, mathematics, and geometry. The parsers will automatically recognize and document some static, semantic concepts and help locate some program semantic errors. Results are shown for three intensively studied codes and seven blind test cases; all test cases are state of the art scientific codes. These techniques may apply to a wider range of scientific codes. If so, the techniques could reduce the time, risk, and effort required to develop and modify scientific codes.

Automatic theory generation from analyst text files using coherence networks

NASA Astrophysics Data System (ADS)

Shaffer, Steven C.

2014-05-01

This paper describes a three-phase process of extracting knowledge from analyst textual reports. Phase 1 involves performing natural language processing on the source text to extract subject-predicate-object triples. In phase 2, these triples are then fed into a coherence network analysis process, using a genetic algorithm optimization. Finally, the highest-value sub networks are processed into a semantic network graph for display. Initial work on a well- known data set (a Wikipedia article on Abraham Lincoln) has shown excellent results without any specific tuning. Next, we ran the process on the SYNthetic Counter-INsurgency (SYNCOIN) data set, developed at Penn State, yielding interesting and potentially useful results.

Ontology based heterogeneous materials database integration and semantic query

NASA Astrophysics Data System (ADS)

Zhao, Shuai; Qian, Quan

2017-10-01

Materials digital data, high throughput experiments and high throughput computations are regarded as three key pillars of materials genome initiatives. With the fast growth of materials data, the integration and sharing of data is very urgent, that has gradually become a hot topic of materials informatics. Due to the lack of semantic description, it is difficult to integrate data deeply in semantic level when adopting the conventional heterogeneous database integration approaches such as federal database or data warehouse. In this paper, a semantic integration method is proposed to create the semantic ontology by extracting the database schema semi-automatically. Other heterogeneous databases are integrated to the ontology by means of relational algebra and the rooted graph. Based on integrated ontology, semantic query can be done using SPARQL. During the experiments, two world famous First Principle Computational databases, OQMD and Materials Project are used as the integration targets, which show the availability and effectiveness of our method.

Semi-Supervised Learning to Identify UMLS Semantic Relations.

PubMed

Luo, Yuan; Uzuner, Ozlem

2014-01-01

The UMLS Semantic Network is constructed by experts and requires periodic expert review to update. We propose and implement a semi-supervised approach for automatically identifying UMLS semantic relations from narrative text in PubMed. Our method analyzes biomedical narrative text to collect semantic entity pairs, and extracts multiple semantic, syntactic and orthographic features for the collected pairs. We experiment with seeded k-means clustering with various distance metrics. We create and annotate a ground truth corpus according to the top two levels of the UMLS semantic relation hierarchy. We evaluate our system on this corpus and characterize the learning curves of different clustering configuration. Using KL divergence consistently performs the best on the held-out test data. With full seeding, we obtain macro-averaged F-measures above 70% for clustering the top level UMLS relations (2-way), and above 50% for clustering the second level relations (7-way).

How to Modify (Implicit) Evaluations of Fear-Related Stimuli: Effects of Feature-Specific Attention Allocation

PubMed Central

Vanaelst, Jolien; Spruyt, Adriaan; De Houwer, Jan

2016-01-01

We demonstrate that feature-specific attention allocation influences the way in which repeated exposure modulates implicit and explicit evaluations toward fear-related stimuli. During an exposure procedure, participants were encouraged to assign selective attention either to the evaluative meaning (i.e., Evaluative Condition) or a non-evaluative, semantic feature (i.e., Semantic Condition) of fear-related stimuli. The influence of the exposure procedure was captured by means of a measure of implicit evaluation, explicit evaluative ratings, and a measure of automatic approach/avoidance tendencies. As predicted, the implicit measure of evaluation revealed a reduced expression of evaluations in the Semantic Condition as compared to the Evaluative Condition. Moreover, this effect generalized toward novel objects that were never presented during the exposure procedure. The explicit measure of evaluation mimicked this effect, although it failed to reach conventional levels of statistical significance. No effects were found in terms of automatic approach/avoidance tendencies. Potential implications for the treatment of anxiety disorders are discussed. PMID:27242626

Automatic response activation in sequential affective priming: an ERP study

PubMed Central

Leuthold, Hartmut; Rothermund, Klaus; Schweinberger, Stefan R.

2012-01-01

Affective priming effects denote faster responses when two successively presented affective stimuli match in valence than when they mismatch. Two mechanisms have been proposed for their explanation: (i) Priming of affective information within a semantic network or distributed memory system (semantic priming). (ii) Automatic activation of the evaluative response through the affective prime (response priming). In this experiment, we sought more direct evidence for prime-induced response activations with measurement of the lateralized readiness potential (LRP). Onset of the stimulus-locked LRP was earlier in affectively congruent trials than in incongruent trials. In addition, priming modulated the LRP-amplitude of slow responses, indicating greater activation of the incorrect response hand in affectively incongruent trials. Onset of the response-locked LRP and peak latency of the P300 component were not modulated by priming but the amplitude of the N400 component was. In combination, these results suggest that both, semantic priming and response priming constitute affective priming effects in the evaluative categorization task. PMID:21642351

How to Modify (Implicit) Evaluations of Fear-Related Stimuli: Effects of Feature-Specific Attention Allocation.

PubMed

Vanaelst, Jolien; Spruyt, Adriaan; De Houwer, Jan

2016-01-01

We demonstrate that feature-specific attention allocation influences the way in which repeated exposure modulates implicit and explicit evaluations toward fear-related stimuli. During an exposure procedure, participants were encouraged to assign selective attention either to the evaluative meaning (i.e., Evaluative Condition) or a non-evaluative, semantic feature (i.e., Semantic Condition) of fear-related stimuli. The influence of the exposure procedure was captured by means of a measure of implicit evaluation, explicit evaluative ratings, and a measure of automatic approach/avoidance tendencies. As predicted, the implicit measure of evaluation revealed a reduced expression of evaluations in the Semantic Condition as compared to the Evaluative Condition. Moreover, this effect generalized toward novel objects that were never presented during the exposure procedure. The explicit measure of evaluation mimicked this effect, although it failed to reach conventional levels of statistical significance. No effects were found in terms of automatic approach/avoidance tendencies. Potential implications for the treatment of anxiety disorders are discussed.

Electrophysiological evidence for early perceptual facilitation and efficient categorization of self-related stimuli during an Implicit Association Test measuring neuroticism.

PubMed

Fleischhauer, Monika; Strobel, Alexander; Diers, Kersten; Enge, Sören

2014-02-01

The Implicit Association Test (IAT) is a widely used latency-based categorization task that indirectly measures the strength of automatic associations between target and attribute concepts. So far, little is known about the perceptual and cognitive processes underlying personality IATs. Thus, the present study examined event-related potential indices during the execution of an IAT measuring neuroticism (N  =  70). The IAT effect was strongly modulated by the P1 component indicating early facilitation of relevant visual input and by a P3b-like late positive component reflecting the efficacy of stimulus categorization. Both components covaried, and larger amplitudes led to faster responses. The results suggest a relationship between early perceptual and semantic processes operating at a more automatic, implicit level and later decision-related categorization of self-relevant stimuli contributing to the IAT effect. Copyright © 2013 Society for Psychophysiological Research.

Ontology construction and application in practice case study of health tourism in Thailand.

PubMed

Chantrapornchai, Chantana; Choksuchat, Chidchanok

2016-01-01

Ontology is one of the key components in semantic webs. It contains the core knowledge for an effective search. However, building ontology requires the carefully-collected knowledge which is very domain-sensitive. In this work, we present the practice of ontology construction for a case study of health tourism in Thailand. The whole process follows the METHONTOLOGY approach, which consists of phases: information gathering, corpus study, ontology engineering, evaluation, publishing, and the application construction. Different sources of data such as structure web documents like HTML and other documents are acquired in the information gathering process. The tourism corpora from various tourism texts and standards are explored. The ontology is evaluated in two aspects: automatic reasoning using Pellet, and RacerPro, and the questionnaires, used to evaluate by experts of the domains: tourism domain experts and ontology experts. The ontology usability is demonstrated via the semantic web application and via example axioms. The developed ontology is actually the first health tourism ontology in Thailand with the published application.

Ontology-Based Exchange and Immediate Application of Business Calculation Definitions for Online Analytical Processing

NASA Astrophysics Data System (ADS)

Kehlenbeck, Matthias; Breitner, Michael H.

Business users define calculated facts based on the dimensions and facts contained in a data warehouse. These business calculation definitions contain necessary knowledge regarding quantitative relations for deep analyses and for the production of meaningful reports. The business calculation definitions are implementation and widely organization independent. But no automated procedures facilitating their exchange across organization and implementation boundaries exist. Separately each organization currently has to map its own business calculations to analysis and reporting tools. This paper presents an innovative approach based on standard Semantic Web technologies. This approach facilitates the exchange of business calculation definitions and allows for their automatic linking to specific data warehouses through semantic reasoning. A novel standard proxy server which enables the immediate application of exchanged definitions is introduced. Benefits of the approach are shown in a comprehensive case study.

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.

Point Cloud Classification of Tesserae from Terrestrial Laser Data Combined with Dense Image Matching for Archaeological Information Extraction

NASA Astrophysics Data System (ADS)

Poux, F.; Neuville, R.; Billen, R.

2017-08-01

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

Simultenious binary hash and features learning for image retrieval

NASA Astrophysics Data System (ADS)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Semantic Web repositories for genomics data using the eXframe platform.

PubMed

Merrill, Emily; Corlosquet, Stéphane; Ciccarese, Paolo; Clark, Tim; Das, Sudeshna

2014-01-01

With the advent of inexpensive assay technologies, there has been an unprecedented growth in genomics data as well as the number of databases in which it is stored. In these databases, sample annotation using ontologies and controlled vocabularies is becoming more common. However, the annotation is rarely available as Linked Data, in a machine-readable format, or for standardized queries using SPARQL. This makes large-scale reuse, or integration with other knowledge bases very difficult. To address this challenge, we have developed the second generation of our eXframe platform, a reusable framework for creating online repositories of genomics experiments. This second generation model now publishes Semantic Web data. To accomplish this, we created an experiment model that covers provenance, citations, external links, assays, biomaterials used in the experiment, and the data collected during the process. The elements of our model are mapped to classes and properties from various established biomedical ontologies. Resource Description Framework (RDF) data is automatically produced using these mappings and indexed in an RDF store with a built-in Sparql Protocol and RDF Query Language (SPARQL) endpoint. Using the open-source eXframe software, institutions and laboratories can create Semantic Web repositories of their experiments, integrate it with heterogeneous resources and make it interoperable with the vast Semantic Web of biomedical knowledge.

Knowledge representation and management: towards an integration of a semantic web in daily health practice.

PubMed

Griffon, N; Charlet, J; Darmoni, Sj

2013-01-01

To summarize the best papers in the field of Knowledge Representation and Management (KRM). A synopsis of the four selected articles for the IMIA Yearbook 2013 KRM section is provided, as well as highlights of current KRM trends, in particular, of the semantic web in daily health practice. The manual selection was performed in three stages: first a set of 3,106 articles, then a second set of 86 articles followed by a third set of 15 articles, and finally the last set of four chosen articles. Among the four selected articles (see Table 1), one focuses on knowledge engineering to prevent adverse drug events; the objective of the second is to propose mappings between clinical archetypes and SNOMED CT in the context of clinical practice; the third presents an ontology to create a question-answering system; the fourth describes a biomonitoring network based on semantic web technologies. These four articles clearly indicate that the health semantic web has become a part of daily practice of health professionals since 2012. In the review of the second set of 86 articles, the same topics included in the previous IMIA yearbook remain active research fields: Knowledge extraction, automatic indexing, information retrieval, natural language processing, management of health terminologies and ontologies.

Disentangling Genuine Semantic Stroop Effects in Reading from Contingency Effects: On the Need for Two Neutral Baselines

PubMed Central

Lorentz, Eric; McKibben, Tessa; Ekstrand, Chelsea; Gould, Layla; Anton, Kathryn; Borowsky, Ron

2016-01-01

The automaticity of reading is often explored through the Stroop effect, whereby color-naming is affected by color words. Color associates (e.g., “sky”) also produce a Stroop effect, suggesting that automatic reading occurs through to the level of semantics, even when reading sub-lexically (e.g., the pseudohomophone “skigh”). However, several previous experiments have confounded congruency with contingency learning, whereby faster responding occurs for more frequent stimuli. Contingency effects reflect a higher frequency-pairing of the word with a font color in the congruent condition than in the incongruent condition due to the limited set of congruent pairings. To determine the extent to which the Stroop effect can be attributed to contingency learning of font colors paired with lexical (word-level) and sub-lexical (phonetically decoded) letter strings, as well as assess facilitation and interference relative to contingency effects, we developed two neutral baselines: each one matched on pair-frequency for congruent and incongruent color words. In Experiments 1 and 3, color words (e.g., “blue”) and their pseudohomophones (e.g., “bloo”) produced significant facilitation and interference relative to neutral baselines, regardless of whether the onset (i.e., first phoneme) was matched to the color words. Color associates (e.g., “ocean”) and their pseudohomophones (e.g., “oshin”), however, showed no significant facilitation or interference relative to onset matched neutral baselines (Experiment 2). When onsets were unmatched, color associate words produced consistent facilitation on RT (e.g., “ocean” vs. “dozen”), but pseudohomophones (e.g., “oshin” vs. “duhzen”) failed to produce facilitation or interference. Our findings suggest that the Stroop effects for color and associated stimuli are sensitive to the type of neutral baseline used, as well as stimulus type (word vs. pseudohomophone). In general, contingency learning plays a large role when repeating congruent items more than incongruent items, but appropriate pair-frequency matched neutral baselines allow for the assessment of genuine facilitation and interference. Using such baselines, we found reading processes proceed to a semantic level for familiar words, but not pseudohomophones (i.e., phonetic decoding). Such assessment is critical for separating the effects of genuine congruency from contingency during automatic word reading in the Stroop task, and when used with color associates, isolates the semantic contribution. PMID:27014177

Disentangling Genuine Semantic Stroop Effects in Reading from Contingency Effects: On the Need for Two Neutral Baselines.

PubMed

Lorentz, Eric; McKibben, Tessa; Ekstrand, Chelsea; Gould, Layla; Anton, Kathryn; Borowsky, Ron

2016-01-01

The automaticity of reading is often explored through the Stroop effect, whereby color-naming is affected by color words. Color associates (e.g., "sky") also produce a Stroop effect, suggesting that automatic reading occurs through to the level of semantics, even when reading sub-lexically (e.g., the pseudohomophone "skigh"). However, several previous experiments have confounded congruency with contingency learning, whereby faster responding occurs for more frequent stimuli. Contingency effects reflect a higher frequency-pairing of the word with a font color in the congruent condition than in the incongruent condition due to the limited set of congruent pairings. To determine the extent to which the Stroop effect can be attributed to contingency learning of font colors paired with lexical (word-level) and sub-lexical (phonetically decoded) letter strings, as well as assess facilitation and interference relative to contingency effects, we developed two neutral baselines: each one matched on pair-frequency for congruent and incongruent color words. In Experiments 1 and 3, color words (e.g., "blue") and their pseudohomophones (e.g., "bloo") produced significant facilitation and interference relative to neutral baselines, regardless of whether the onset (i.e., first phoneme) was matched to the color words. Color associates (e.g., "ocean") and their pseudohomophones (e.g., "oshin"), however, showed no significant facilitation or interference relative to onset matched neutral baselines (Experiment 2). When onsets were unmatched, color associate words produced consistent facilitation on RT (e.g., "ocean" vs. "dozen"), but pseudohomophones (e.g., "oshin" vs. "duhzen") failed to produce facilitation or interference. Our findings suggest that the Stroop effects for color and associated stimuli are sensitive to the type of neutral baseline used, as well as stimulus type (word vs. pseudohomophone). In general, contingency learning plays a large role when repeating congruent items more than incongruent items, but appropriate pair-frequency matched neutral baselines allow for the assessment of genuine facilitation and interference. Using such baselines, we found reading processes proceed to a semantic level for familiar words, but not pseudohomophones (i.e., phonetic decoding). Such assessment is critical for separating the effects of genuine congruency from contingency during automatic word reading in the Stroop task, and when used with color associates, isolates the semantic contribution.

SPARK: Adapting Keyword Query to Semantic Search

NASA Astrophysics Data System (ADS)

Zhou, Qi; Wang, Chong; Xiong, Miao; Wang, Haofen; Yu, Yong

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.

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.

The Nature of Indexing: How Humans and Machines Analyze Messages and Texts for Retrieval. Part II: Machine Indexing, and the Allocation of Human versus Machine Effort.

ERIC Educational Resources Information Center

Anderson, James D.; Perez-Carballo, Jose

2001-01-01

Discussion of human intellectual indexing versus automatic indexing focuses on automatic indexing. Topics include keyword indexing; negative vocabulary control; counting words; comparative counting and weighting; stemming; words versus phrases; clustering; latent semantic indexing; citation indexes; bibliographic coupling; co-citation; relevance…

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

Biomedical question answering using semantic relations.

PubMed

Hristovski, Dimitar; Dinevski, Dejan; Kastrin, Andrej; Rindflesch, Thomas C

2015-01-16

The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature. We extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si ). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%). In this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.

Advancing translational research with the Semantic Web.

PubMed

Ruttenberg, Alan; Clark, Tim; Bug, William; Samwald, Matthias; Bodenreider, Olivier; Chen, Helen; Doherty, Donald; Forsberg, Kerstin; Gao, Yong; Kashyap, Vipul; Kinoshita, June; Luciano, Joanne; Marshall, M Scott; Ogbuji, Chimezie; Rees, Jonathan; Stephens, Susie; Wong, Gwendolyn T; Wu, Elizabeth; Zaccagnini, Davide; Hongsermeier, Tonya; Neumann, Eric; Herman, Ivan; Cheung, Kei-Hoi

2007-05-09

A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature. We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine. Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.

Advancing translational research with the Semantic Web

PubMed Central

Ruttenberg, Alan; Clark, Tim; Bug, William; Samwald, Matthias; Bodenreider, Olivier; Chen, Helen; Doherty, Donald; Forsberg, Kerstin; Gao, Yong; Kashyap, Vipul; Kinoshita, June; Luciano, Joanne; Marshall, M Scott; Ogbuji, Chimezie; Rees, Jonathan; Stephens, Susie; Wong, Gwendolyn T; Wu, Elizabeth; Zaccagnini, Davide; Hongsermeier, Tonya; Neumann, Eric; Herman, Ivan; Cheung, Kei-Hoi

2007-01-01

Background A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature. Results We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine. Conclusion Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work. PMID:17493285

Semantator: semantic annotator for converting biomedical text to linked data.

PubMed

Tao, Cui; Song, Dezhao; Sharma, Deepak; Chute, Christopher G

2013-10-01

More than 80% of biomedical data is embedded in plain text. The unstructured nature of these text-based documents makes it challenging to easily browse and query the data of interest in them. One approach to facilitate browsing and querying biomedical text is to convert the plain text to a linked web of data, i.e., converting data originally in free text to structured formats with defined meta-level semantics. In this paper, we introduce Semantator (Semantic Annotator), a semantic-web-based environment for annotating data of interest in biomedical documents, browsing and querying the annotated data, and interactively refining annotation results if needed. Through Semantator, information of interest can be either annotated manually or semi-automatically using plug-in information extraction tools. The annotated results will be stored in RDF and can be queried using the SPARQL query language. In addition, semantic reasoners can be directly applied to the annotated data for consistency checking and knowledge inference. Semantator has been released online and was used by the biomedical ontology community who provided positive feedbacks. Our evaluation results indicated that (1) Semantator can perform the annotation functionalities as designed; (2) Semantator can be adopted in real applications in clinical and transactional research; and (3) the annotated results using Semantator can be easily used in Semantic-web-based reasoning tools for further inference. Copyright © 2013 Elsevier Inc. All rights reserved.

New approach for cognitive analysis and understanding of medical patterns and visualizations

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2003-11-01

This paper presents new opportunities for applying linguistic description of the picture merit content and AI methods to undertake tasks of the automatic understanding of images semantics in intelligent medical information systems. A successful obtaining of the crucial semantic content of the medical image may contribute considerably to the creation of new intelligent multimedia cognitive medical systems. Thanks to the new idea of cognitive resonance between stream of the data extracted from the image using linguistic methods and expectations taken from the representaion of the medical knowledge, it is possible to understand the merit content of the image even if teh form of the image is very different from any known pattern. This article proves that structural techniques of artificial intelligence may be applied in the case of tasks related to automatic classification and machine perception based on semantic pattern content in order to determine the semantic meaning of the patterns. In the paper are described some examples presenting ways of applying such techniques in the creation of cognitive vision systems for selected classes of medical images. On the base of scientific research described in the paper we try to build some new systems for collecting, storing, retrieving and intelligent interpreting selected medical images especially obtained in radiological and MRI examinations.

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.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

PubMed Central

Xian, Xuefeng; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost. PMID:28588611

Electrostimulation mapping of comprehension of auditory and visual words.

PubMed

Roux, Franck-Emmanuel; Miskin, Krasimir; Durand, Jean-Baptiste; Sacko, Oumar; Réhault, Emilie; Tanova, Rositsa; Démonet, Jean-François

2015-10-01

In order to spare functional areas during the removal of brain tumours, electrical stimulation mapping was used in 90 patients (77 in the left hemisphere and 13 in the right; 2754 cortical sites tested). Language functions were studied with a special focus on comprehension of auditory and visual words and the semantic system. In addition to naming, patients were asked to perform pointing tasks from auditory and visual stimuli (using sets of 4 different images controlled for familiarity), and also auditory object (sound recognition) and Token test tasks. Ninety-two auditory comprehension interference sites were observed. We found that the process of auditory comprehension involved a few, fine-grained, sub-centimetre cortical territories. Early stages of speech comprehension seem to relate to two posterior regions in the left superior temporal gyrus. Downstream lexical-semantic speech processing and sound analysis involved 2 pathways, along the anterior part of the left superior temporal gyrus, and posteriorly around the supramarginal and middle temporal gyri. Electrostimulation experimentally dissociated perceptual consciousness attached to speech comprehension. The initial word discrimination process can be considered as an "automatic" stage, the attention feedback not being impaired by stimulation as would be the case at the lexical-semantic stage. Multimodal organization of the superior temporal gyrus was also detected since some neurones could be involved in comprehension of visual material and naming. These findings demonstrate a fine graded, sub-centimetre, cortical representation of speech comprehension processing mainly in the left superior temporal gyrus and are in line with those described in dual stream models of language comprehension processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Oscillatory EEG dynamics underlying automatic chunking during sentence processing.

PubMed

Bonhage, Corinna E; Meyer, Lars; Gruber, Thomas; Friederici, Angela D; Mueller, Jutta L

2017-05-15

Sentences are easier to remember than random word sequences, likely because linguistic regularities facilitate chunking of words into meaningful groups. The present electroencephalography study investigated the neural oscillations modulated by this so-called sentence superiority effect during the encoding and maintenance of sentence fragments versus word lists. We hypothesized a chunking-related modulation of neural processing during the encoding and retention of sentences (i.e., sentence fragments) as compared to word lists. Time-frequency analysis revealed a two-fold oscillatory pattern for the memorization of sentences: Sentence encoding was accompanied by higher delta amplitude (4Hz), originating both from regions processing syntax as well as semantics (bilateral superior/middle temporal regions and fusiform gyrus). Subsequent sentence retention was reflected in decreased theta (6Hz) and beta/gamma (27-32Hz) amplitude instead. Notably, whether participants simply read or properly memorized the sentences did not impact chunking-related activity during encoding. Therefore, we argue that the sentence superiority effect is grounded in highly automatized language processing mechanisms, which generate meaningful memory chunks irrespective of task demands. Copyright © 2017 Elsevier Inc. All rights reserved.

Dorsomedial striatum involvement in regulating conflict between current and presumed outcomes.

PubMed

Mestres-Missé, Anna; Bazin, Pierre-Louis; Trampel, Robert; Turner, Robert; Kotz, Sonja A

2014-09-01

The balance between automatic and controlled processing is essential to human flexible but optimal behavior. On the one hand, the automation of habitual behavior and processing is indispensable, and, on the other hand, strategic processing is needed in light of unexpected, conflicting, or new situations. Using ultra-high-field high-resolution functional magnetic resonance imaging (7T-fMRI), the present study examined the role of subcortical structures in mediating this balance. Participants were asked to judge the congruency of sentences containing a semantically ambiguous or unambiguous word. Ambiguous sentences had three possible resolutions: dominant meaning, subordinate meaning, and incongruent. The dominant interpretation represents the most habitual response, whereas both the subordinate and incongruent options clash with this automatic response, and, hence, require cognitive control. Moreover, the subordinate resolution entails a less expected but correct outcome, while the incongruent condition is simply wrong. The current results reveal the involvement of the anterior dorsomedial striatum in modulating and resolving conflict between actual and expected outcomes, and highlight the importance of cortical and subcortical cooperation in this process. Copyright © 2014 Elsevier Inc. All rights reserved.

Semantic mapping reveals distinct patterns in descriptions of social relations in adults with autism spectrum disorder.

PubMed

Luo, Sean X; Shinall, Jacqueline A; Peterson, Bradley S; Gerber, Andrew J

2016-08-01

Adults with autism spectrum disorder (ASD) may describe other individuals differently compared with typical adults. In this study, we first asked participants to describe closely related individuals such as parents and close friends with 10 positive and 10 negative characteristics. We then used standard natural language processing methods to digitize and visualize these descriptions. The complex patterns of these descriptive sentences exhibited a difference in semantic space between individuals with ASD and control participants. Machine learning algorithms were able to automatically detect and discriminate between these two groups. Furthermore, we showed that these descriptive sentences from adults with ASD exhibited fewer connections as defined by word-word co-occurrences in descriptions, and these connections in words formed a less "small-world" like network. Autism Res 2016, 9: 846-853. © 2015 International Society for Autism Research, Wiley Periodicals, Inc. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Conveying the concept of movement in music: An event-related brain potential study.

PubMed

Zhou, Linshu; Jiang, Cunmei; Wu, Yingying; Yang, Yufang

2015-10-01

This study on event-related brain potential investigated whether music can convey the concept of movement. Using a semantic priming paradigm, natural musical excerpts were presented to non-musicians, followed by semantically congruent or incongruent pictures that depicted objects either in motion or at rest. The priming effects were tested in object decision and implicit recognition tasks to distinguish the effects of automatic conceptual activation from response competition. Results showed that in both tasks, pictures that were incongruent to preceding musical excerpts elicited larger N400 than congruent pictures, suggesting that music can prime the representations of movement concepts. Results of the multiple regression analysis showed that movement expression could be well predicted by specific acoustic and musical features, indicating the associations between music per se and the processing of iconic musical meaning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Applied and implied semantics in crystallographic publishing

PubMed Central

2012-01-01

Background Crystallography is a data-rich, software-intensive scientific discipline with a community that has undertaken direct responsibility for publishing its own scientific journals. That community has worked actively to develop information exchange standards allowing readers of structure reports to access directly, and interact with, the scientific content of the articles. Results Structure reports submitted to some journals of the International Union of Crystallography (IUCr) can be automatically validated and published through an efficient and cost-effective workflow. Readers can view and interact with the structures in three-dimensional visualization applications, and can access the experimental data should they wish to perform their own independent structure solution and refinement. The journals also layer on top of this facility a number of automated annotations and interpretations to add further scientific value. Conclusions The benefits of semantically rich information exchange standards have revolutionised the scholarly publishing process for crystallography, and establish a model relevant to many other physical science disciplines. PMID:22932420

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback

PubMed Central

Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery. PMID:27861505

Semantic Web Service Delivery in Healthcare Based on Functional and Non-Functional Properties.

PubMed

Schweitzer, Marco; Gorfer, Thilo; Hörbst, Alexander

2017-01-01

In the past decades, a lot of endeavor has been made on the trans-institutional exchange of healthcare data through electronic health records (EHR) in order to obtain a lifelong, shared accessible health record of a patient. Besides basic information exchange, there is a growing need for Information and Communication Technology (ICT) to support the use of the collected health data in an individual, case-specific workflow-based manner. This paper presents the results on how workflows can be used to process data from electronic health records, following a semantic web service approach that enables automatic discovery, composition and invocation of suitable web services. Based on this solution, the user (physician) can define its needs from a domain-specific perspective, whereas the ICT-system fulfills those needs with modular web services. By involving also non-functional properties for the service selection, this approach is even more suitable for the dynamic medical domain.

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback.

PubMed

Hu, Kai; Gui, Zhipeng; Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery.

Toward image phylogeny forests: automatically recovering semantically similar image relationships.

PubMed

Dias, Zanoni; Goldenstein, Siome; Rocha, Anderson

2013-09-10

In the past few years, several near-duplicate detection methods appeared in the literature to identify the cohabiting versions of a given document online. Following this trend, there are some initial attempts to go beyond the detection task, and look into the structure of evolution within a set of related images overtime. In this paper, we aim at automatically identify the structure of relationships underlying the images, correctly reconstruct their past history and ancestry information, and group them in distinct trees of processing history. We introduce a new algorithm that automatically handles sets of images comprising different related images, and outputs the phylogeny trees (also known as a forest) associated with them. Image phylogeny algorithms have many applications such as finding the first image within a set posted online (useful for tracking copyright infringement perpetrators), hint at child pornography content creators, and narrowing down a list of suspects for online harassment using photographs. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Experiments in automatic word class and word sense identification for information retrieval

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

Gauch, S.; Futrelle, R.P.

Automatic identification of related words and automatic detection of word senses are two long-standing goals of researchers in natural language processing. Word class information and word sense identification may enhance the performance of information retrieval system4ms. Large online corpora and increased computational capabilities make new techniques based on corpus linguisitics feasible. Corpus-based analysis is especially needed for corpora from specialized fields for which no electronic dictionaries or thesauri exist. The methods described here use a combination of mutual information and word context to establish word similarities. Then, unsupervised classification is done using clustering in the word space, identifying word classesmore » without pretagging. We also describe an extension of the method to handle the difficult problems of disambiguation and of determining part-of-speech and semantic information for low-frequency words. The method is powerful enough to produce high-quality results on a small corpus of 200,000 words from abstracts in a field of molecular biology.« less

Modeling loosely annotated images using both given and imagined annotations

NASA Astrophysics Data System (ADS)

Tang, Hong; Boujemaa, Nozha; Chen, Yunhao; Deng, Lei

2011-12-01

In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning a topic model. In particular, some ``imagined'' keywords are poured into the incomplete annotation through measuring similarity between keywords in terms of their co-occurrence. Then, both given and imagined annotations are employed to learn probabilistic topic models for automatically annotating new images. We conduct experiments on two image databases (i.e., Corel and ESP) coupled with their loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods. The proposed method improves word-driven probability latent semantic analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.

Model Checking Abstract PLEXIL Programs with SMART

NASA Technical Reports Server (NTRS)

Siminiceanu, Radu I.

2007-01-01

We describe a method to automatically generate discrete-state models of abstract Plan Execution Interchange Language (PLEXIL) programs that can be analyzed using model checking tools. Starting from a high-level description of a PLEXIL program or a family of programs with common characteristics, the generator lays the framework that models the principles of program execution. The concrete parts of the program are not automatically generated, but require the modeler to introduce them by hand. As a case study, we generate models to verify properties of the PLEXIL macro constructs that are introduced as shorthand notation. After an exhaustive analysis, we conclude that the macro definitions obey the intended semantics and behave as expected, but contingently on a few specific requirements on the timing semantics of micro-steps in the concrete executive implementation.

Confusing similar words: ERP correlates of lexical-semantic processing in first language attrition and late second language acquisition.

PubMed

Kasparian, Kristina; Steinhauer, Karsten

2016-12-01

First language (L1) attrition is a socio-linguistic circumstance where second language (L2) learning coincides with changes in exposure and use of the native-L1. Attriters often report experiencing a decline in automaticity or proficiency in their L1 after a prolonged period in the L2 environment, while their L2 proficiency continues to strengthen. Investigating the neurocognitive correlates of attrition alongside those of late L2 acquisition addresses the question of whether the brain mechanisms underlying both L1 and L2 processing are strongly determined by proficiency, irrespective of whether the language was acquired from birth or in adulthood. Using event-related-potentials (ERPs), we examined lexical-semantic processing in Italian L1 attriters, compared to adult Italian L2 learners and to Italian monolingual native speakers. We contrasted the processing of classical lexical-semantic violations (Mismatch condition) with sentences that were equally semantically implausible but arguably trickier, as the target-noun was "swapped" with an orthographic neighbor that differed only in its final vowel and gender-marking morpheme (e.g., cappello (hat) vs. cappella (chapel)). Our aim was to determine whether sentences with such "confusable nouns" (Swap condition) would be processed as semantically correct by late L2 learners and L1 attriters, especially for those individuals with lower Italian proficiency scores. We found that lower-proficiency Italian speakers did not show significant N400 effects for Swap violations relative to correct sentences, regardless of whether Italian was the L1 or the L2. Crucially, N400 response profiles followed a continuum of "nativelikeness" predicted by Italian proficiency scores - high-proficiency attriters and high-proficiency Italian learners were indistinguishable from native controls, whereas attriters and L2 learners in the lower-proficiency range showed significantly reduced N400 effects for "Swap" errors. Importantly, attriters and late L2 learners did not differ in their N400 responses when they belonged to the same proficiency subgroup. Attriters also showed an enhanced P600 response to both kinds of lexical-semantic anomalies, which we discuss as reflecting increased conflict-monitoring and conscious "second thought" processes specifically in attriters. Our findings provide some of the first ERP evidence of attrition effects, and are compatible with accounts of ongoing neuroplasticity for language in adulthood. Proficiency, rather than age-of-acquisition, seems to be the key factor in modulating certain neurocognitive responses, not only within L2 learners but also in L1 attriters. Copyright © 2016 Elsevier Ltd. All rights reserved.

Semantic SenseLab: implementing the vision of the Semantic Web in neuroscience

PubMed Central

Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi

2011-01-01

Summary Objective Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Methods Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. Conclusion We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/ PMID:20006477

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience.

PubMed

Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi

2010-01-01

Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/. 2009 Elsevier B.V. All rights reserved.

Semantic Web repositories for genomics data using the eXframe platform

PubMed Central

2014-01-01

Background With the advent of inexpensive assay technologies, there has been an unprecedented growth in genomics data as well as the number of databases in which it is stored. In these databases, sample annotation using ontologies and controlled vocabularies is becoming more common. However, the annotation is rarely available as Linked Data, in a machine-readable format, or for standardized queries using SPARQL. This makes large-scale reuse, or integration with other knowledge bases very difficult. Methods To address this challenge, we have developed the second generation of our eXframe platform, a reusable framework for creating online repositories of genomics experiments. This second generation model now publishes Semantic Web data. To accomplish this, we created an experiment model that covers provenance, citations, external links, assays, biomaterials used in the experiment, and the data collected during the process. The elements of our model are mapped to classes and properties from various established biomedical ontologies. Resource Description Framework (RDF) data is automatically produced using these mappings and indexed in an RDF store with a built-in Sparql Protocol and RDF Query Language (SPARQL) endpoint. Conclusions Using the open-source eXframe software, institutions and laboratories can create Semantic Web repositories of their experiments, integrate it with heterogeneous resources and make it interoperable with the vast Semantic Web of biomedical knowledge. PMID:25093072

Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features.

PubMed

Zhu, Feida; Yan, Zhicheng; Bu, Jiajun; Yu, Yizhou

2017-05-10

Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we first propose a novel deep learning architecture for exemplar-based image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture consists of fully convolutional networks (FCNs) for automatic semantics-aware feature extraction and fully connected neural layers for adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. To extend our deep network from image stylization to video stylization, we exploit temporal superpixels (TSPs) to facilitate the transfer of artistic styles from image exemplars to videos. Experiments on a number of datasets for image stylization as well as a diverse set of video clips demonstrate the effectiveness of our deep learning architecture.

Linking DICOM pixel data with radiology reports using automatic semantic annotation

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Kim, Woojin; Munasinghe, Indeera; Criminisi, Antonio; White, Steve; Siddiqui, Khan

2012-02-01

Improved access to DICOM studies to both physicians and patients is changing the ways medical imaging studies are visualized and interpreted beyond the confines of radiologists' PACS workstations. While radiologists are trained for viewing and image interpretation, a non-radiologist physician relies on the radiologists' reports. Consequently, patients historically have been typically informed about their imaging findings via oral communication with their physicians, even though clinical studies have shown that patients respond to physician's advice significantly better when the individual patients are shown their own actual data. Our previous work on automated semantic annotation of DICOM Computed Tomography (CT) images allows us to further link radiology report with the corresponding images, enabling us to bridge the gap between image data with the human interpreted textual description of the corresponding imaging studies. The mapping of radiology text is facilitated by natural language processing (NLP) based search application. When combined with our automated semantic annotation of images, it enables navigation in large DICOM studies by clicking hyperlinked text in the radiology reports. An added advantage of using semantic annotation is the ability to render the organs to their default window level setting thus eliminating another barrier to image sharing and distribution. We believe such approaches would potentially enable the consumer to have access to their imaging data and navigate them in an informed manner.

A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC

PubMed Central

Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich

2015-01-01

Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. PMID:25948699

Modeling semantic aspects for cross-media image indexing.

PubMed

Monay, Florent; Gatica-Perez, Daniel

2007-10-01

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework.

Algorithmic Classification of Five Characteristic Types of Paraphasias.

PubMed

Fergadiotis, Gerasimos; Gorman, Kyle; Bedrick, Steven

2016-12-01

This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions. Then we implemented a phonological-similarity algorithm to identify phonologically related real-word errors. Last, we assessed the performance of a semantic-similarity criterion that was based on word2vec (Mikolov, Yih, & Zweig, 2013). Overall, the algorithmic classification replicated human scoring for the major categories of paraphasias studied with high accuracy. The tool that was based on the SUBTLEXus frequency norms was more than 97% accurate in making lexicality judgments. The phonological-similarity criterion was approximately 91% accurate, and the overall classification accuracy of the semantic classifier ranged from 86% to 90%. Overall, the results highlight the potential of tools from the field of natural language processing for the development of highly reliable, cost-effective diagnostic tools suitable for collecting high-quality measurement data for research and clinical purposes.

Automatic Identification of Critical Follow-Up Recommendation Sentences in Radiology Reports

PubMed Central

Yetisgen-Yildiz, Meliha; Gunn, Martin L.; Xia, Fei; Payne, Thomas H.

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports. PMID:22195225

Automatic identification of critical follow-up recommendation sentences in radiology reports.

PubMed

Yetisgen-Yildiz, Meliha; Gunn, Martin L; Xia, Fei; Payne, Thomas H

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports.

Composing Data Parallel Code for a SPARQL Graph Engine

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

Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste

Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basicmore » graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.« less

Accelerating semantic graph databases on commodity clusters

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

Morari, Alessandro; Castellana, Vito G.; Haglin, David J.

We are developing a full software system for accelerating semantic graph databases on commodity cluster that scales to hundreds of nodes while maintaining constant query throughput. Our framework comprises a SPARQL to C++ compiler, a library of parallel graph methods and a custom multithreaded runtime layer, which provides a Partitioned Global Address Space (PGAS) programming model with fork/join parallelism and automatic load balancing over a commodity clusters. We present preliminary results for the compiler and for the runtime.

A Flexible Statechart-to-Model-Checker Translator

NASA Technical Reports Server (NTRS)

Rouquette, Nicolas; Dunphy, Julia; Feather, Martin S.

2000-01-01

Many current-day software design tools offer some variant of statechart notation for system specification. We, like others, have built an automatic translator from (a subset of) statecharts to a model checker, for use to validate behavioral requirements. Our translator is designed to be flexible. This allows us to quickly adjust the translator to variants of statechart semantics, including problem-specific notational conventions that designers employ. Our system demonstration will be of interest to the following two communities: (1) Potential end-users: Our demonstration will show translation from statecharts created in a commercial UML tool (Rational Rose) to Promela, the input language of Holzmann's model checker SPIN. The translation is accomplished automatically. To accommodate the major variants of statechart semantics, our tool offers user-selectable choices among semantic alternatives. Options for customized semantic variants are also made available. The net result is an easy-to-use tool that operates on a wide range of statechart diagrams to automate the pathway to model-checking input. (2) Other researchers: Our translator embodies, in one tool, ideas and approaches drawn from several sources. Solutions to the major challenges of statechart-to-model-checker translation (e.g., determining which transition(s) will fire, handling of concurrent activities) are retired in a uniform, fully mechanized, setting. The way in which the underlying architecture of the translator itself facilitates flexible and customizable translation will also be evident.

Developing a Satellite Based Automatic System for Crop Monitoring: Kenya's Great Rift Valley, A Case Study

NASA Astrophysics Data System (ADS)

Lucciani, Roberto; Laneve, Giovanni; Jahjah, Munzer; Mito, Collins

2016-08-01

The crop growth stage represents essential information for agricultural areas management. In this study we investigate the feasibility of a tool based on remotely sensed satellite (Landsat 8) imagery, capable of automatically classify crop fields and how much resolution enhancement based on pan-sharpening techniques and phenological information extraction, useful to create decision rules that allow to identify semantic class to assign to an object, can effectively support the classification process. Moreover we investigate the opportunity to extract vegetation health status information from remotely sensed assessment of the equivalent water thickness (EWT). Our case study is the Kenya's Great Rift valley, in this area a ground truth campaign was conducted during August 2015 in order to collect crop fields GPS measurements, leaf area index (LAI) and chlorophyll samples.

The effects of increasing semantic-associate list length on the Deese-Roediger-McDermott false recognition memory: Dual false-memory process in retrieval from sub- and supraspan lists.

PubMed

Jou, Jerwen; Arredondo, Mario L; Li, Cheng; Escamilla, Eric E; Zuniga, Richard

2017-10-01

In this study, the number of semantic associates in Deese-Roediger-McDermott (DRM) lists was varied from 4 to 14 in a modified Sternberg paradigm. The false alarm (FA) and correct rejection (CR) reaction time (RT)/memory-set size (MSS) functions of critical lures showed a cross-over interaction at approximately MSS 7, suggesting a reversal of the relative dominance between these two responses to the critical lure at this point and also indicating the location of the boundary between the sub- and supraspan MSS. For the subspan lists, FA to critical lures was slower than CR, suggesting a slow, strategic mechanism driving the false memory. Conversely, for the supraspan lists, critical lure FA was faster than its CR, suggesting a spontaneous mechanism driving the false memory. Results of two experiments showed that an automatic, fast, and a slow, controlled process could be error-prone or error-corrective, depending on the length of the DRM memory list. Thus there is a dual retrieval process in false memory as in true memory. The findings can be explained by both the activation/monitoring and the fuzzy-trace theories.

The effects of context on processing words during sentence reading among adults varying in age and literacy skill.

PubMed

Steen-Baker, Allison A; Ng, Shukhan; Payne, Brennan R; Anderson, Carolyn J; Federmeier, Kara D; Stine-Morrow, Elizabeth A L

2017-08-01

The facilitation of word processing by sentence context reflects the interaction between the build-up of message-level semantics and lexical processing. Yet, little is known about how this effect varies through adulthood as a function of reading skill. In this study, Participants 18-64 years old with a range of literacy competence read simple sentences as their eye movements were monitored. We manipulated the predictability of a sentence-final target word, operationalized as cloze probability. First fixation durations showed an interaction between age and literacy skill, decreasing with age among more skilled readers but increasing among less skilled readers. This pattern suggests that age-related slowing may impact reading when not buffered by skill, but with continued practice, automatization of reading can continue to develop in adulthood. In absolute terms, readers were sensitive to predictability, regardless of age or literacy, in both early and later measures. Older readers showed differential contextual sensitivity in regression patterns, effects not moderated by literacy skill. Finally, comprehension performance increased with age and literacy skill, but performance among less skilled readers was especially reduced when predictability was low, suggesting that low-literacy adults (regardless of age) struggle when creating mental representations under weaker semantic constraints. Collectively, these findings suggest that aging readers (regardless of reading skill) are more sensitive to context for meaning-integration processes; that less skilled adult readers (regardless of age) depend more on a constrained semantic representation for comprehension; and that the capacity for literacy engagement enables continued development of efficient lexical processing in adult reading development. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Motor signatures of emotional reactivity in frontotemporal dementia.

PubMed

Marshall, Charles R; Hardy, Chris J D; Russell, Lucy L; Clark, Camilla N; Bond, Rebecca L; Dick, Katrina M; Brotherhood, Emilie V; Mummery, Cath J; Schott, Jonathan M; Rohrer, Jonathan D; Kilner, James M; Warren, Jason D

2018-01-18

Automatic motor mimicry is essential to the normal processing of perceived emotion, and disrupted automatic imitation might underpin socio-emotional deficits in neurodegenerative diseases, particularly the frontotemporal dementias. However, the pathophysiology of emotional reactivity in these diseases has not been elucidated. We studied facial electromyographic responses during emotion identification on viewing videos of dynamic facial expressions in 37 patients representing canonical frontotemporal dementia syndromes versus 21 healthy older individuals. Neuroanatomical associations of emotional expression identification accuracy and facial muscle reactivity were assessed using voxel-based morphometry. Controls showed characteristic profiles of automatic imitation, and this response predicted correct emotion identification. Automatic imitation was reduced in the behavioural and right temporal variant groups, while the normal coupling between imitation and correct identification was lost in the right temporal and semantic variant groups. Grey matter correlates of emotion identification and imitation were delineated within a distributed network including primary visual and motor, prefrontal, insular, anterior temporal and temporo-occipital junctional areas, with common involvement of supplementary motor cortex across syndromes. Impaired emotional mimesis may be a core mechanism of disordered emotional signal understanding and reactivity in frontotemporal dementia, with implications for the development of novel physiological biomarkers of socio-emotional dysfunction in these diseases.

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

NASA Astrophysics Data System (ADS)

Davenport, Jack H.

2016-05-01

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

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.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

Semantic-based surveillance video retrieval.

PubMed

Hu, Weiming; Xie, Dan; Fu, Zhouyu; Zeng, Wenrong; Maybank, Steve

2007-04-01

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

Semantics by analogy for illustrative volume visualization☆

PubMed Central

Gerl, Moritz; Rautek, Peter; Isenberg, Tobias; Gröller, Eduard

2012-01-01

We present an interactive graphical approach for the explicit specification of semantics for volume visualization. This explicit and graphical specification of semantics for volumetric features allows us to visually assign meaning to both input and output parameters of the visualization mapping. This is in contrast to the implicit way of specifying semantics using transfer functions. In particular, we demonstrate how to realize a dynamic specification of semantics which allows to flexibly explore a wide range of mappings. Our approach is based on three concepts. First, we use semantic shader augmentation to automatically add rule-based rendering functionality to static visualization mappings in a shader program, while preserving the visual abstraction that the initial shader encodes. With this technique we extend recent developments that define a mapping between data attributes and visual attributes with rules, which are evaluated using fuzzy logic. Second, we let users define the semantics by analogy through brushing on renderings of the data attributes of interest. Third, the rules are specified graphically in an interface that provides visual clues for potential modifications. Together, the presented methods offer a high degree of freedom in the specification and exploration of rule-based mappings and avoid the limitations of a linguistic rule formulation. PMID:23576827

Populating the Semantic Web by Macro-reading Internet Text

NASA Astrophysics Data System (ADS)

Mitchell, Tom M.; Betteridge, Justin; Carlson, Andrew; Hruschka, Estevam; Wang, Richard

A key question regarding the future of the semantic web is "how will we acquire structured information to populate the semantic web on a vast scale?" One approach is to enter this information manually. A second approach is to take advantage of pre-existing databases, and to develop common ontologies, publishing standards, and reward systems to make this data widely accessible. We consider here a third approach: developing software that automatically extracts structured information from unstructured text present on the web. We also describe preliminary results demonstrating that machine learning algorithms can learn to extract tens of thousands of facts to populate a diverse ontology, with imperfect but reasonably good accuracy.

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

PubMed

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

2017-03-01

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

Utilizing semantic Wiki technology for intelligence analysis at the tactical edge

NASA Astrophysics Data System (ADS)

Little, Eric

2014-05-01

Challenges exist for intelligence analysts to efficiently and accurately process large amounts of data collected from a myriad of available data sources. These challenges are even more evident for analysts who must operate within small military units at the tactical edge. In such environments, decisions must be made quickly without guaranteed access to the kinds of large-scale data sources available to analysts working at intelligence agencies. Improved technologies must be provided to analysts at the tactical edge to make informed, reliable decisions, since this is often a critical collection point for important intelligence data. To aid tactical edge users, new types of intelligent, automated technology interfaces are required to allow them to rapidly explore information associated with the intersection of hard and soft data fusion, such as multi-INT signals, semantic models, social network data, and natural language processing of text. Abilities to fuse these types of data is paramount to providing decision superiority. For these types of applications, we have developed BLADE. BLADE allows users to dynamically add, delete and link data via a semantic wiki, allowing for improved interaction between different users. Analysts can see information updates in near-real-time due to a common underlying set of semantic models operating within a triple store that allows for updates on related data points from independent users tracking different items (persons, events, locations, organizations, etc.). The wiki can capture pictures, videos and related information. New information added directly to pages is automatically updated in the triple store and its provenance and pedigree is tracked over time, making that data more trustworthy and easily integrated with other users' pages.

Sortal anaphora resolution to enhance relation extraction from biomedical literature.

PubMed

Kilicoglu, Halil; Rosemblat, Graciela; Fiszman, Marcelo; Rindflesch, Thomas C

2016-04-14

Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization. Coreference resolution is a foundational yet challenging natural language processing task which, if performed successfully, is likely to enhance such systems significantly. In this paper, we propose a semantically oriented, rule-based method to resolve sortal anaphora, a specific type of coreference that forms the majority of coreference instances in biomedical literature. The method addresses all entity types and relies on linguistic components of SemRep, a broad-coverage biomedical relation extraction system. It has been incorporated into SemRep, extending its core semantic interpretation capability from sentence level to discourse level. We evaluated our sortal anaphora resolution method in several ways. The first evaluation specifically focused on sortal anaphora relations. Our methodology achieved a F1 score of 59.6 on the test portion of a manually annotated corpus of 320 Medline abstracts, a 4-fold improvement over the baseline method. Investigating the impact of sortal anaphora resolution on relation extraction, we found that the overall effect was positive, with 50 % of the changes involving uninformative relations being replaced by more specific and informative ones, while 35 % of the changes had no effect, and only 15 % were negative. We estimate that anaphora resolution results in changes in about 1.5 % of approximately 82 million semantic relations extracted from the entire PubMed. Our results demonstrate that a heavily semantic approach to sortal anaphora resolution is largely effective for biomedical literature. Our evaluation and error analysis highlight some areas for further improvements, such as coordination processing and intra-sentential antecedent selection.

Semantic distance-based creation of clusters of pharmacovigilance terms and their evaluation.

PubMed

Dupuch, Marie; Grabar, Natalia

2015-04-01

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. The detection of adverse drug reactions is performed using statistical algorithms and groupings of ADR terms from the MedDRA (Medical Dictionary for Drug Regulatory Activities) terminology. Standardized MedDRA Queries (SMQs) are the groupings which become a standard for assisting the retrieval and evaluation of MedDRA-coded ADR reports worldwide. Currently 84 SMQs have been created, while several important safety topics are not yet covered. Creation of SMQs is a long and tedious process performed by the experts. It relies on manual analysis of MedDRA in order to find out all the relevant terms to be included in a SMQ. Our objective is to propose an automatic method for assisting the creation of SMQs using the clustering of terms which are semantically similar. The experimental method relies on a specific semantic resource, and also on the semantic distance algorithms and clustering approaches. We perform several experiments in order to define the optimal parameters. Our results show that the proposed method can assist the creation of SMQs and make this process faster and systematic. The average performance of the method is precision 59% and recall 26%. The correlation of the results obtained is 0.72 against the medical doctors judgments and 0.78 against the medical coders judgments. These results and additional evaluation indicate that the generated clusters can be efficiently used for the detection of pharmacovigilance signals, as they provide better signal detection than the existing SMQs. Copyright © 2014. Published by Elsevier Inc.

Partial sleep deprivation does not alter processes involved in semantic word priming: event-related potential evidence.

PubMed

Tavakoli, Paniz; Muller-Gass, Alexandra; Campbell, Kenneth

2015-03-01

Sleep deprivation has generally been observed to have a detrimental effect on tasks that require sustained attention for successful performance. It might however be possible to counter these effects by altering cognitive strategies. A recent semantic word priming study indicated that subjects used an effortful predictive-expectancy search of semantic memory following normal sleep, but changed to an automatic, effortless strategy following total sleep deprivation. Partial sleep deprivation occurs much more frequently than total sleep deprivation. The present study therefore employed a similar priming task following either 4h of sleep or following normal sleep. The purpose of the study was to determine whether partial sleep deprivation would also lead to a shift in cognitive strategy to compensate for an inability to sustain attention and effortful processing necessary for using the predicative expectancy strategy. Sixteen subjects were presented with word pairs, a prime and a target that were either strongly semantically associated (cat...dog), weakly associated (cow...barn) or not associated (apple...road). The subject's task was to determine if the target word was semantically associated to the prime. A strong priming effect was observed in both conditions. RTs were slower, accuracy lower, and N400 larger to unassociated targets, independent of the amount of sleep. The overall N400 did not differ as a function of sleep. The scalp distribution of the N400 was also similar following both normal sleep and sleep loss. There was thus little evidence of a difference in the processing of the target stimulus as a function of the amount sleep. Similarly, ERPs in the period between the onset of the prime and the subsequent target also did not differ between the normal sleep and sleep loss conditions. In contrast to total sleep deprivation, subjects therefore appeared to use a common predictive expectancy strategy in both conditions. This strategy does however require an effortful sustaining of attention, and may not have been entirely successful when sleep was restricted. A slight but significant decrease in accuracy was noted. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2012-09-01

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

SciFlo: Semantically-Enabled Grid Workflow for Collaborative Science

NASA Astrophysics Data System (ADS)

Yunck, T.; Wilson, B. D.; Raskin, R.; Manipon, G.

2005-12-01

SciFlo is a system for Scientific Knowledge Creation on the Grid using a Semantically-Enabled Dataflow Execution Environment. SciFlo leverages Simple Object Access Protocol (SOAP) Web Services and the Grid Computing standards (WS-* standards and the Globus Alliance toolkits), and enables scientists to do multi-instrument Earth Science by assembling reusable SOAP Services, native executables, local command-line scripts, and python codes into a distributed computing flow (a graph of operators). SciFlo's XML dataflow documents can be a mixture of concrete operators (fully bound operations) and abstract template operators (late binding via semantic lookup). All data objects and operators can be both simply typed (simple and complex types in XML schema) and semantically typed using controlled vocabularies (linked to OWL ontologies such as SWEET). By exploiting ontology-enhanced search and inference, one can discover (and automatically invoke) Web Services and operators that have been semantically labeled as performing the desired transformation, and adapt a particular invocation to the proper interface (number, types, and meaning of inputs and outputs). The SciFlo client & server engines optimize the execution of such distributed data flows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The scientist injects a distributed computation into the Grid by simply filling out an HTML form or directly authoring the underlying XML dataflow document, and results are returned directly to the scientist's desktop. A Visual Programming tool is also being developed, but it is not required. Once an analysis has been specified for a granule or day of data, it can be easily repeated with different control parameters and over months or years of data. SciFlo uses and preserves semantics, and also generates and infers new semantic annotations. Specifically, the SciFlo engine uses semantic metadata to understand (infer) what it is doing and potentially improve the data flow; preserves semantics by saving links to the semantics of (metadata describing) the input datasets, related datasets, and the data transformations (algorithms) used to generate downstream products; generates new metadata by allowing the user to add semantic annotations to the generated data products (or simply accept automatically generated provenance annotations); and infers new semantic metadata by understanding and applying logic to the semantics of the data and the transformations performed. Much ontology development still needs to be done but, nevertheless, SciFlo documents provide a substrate for using and preserving more semantics as ontologies develop. We will give a live demonstration of the growing SciFlo network using an example dataflow in which atmospheric temperature and water vapor profiles from three Earth Observing System (EOS) instruments are retrieved using SOAP (geo-location query & data access) services, co-registered, and visually & statistically compared on demand (see http://sciflo.jpl.nasa.gov for more information).

Computable visually observed phenotype ontological framework for plants

PubMed Central

2011-01-01

Background The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed. Results We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research. Conclusions The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community. PMID:21702966

A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC.

PubMed

Kors, Jan A; Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich

2015-09-01

To create a multilingual gold-standard corpus for biomedical concept recognition. We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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

Automatic geospatial information Web service composition based on ontology interface matching

NASA Astrophysics Data System (ADS)

Xu, Xianbin; Wu, Qunyong; Wang, Qinmin

2008-10-01

With Web services technology the functions of WebGIS can be presented as a kind of geospatial information service, and helped to overcome the limitation of the information-isolated situation in geospatial information sharing field. Thus Geospatial Information Web service composition, which conglomerates outsourced services working in tandem to offer value-added service, plays the key role in fully taking advantage of geospatial information services. This paper proposes an automatic geospatial information web service composition algorithm that employed the ontology dictionary WordNet to analyze semantic distances among the interfaces. Through making matching between input/output parameters and the semantic meaning of pairs of service interfaces, a geospatial information web service chain can be created from a number of candidate services. A practice of the algorithm is also proposed and the result of it shows the feasibility of this algorithm and the great promise in the emerging demand for geospatial information web service composition.

Standardized mappings--a framework to combine different semantic mappers into a standardized web-API.

PubMed

Neuhaus, Philipp; Doods, Justin; Dugas, Martin

2015-01-01

Automatic coding of medical terms is an important, but highly complicated and laborious task. To compare and evaluate different strategies a framework with a standardized web-interface was created. Two UMLS mapping strategies are compared to demonstrate the interface. The framework is a Java Spring application running on a Tomcat application server. It accepts different parameters and returns results in JSON format. To demonstrate the framework, a list of medical data items was mapped by two different methods: similarity search in a large table of terminology codes versus search in a manually curated repository. These mappings were reviewed by a specialist. The evaluation shows that the framework is flexible (due to standardized interfaces like HTTP and JSON), performant and reliable. Accuracy of automatically assigned codes is limited (up to 40%). Combining different semantic mappers into a standardized Web-API is feasible. This framework can be easily enhanced due to its modular design.

Multimodal Excitatory Interfaces with Automatic Content Classification

NASA Astrophysics Data System (ADS)

Williamson, John; Murray-Smith, Roderick

We describe a non-visual interface for displaying data on mobile devices, based around active exploration: devices are shaken, revealing the contents rattling around inside. This combines sample-based contact sonification with event playback vibrotactile feedback for a rich and compelling display which produces an illusion much like balls rattling inside a box. Motion is sensed from accelerometers, directly linking the motions of the user to the feedback they receive in a tightly closed loop. The resulting interface requires no visual attention and can be operated blindly with a single hand: it is reactive rather than disruptive. This interaction style is applied to the display of an SMS inbox. We use language models to extract salient features from text messages automatically. The output of this classification process controls the timbre and physical dynamics of the simulated objects. The interface gives a rapid semantic overview of the contents of an inbox, without compromising privacy or interrupting the user.

Cross-Language Distributions of High Frequency and Phonetically Similar Cognates

PubMed Central

Schepens, Job; Dijkstra, Ton; Grootjen, Franc; van Heuven, Walter J. B.

2013-01-01

The coinciding form and meaning similarity of cognates, e.g. ‘flamme’ (French), ‘Flamme’ (German), ‘vlam’ (Dutch), meaning ‘flame’ in English, facilitates learning of additional languages. The cross-language frequency and similarity distributions of cognates vary according to evolutionary change and language contact. We compare frequency and orthographic (O), phonetic (P), and semantic similarity of cognates, automatically identified in semi-complete lexicons of six widely spoken languages. Comparisons of P and O similarity reveal inconsistent mappings in language pairs with deep orthographies. The frequency distributions show that cognate frequency is reduced in less closely related language pairs as compared to more closely related languages (e.g., French-English vs. German-English). These frequency and similarity patterns may support a better understanding of cognate processing in natural and experimental settings. The automatically identified cognates are available in the supplementary materials, including the frequency and similarity measurements. PMID:23675449

Consistent model driven architecture

NASA Astrophysics Data System (ADS)

Niepostyn, Stanisław J.

2015-09-01

The goal of the MDA is to produce software systems from abstract models in a way where human interaction is restricted to a minimum. These abstract models are based on the UML language. However, the semantics of UML models is defined in a natural language. Subsequently the verification of consistency of these diagrams is needed in order to identify errors in requirements at the early stage of the development process. The verification of consistency is difficult due to a semi-formal nature of UML diagrams. We propose automatic verification of consistency of the series of UML diagrams originating from abstract models implemented with our consistency rules. This Consistent Model Driven Architecture approach enables us to generate automatically complete workflow applications from consistent and complete models developed from abstract models (e.g. Business Context Diagram). Therefore, our method can be used to check practicability (feasibility) of software architecture models.

Gloss in Sanskrit Wordnet

NASA Astrophysics Data System (ADS)

Kulkarni, Malhar; Kulkarni, Irawati; Dangarikar, Chaitali; Bhattacharyya, Pushpak

Glosses and examples are the essential components of the computational lexical databases like, Wordnet. These two components of the lexical database can be used in building domain ontologies, semantic relations, phrase structure rules etc., and can help automatic or manual word sense disambiguation tasks. The present paper aims to highlight the importance of gloss in the process of WSD based on the experiences from building Sanskrit Wordnet. This paper presents a survey of Sanskrit Synonymy lexica, use of Navya-Nyāya terminology in developing a gloss and the kind of patterns evolved that are useful for the computational purpose of WSD with special reference to Sanskrit.

The pros and cons of masked priming.

PubMed

Forster, K I

1998-03-01

Masked priming paradigms offer the promise of tapping automatic, strategy-free lexical processing, as evidenced by the lack of expectancy disconfirmation effects, and proportionality effects in semantic priming experiments. But several recent findings suggest the effects may be prelexical. These findings concern nonword priming effects in lexical decision and naming, the effects of mixed-case presentation on nonword priming, and the dependence of priming on the nature of the distractors in lexical decision, suggesting possible strategy effects. The theory underlying each of these effects is discussed, and alternative explanations are developed that do not preclude a lexical basis for masked priming effects.

Automatic identification of comparative effectiveness research from Medline citations to support clinicians’ treatment information needs

PubMed Central

Zhang, Mingyuan; Fiol, Guilherme Del; Grout, Randall W.; Jonnalagadda, Siddhartha; Medlin, Richard; Mishra, Rashmi; Weir, Charlene; Liu, Hongfang; Mostafa, Javed; Fiszman, Marcelo

2014-01-01

Online knowledge resources such as Medline can address most clinicians’ patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care. Objective Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies. Methods The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression. Results Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard. Conclusion Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making. PMID:23920677

Automatic guidance of attention during real-world visual search.

PubMed

Seidl-Rathkopf, Katharina N; Turk-Browne, Nicholas B; Kastner, Sabine

2015-08-01

Looking for objects in cluttered natural environments is a frequent task in everyday life. This process can be difficult, because the features, locations, and times of appearance of relevant objects often are not known in advance. Thus, a mechanism by which attention is automatically biased toward information that is potentially relevant may be helpful. We tested for such a mechanism across five experiments by engaging participants in real-world visual search and then assessing attentional capture for information that was related to the search set but was otherwise irrelevant. Isolated objects captured attention while preparing to search for objects from the same category embedded in a scene, as revealed by lower detection performance (Experiment 1A). This capture effect was driven by a central processing bottleneck rather than the withdrawal of spatial attention (Experiment 1B), occurred automatically even in a secondary task (Experiment 2A), and reflected enhancement of matching information rather than suppression of nonmatching information (Experiment 2B). Finally, attentional capture extended to objects that were semantically associated with the target category (Experiment 3). We conclude that attention is efficiently drawn towards a wide range of information that may be relevant for an upcoming real-world visual search. This mechanism may be adaptive, allowing us to find information useful for our behavioral goals in the face of uncertainty.

Automatic Selection of Clinical Trials Based on A Semantic Web Approach.

PubMed

Cuggia, Marc; Campillo-Gimenez, Boris; Bouzille, Guillaume; Besana, Paolo; Jouini, Wassim; Dufour, Jean-Charles; Zekri, Oussama; Gibaud, Isabelle; Garde, Cyril; Duvauferier, Regis

2015-01-01

Recruitment of patients in clinical trials is nowadays preoccupying, as the inclusion rate is particularly low. The main identified factors are the multiplicity of open clinical trials, the high number and complexity of eligibility criteria, and the additional workload that a systematic search of the clinical trials a patient could be enrolled in for a physician. The principal objective of the ASTEC project is to automate the prescreening phase during multidisciplinary meetings (MDM). This paper presents the evaluation of a computerized recruitment support systems (CRSS) based on semantic web approach. The evaluation of the system was based on data collected retrospectively from a 6 month period of MDM in Urology and on 4 clinical trials of prostate cancer. The classification performance of the ASTEC system had a precision of 21%, recall of 93%, and an error rate equal to 37%. Missing data was the main issue encountered. The system was designed to be both scalable to other clinical domains and usable during MDM process.

Semantics-informed geological maps: Conceptual modeling and knowledge encoding

NASA Astrophysics Data System (ADS)

Lombardo, Vincenzo; Piana, Fabrizio; Mimmo, Dario

2018-07-01

This paper introduces a novel, semantics-informed geologic mapping process, whose application domain is the production of a synthetic geologic map of a large administrative region. A number of approaches concerning the expression of geologic knowledge through UML schemata and ontologies have been around for more than a decade. These approaches have yielded resources that concern specific domains, such as, e.g., lithology. We develop a conceptual model that aims at building a digital encoding of several domains of geologic knowledge, in order to support the interoperability of the sources. We apply the devised terminological base to the classification of the elements of a geologic map of the Italian Western Alps and northern Apennines (Piemonte region). The digitally encoded knowledge base is a merged set of ontologies, called OntoGeonous. The encoding process identifies the objects of the semantic encoding, the geologic units, gathers the relevant information about such objects from authoritative resources, such as GeoSciML (giving priority to the application schemata reported in the INSPIRE Encoding Cookbook), and expresses the statements by means of axioms encoded in the Web Ontology Language (OWL). To support interoperability, OntoGeonous interlinks the general concepts by referring to the upper part level of ontology SWEET (developed by NASA), and imports knowledge that is already encoded in ontological format (e.g., ontology Simple Lithology). Machine-readable knowledge allows for consistency checking and for classification of the geological map data through algorithms of automatic reasoning.

Tasking and sharing sensing assets using controlled natural language

NASA Astrophysics Data System (ADS)

Preece, Alun; Pizzocaro, Diego; Braines, David; Mott, David

2012-06-01

We introduce an approach to representing intelligence, surveillance, and reconnaissance (ISR) tasks at a relatively high level in controlled natural language. We demonstrate that this facilitates both human interpretation and machine processing of tasks. More specically, it allows the automatic assignment of sensing assets to tasks, and the informed sharing of tasks between collaborating users in a coalition environment. To enable automatic matching of sensor types to tasks, we created a machine-processable knowledge representation based on the Military Missions and Means Framework (MMF), and implemented a semantic reasoner to match task types to sensor types. We combined this mechanism with a sensor-task assignment procedure based on a well-known distributed protocol for resource allocation. In this paper, we re-formulate the MMF ontology in Controlled English (CE), a type of controlled natural language designed to be readable by a native English speaker whilst representing information in a structured, unambiguous form to facilitate machine processing. We show how CE can be used to describe both ISR tasks (for example, detection, localization, or identication of particular kinds of object) and sensing assets (for example, acoustic, visual, or seismic sensors, mounted on motes or unmanned vehicles). We show how these representations enable an automatic sensor-task assignment process. Where a group of users are cooperating in a coalition, we show how CE task summaries give users in the eld a high-level picture of ISR coverage of an area of interest. This allows them to make ecient use of sensing resources by sharing tasks.

The representation of conceptual knowledge: visual, auditory, and olfactory imagery compared with semantic processing.

PubMed

Palmiero, Massimiliano; Di Matteo, Rosalia; Belardinelli, Marta Olivetti

2014-05-01

Two experiments comparing imaginative processing in different modalities and semantic processing were carried out to investigate the issue of whether conceptual knowledge can be represented in different format. Participants were asked to judge the similarity between visual images, auditory images, and olfactory images in the imaginative block, if two items belonged to the same category in the semantic block. Items were verbally cued in both experiments. The degree of similarity between the imaginative and semantic items was changed across experiments. Experiment 1 showed that the semantic processing was faster than the visual and the auditory imaginative processing, whereas no differentiation was possible between the semantic processing and the olfactory imaginative processing. Experiment 2 revealed that only the visual imaginative processing could be differentiated from the semantic processing in terms of accuracy. These results showed that the visual and auditory imaginative processing can be differentiated from the semantic processing, although both visual and auditory images strongly rely on semantic representations. On the contrary, no differentiation is possible within the olfactory domain. Results are discussed in the frame of the imagery debate.

What role does the anterior temporal lobe play in sentence-level processing? Neural correlates of syntactic processing in semantic variant primary progressive aphasia.

PubMed

Wilson, Stephen M; DeMarco, Andrew T; Henry, Maya L; Gesierich, Benno; Babiak, Miranda; Mandelli, Maria Luisa; Miller, Bruce L; Gorno-Tempini, Maria Luisa

2014-05-01

Neuroimaging and neuropsychological studies have implicated the anterior temporal lobe (ATL) in sentence-level processing, with syntactic structure-building and/or combinatorial semantic processing suggested as possible roles. A potential challenge to the view that the ATL is involved in syntactic aspects of sentence processing comes from the clinical syndrome of semantic variant primary progressive aphasia (semantic PPA; also known as semantic dementia). In semantic PPA, bilateral neurodegeneration of the ATLs is associated with profound lexical semantic deficits, yet syntax is strikingly spared. The goal of this study was to investigate the neural correlates of syntactic processing in semantic PPA to determine which regions normally involved in syntactic processing are damaged in semantic PPA and whether spared syntactic processing depends on preserved functionality of intact regions, preserved functionality of atrophic regions, or compensatory functional reorganization. We scanned 20 individuals with semantic PPA and 24 age-matched controls using structural MRI and fMRI. Participants performed a sentence comprehension task that emphasized syntactic processing and minimized lexical semantic demands. We found that, in controls, left inferior frontal and left posterior temporal regions were modulated by syntactic processing, whereas anterior temporal regions were not significantly modulated. In the semantic PPA group, atrophy was most severe in the ATLs but extended to the posterior temporal regions involved in syntactic processing. Functional activity for syntactic processing was broadly similar in patients and controls; in particular, whole-brain analyses revealed no significant differences between patients and controls in the regions modulated by syntactic processing. The atrophic left ATL did show abnormal functionality in semantic PPA patients; however, this took the unexpected form of a failure to deactivate. Taken together, our findings indicate that spared syntactic processing in semantic PPA depends on preserved functionality of structurally intact left frontal regions and moderately atrophic left posterior temporal regions, but no functional reorganization was apparent as a consequence of anterior temporal atrophy and dysfunction. These results suggest that the role of the ATL in sentence processing is less likely to relate to syntactic structure-building and more likely to relate to higher-level processes such as combinatorial semantic processing.

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations.

PubMed

Zhao, Jian; Glueck, Michael; Breslav, Simon; Chevalier, Fanny; Khan, Azam

2017-01-01

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

Feature relevance assessment for the semantic interpretation of 3D point cloud data

NASA Astrophysics Data System (ADS)

Weinmann, M.; Jutzi, B.; Mallet, C.

2013-10-01

The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

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

Localising semantic and syntactic processing in spoken and written language comprehension: an Activation Likelihood Estimation meta-analysis.

PubMed

Rodd, Jennifer M; Vitello, Sylvia; Woollams, Anna M; Adank, Patti

2015-02-01

We conducted an Activation Likelihood Estimation (ALE) meta-analysis to identify brain regions that are recruited by linguistic stimuli requiring relatively demanding semantic or syntactic processing. We included 54 functional MRI studies that explicitly varied the semantic or syntactic processing load, while holding constant demands on earlier stages of processing. We included studies that introduced a syntactic/semantic ambiguity or anomaly, used a priming manipulation that specifically reduced the load on semantic/syntactic processing, or varied the level of syntactic complexity. The results confirmed the critical role of the posterior left Inferior Frontal Gyrus (LIFG) in semantic and syntactic processing. These results challenge models of sentence comprehension highlighting the role of anterior LIFG for semantic processing. In addition, the results emphasise the posterior (but not anterior) temporal lobe for both semantic and syntactic processing. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.

Simplifying HL7 Version 3 messages.

PubMed

Worden, Robert; Scott, Philip

2011-01-01

HL7 Version 3 offers a semantically robust method for healthcare interoperability but has been criticized as overly complex to implement. This paper reviews initiatives to simplify HL7 Version 3 messaging and presents a novel approach based on semantic mapping. Based on user-defined definitions, precise transforms between simple and full messages are automatically generated. Systems can be interfaced with the simple messages and achieve interoperability with full Version 3 messages through the transforms. This reduces the costs of HL7 interfacing and will encourage better uptake of HL7 Version 3 and CDA.

POLE.VAULT: A Semantic Framework for Health Policy Evaluation and Logical Testing.

PubMed

Shaban-Nejad, Arash; Okhmatovskaia, Anya; Shin, Eun Kyong; Davis, Robert L; Buckeridge, David L

2017-01-01

The major goal of our study is to provide an automatic evaluation framework that aligns the results generated through semantic reasoning with the best available evidence regarding effective interventions to support the logical evaluation of public health policies. To this end, we have designed the POLicy EVAlUation & Logical Testing (POLE.VAULT) Framework to assist different stakeholders and decision-makers in making informed decisions about different health-related interventions, programs and ultimately policies, based on the contextual knowledge and the best available evidence at both individual and aggregate levels.

Automatic summarization of soccer highlights using audio-visual descriptors.

PubMed

Raventós, A; Quijada, R; Torres, Luis; Tarrés, Francesc

2015-01-01

Automatic summarization generation of sports video content has been object of great interest for many years. Although semantic descriptions techniques have been proposed, many of the approaches still rely on low-level video descriptors that render quite limited results due to the complexity of the problem and to the low capability of the descriptors to represent semantic content. In this paper, a new approach for automatic highlights summarization generation of soccer videos using audio-visual descriptors is presented. The approach is based on the segmentation of the video sequence into shots that will be further analyzed to determine its relevance and interest. Of special interest in the approach is the use of the audio information that provides additional robustness to the overall performance of the summarization system. For every video shot a set of low and mid level audio-visual descriptors are computed and lately adequately combined in order to obtain different relevance measures based on empirical knowledge rules. The final summary is generated by selecting those shots with highest interest according to the specifications of the user and the results of relevance measures. A variety of results are presented with real soccer video sequences that prove the validity of the approach.

Adaptive Semantic and Social Web-based learning and assessment environment for the STEM

NASA Astrophysics Data System (ADS)

Babaie, Hassan; Atchison, Chris; Sunderraman, Rajshekhar

2014-05-01

We are building a cloud- and Semantic Web-based personalized, adaptive learning environment for the STEM fields that integrates and leverages Social Web technologies to allow instructors and authors of learning material to collaborate in semi-automatic development and update of their common domain and task ontologies and building their learning resources. The semi-automatic ontology learning and development minimize issues related to the design and maintenance of domain ontologies by knowledge engineers who do not have any knowledge of the domain. The social web component of the personal adaptive system will allow individual and group learners to interact with each other and discuss their own learning experience and understanding of course material, and resolve issues related to their class assignments. The adaptive system will be capable of representing key knowledge concepts in different ways and difficulty levels based on learners' differences, and lead to different understanding of the same STEM content by different learners. It will adapt specific pedagogical strategies to individual learners based on their characteristics, cognition, and preferences, allow authors to assemble remotely accessed learning material into courses, and provide facilities for instructors to assess (in real time) the perception of students of course material, monitor their progress in the learning process, and generate timely feedback based on their understanding or misconceptions. The system applies a set of ontologies that structure the learning process, with multiple user friendly Web interfaces. These include the learning ontology (models learning objects, educational resources, and learning goal); context ontology (supports adaptive strategy by detecting student situation), domain ontology (structures concepts and context), learner ontology (models student profile, preferences, and behavior), task ontologies, technological ontology (defines devices and places that surround the student), pedagogy ontology, and learner ontology (defines time constraint, comment, profile).

Application of Mls Data to the Assessment of Safety-Related Features in the Surrounding Area of Automatically Detected Pedestrian Crossings

NASA Astrophysics Data System (ADS)

Soilán, M.; Riveiro, B.; Sánchez-Rodríguez, A.; González-deSantos, L. M.

2018-05-01

During the last few years, there has been a huge methodological development regarding the automatic processing of 3D point cloud data acquired by both terrestrial and aerial mobile mapping systems, motivated by the improvement of surveying technologies and hardware performance. This paper presents a methodology that, in a first place, extracts geometric and semantic information regarding the road markings within the surveyed area from Mobile Laser Scanning (MLS) data, and then employs it to isolate street areas where pedestrian crossings are found and, therefore, pedestrians are more likely to cross the road. Then, different safety-related features can be extracted in order to offer information about the adequacy of the pedestrian crossing regarding its safety, which can be displayed in a Geographical Information System (GIS) layer. These features are defined in four different processing modules: Accessibility analysis, traffic lights classification, traffic signs classification, and visibility analysis. The validation of the proposed methodology has been carried out in two different cities in the northwest of Spain, obtaining both quantitative and qualitative results for pedestrian crossing classification and for each processing module of the safety assessment on pedestrian crossing environments.

Towards Automatic Validation and Healing of Citygml Models for Geometric and Semantic Consistency

NASA Astrophysics Data System (ADS)

Alam, N.; Wagner, D.; Wewetzer, M.; von Falkenhausen, J.; Coors, V.; Pries, M.

2013-09-01

A steadily growing number of application fields for large 3D city models have emerged in recent years. Like in many other domains, data quality is recognized as a key factor for successful business. Quality management is mandatory in the production chain nowadays. Automated domain-specific tools are widely used for validation of business-critical data but still common standards defining correct geometric modeling are not precise enough to define a sound base for data validation of 3D city models. Although the workflow for 3D city models is well-established from data acquisition to processing, analysis and visualization, quality management is not yet a standard during this workflow. Processing data sets with unclear specification leads to erroneous results and application defects. We show that this problem persists even if data are standard compliant. Validation results of real-world city models are presented to demonstrate the potential of the approach. A tool to repair the errors detected during the validation process is under development; first results are presented and discussed. The goal is to heal defects of the models automatically and export a corrected CityGML model.

TEXTINFO: a tool for automatic determination of patient clinical profiles using text analysis.

PubMed Central

Borst, F.; Lyman, M.; Nhàn, N. T.; Tick, L. J.; Sager, N.; Scherrer, J. R.

1991-01-01

The clinical data contained in narrative patient documents is made available via grammatical and semantic processing. Retrievals from the resulting relational database tables are matched against a set of clinical descriptors to obtain clinical profiles of the patients in terms of the descriptors present in the documents. Discharge summaries of 57 Dept. of Digestive Surgery patients were processed in this manner. Factor analysis and discriminant analysis procedures were then applied, showing the profiles to be useful for diagnosis definitions (by establishing relations between diagnoses and clinical findings), for diagnosis assessment (by viewing the match between a definition and observed events recorded in a patient text), and potentially for outcome evaluation based on the classification abilities of clinical signs. PMID:1807679

What role does the anterior temporal lobe play in sentence-level processing? Neural correlates of syntactic processing in semantic PPA

PubMed Central

Wilson, Stephen M.; DeMarco, Andrew T.; Henry, Maya L.; Gesierich, Benno; Babiak, Miranda; Mandelli, Maria Luisa; Miller, Bruce L.; Gorno-Tempini, Maria Luisa

2014-01-01

Neuroimaging and neuropsychological studies have implicated the anterior temporal lobe (ATL) in sentence-level processing, with syntactic structure-building and/or combinatorial semantic processing suggested as possible roles. A potential challenge to the view that the ATL is involved in syntactic aspects of sentence processing comes from the clinical syndrome of semantic variant primary progressive aphasia (semantic PPA, also known as semantic dementia). In semantic PPA, bilateral neurodegeneration of the anterior temporal lobes is associated with profound lexical semantic deficits, yet syntax is strikingly spared. The goal of this study was to investigate the neural correlates of syntactic processing in semantic PPA, in order to determine which regions normally involved in syntactic processing are damaged in semantic PPA, and whether spared syntactic processing depends on preserved functionality of intact regions, preserved functionality of atrophic regions, or compensatory functional reorganization. We scanned 20 individuals with semantic PPA and 24 age-matched controls using structural and functional MRI. Participants performed a sentence comprehension task that emphasized syntactic processing and minimized lexical semantic demands. We found that in controls, left inferior frontal and left posterior temporal regions were modulated by syntactic processing, while anterior temporal regions were not significantly modulated. In the semantic PPA group, atrophy was most severe in the anterior temporal lobes, but extended to the posterior temporal regions involved in syntactic processing. Functional activity for syntactic processing was broadly similar in patients and controls; in particular, whole-brain analyses revealed no significant differences between patients and controls in the regions modulated by syntactic processing. The atrophic left anterior temporal lobe did show abnormal functionality in semantic PPA patients, however this took the unexpected form of a failure to deactivate. Taken together, our findings indicate that spared syntactic processing in semantic PPA depends on preserved functionality of structurally intact left frontal regions and moderately atrophic left posterior temporal regions, but no functional reorganization was apparent as a consequence of anterior temporal atrophy and dysfunction. These results suggest that the role of the anterior temporal lobe in sentence processing is less likely to relate to syntactic structure-building, and more likely to relate to higher level processes such as combinatorial semantic processing. PMID:24345172

Transcranial Direct Current Stimulation Effects on Semantic Processing in Healthy Individuals.

PubMed

Joyal, Marilyne; Fecteau, Shirley

2016-01-01

Semantic processing allows us to use conceptual knowledge about the world. It has been associated with a large distributed neural network that includes the frontal, temporal and parietal cortices. Recent studies using transcranial direct current stimulation (tDCS) also contributed at investigating semantic processing. The goal of this article was to review studies investigating semantic processing in healthy individuals with tDCS and discuss findings from these studies in line with neuroimaging results. Based on functional magnetic resonance imaging studies assessing semantic processing, we predicted that tDCS applied over the inferior frontal gyrus, middle temporal gyrus, and posterior parietal cortex will impact semantic processing. We conducted a search on Pubmed and selected 27 articles in which tDCS was used to modulate semantic processing in healthy subjects. We analysed each article according to these criteria: demographic information, experimental outcomes assessing semantic processing, study design, and effects of tDCS on semantic processes. From the 27 reviewed studies, 8 found main effects of stimulation. In addition to these 8 studies, 17 studies reported an interaction between stimulus types and stimulation conditions (e.g. incoherent functional, but not instrumental, actions were processed faster when anodal tDCS was applied over the posterior parietal cortex as compared to sham tDCS). Results suggest that regions in the frontal, temporal, and parietal cortices are involved in semantic processing. tDCS can modulate some aspects of semantic processing and provide information on the functional roles of brain regions involved in this cognitive process. Copyright © 2016 Elsevier Inc. All rights reserved.

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

ERIC Educational Resources Information Center

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

2017-01-01

Individuals with autism spectrum disorders (ASD) experience difficulties with language, particularly higher-level functions like semantic integration. Yet some studies indicate that semantic processing of non-linguistic stimuli is not impaired, suggesting a language-specific deficit in semantic processing. Using a semantic priming task, we…

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

Lowering the Barriers to Integrative Aquatic Ecosystem Science: Semantic Provenance, Open Linked Data, and Workflows

NASA Astrophysics Data System (ADS)

Harmon, T.; Hofmann, A. F.; Utz, R.; Deelman, E.; Hanson, P. C.; Szekely, P.; Villamizar, S. R.; Knoblock, C.; Guo, Q.; Crichton, D. J.; McCann, M. P.; Gil, Y.

2011-12-01

Environmental cyber-observatory (ECO) planning and implementation has been ongoing for more than a decade now, and several major efforts have recently come online or will soon. Some investigators in the relevant research communities will use ECO data, traditionally by developing their own client-side services to acquire data and then manually create custom tools to integrate and analyze it. However, a significant portion of the aquatic ecosystem science community will need more custom services to manage locally collected data. The latter group represents enormous intellectual capacity when one envisions thousands of ecosystems scientists supplementing ECO baseline data by sharing their own locally intensive observational efforts. This poster summarizes the outcomes of the June 2011 Workshop for Aquatic Ecosystem Sustainability (WAES) which focused on the needs of aquatic ecosystem research on inland waters and oceans. Here we advocate new approaches to support scientists to model, integrate, and analyze data based on: 1) a new breed of software tools in which semantic provenance is automatically created and used by the system, 2) the use of open standards based on RDF and Linked Data Principles to facilitate sharing of data and provenance annotations, 3) the use of workflows to represent explicitly all data preparation, integration, and processing steps in a way that is automatically repeatable. Aquatic ecosystems workflow exemplars are provided and discussed in terms of their potential broaden data sharing, analysis and synthesis thereby increasing the impact of aquatic ecosystem research.

Semantic Typicality Effects in Acquired Dyslexia: Evidence for Semantic Impairment in Deep Dyslexia.

PubMed

Riley, Ellyn A; Thompson, Cynthia K

2010-06-01

BACKGROUND: Acquired deep dyslexia is characterized by impairment in grapheme-phoneme conversion and production of semantic errors in oral reading. Several theories have attempted to explain the production of semantic errors in deep dyslexia, some proposing that they arise from impairments in both grapheme-phoneme and lexical-semantic processing, and others proposing that such errors stem from a deficit in phonological production. Whereas both views have gained some acceptance, the limited evidence available does not clearly eliminate the possibility that semantic errors arise from a lexical-semantic input processing deficit. AIMS: To investigate semantic processing in deep dyslexia, this study examined the typicality effect in deep dyslexic individuals, phonological dyslexic individuals, and controls using an online category verification paradigm. This task requires explicit semantic access without speech production, focusing observation on semantic processing from written or spoken input. METHODS #ENTITYSTARTX00026; PROCEDURES: To examine the locus of semantic impairment, the task was administered in visual and auditory modalities with reaction time as the primary dependent measure. Nine controls, six phonological dyslexic participants, and five deep dyslexic participants completed the study. OUTCOMES #ENTITYSTARTX00026; RESULTS: Controls and phonological dyslexic participants demonstrated a typicality effect in both modalities, while deep dyslexic participants did not demonstrate a typicality effect in either modality. CONCLUSIONS: These findings suggest that deep dyslexia is associated with a semantic processing deficit. Although this does not rule out the possibility of concomitant deficits in other modules of lexical-semantic processing, this finding suggests a direction for treatment of deep dyslexia focused on semantic processing.

Evidence for the contribution of a threshold retrieval process to semantic memory.

PubMed

Kempnich, Maria; Urquhart, Josephine A; O'Connor, Akira R; Moulin, Chris J A

2017-10-01

It is widely held that episodic retrieval can recruit two processes: a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver-operating characteristics (ROCs) for a marker of a threshold retrieval process. We fitted observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual-process signal detection models) and a novel dual-process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions, we anticipated the DP-RR model would best fit the semantic task data. Experiment 1 (506 participants) provided evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. Experiment 2 (316 participants) found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual-process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.

Differential Phonological and Semantic Modulation of Neurophysiological Responses to Visual Word Recognition.

PubMed

Drakesmith, Mark; El-Deredy, Wael; Welbourne, Stephen

2015-01-01

Reading words for meaning relies on orthographic, phonological and semantic processing. The triangle model implicates a direct orthography-to-semantics pathway and a phonologically mediated orthography-to-semantics pathway, which interact with each other. The temporal evolution of processing in these routes is not well understood, although theoretical evidence predicts early phonological processing followed by interactive phonological and semantic processing. This study used electroencephalography-event-related potential (ERP) analysis and magnetoencephalography (MEG) source localisation to identify temporal markers and the corresponding neural generators of these processes in early (∼200 ms) and late (∼400 ms) neurophysiological responses to visual words, pseudowords and consonant strings. ERP showed an effect of phonology but not semantics in both time windows, although at ∼400 ms there was an effect of stimulus familiarity. Phonological processing at ~200 ms was localised to the left occipitotemporal cortex and the inferior frontal gyrus. At 400 ms, there was continued phonological processing in the inferior frontal gyrus and additional semantic processing in the anterior temporal cortex. There was also an area in the left temporoparietal junction which was implicated in both phonological and semantic processing. In ERP, the semantic response at ∼400 ms appeared to be masked by concurrent processes relating to familiarity, while MEG successfully differentiated these processes. The results support the prediction of early phonological processing followed by an interaction of phonological and semantic processing during word recognition. Neuroanatomical loci of these processes are consistent with previous neuropsychological and functional magnetic resonance imaging studies. The results also have implications for the classical interpretation of N400-like responses as markers for semantic processing.

Semantic Advertising for Web 3.0

NASA Astrophysics Data System (ADS)

Thomas, Edward; Pan, Jeff Z.; Taylor, Stuart; Ren, Yuan; Jekjantuk, Nophadol; Zhao, Yuting

Advertising on the World Wide Web is based around automatically matching web pages with appropriate advertisements, in the form of banner ads, interactive adverts, or text links. Traditionally this has been done by manual classification of pages, or more recently using information retrieval techniques to find the most important keywords from the page, and match these to keywords being used by adverts. In this paper, we propose a new model for online advertising, based around lightweight embedded semantics. This will improve the relevancy of adverts on the World Wide Web and help to kick-start the use of RDFa as a mechanism for adding lightweight semantic attributes to the Web. Furthermore, we propose a system architecture for the proposed new model, based on our scalable ontology reasoning infrastructure TrOWL.

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

NASA Astrophysics Data System (ADS)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories.

PubMed

Hasan, Mehedi; Kotov, Alexander; Carcone, April; Dong, Ming; Naar, Sylvie; Hartlieb, Kathryn Brogan

2016-08-01

This study examines the effectiveness of state-of-the-art supervised machine learning methods in conjunction with different feature types for the task of automatic annotation of fragments of clinical text based on codebooks with a large number of categories. We used a collection of motivational interview transcripts consisting of 11,353 utterances, which were manually annotated by two human coders as the gold standard, and experimented with state-of-art classifiers, including Naïve Bayes, J48 Decision Tree, Support Vector Machine (SVM), Random Forest (RF), AdaBoost, DiscLDA, Conditional Random Fields (CRF) and Convolutional Neural Network (CNN) in conjunction with lexical, contextual (label of the previous utterance) and semantic (distribution of words in the utterance across the Linguistic Inquiry and Word Count dictionaries) features. We found out that, when the number of classes is large, the performance of CNN and CRF is inferior to SVM. When only lexical features were used, interview transcripts were automatically annotated by SVM with the highest classification accuracy among all classifiers of 70.8%, 61% and 53.7% based on the codebooks consisting of 17, 20 and 41 codes, respectively. Using contextual and semantic features, as well as their combination, in addition to lexical ones, improved the accuracy of SVM for annotation of utterances in motivational interview transcripts with a codebook consisting of 17 classes to 71.5%, 74.2%, and 75.1%, respectively. Our results demonstrate the potential of using machine learning methods in conjunction with lexical, semantic and contextual features for automatic annotation of clinical interview transcripts with near-human accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.

Brain model of text animation as a data mining strategy.

PubMed

Astakhova, Tamara; Astakhov, Vadim

2009-01-01

Imagination is the critical point in developing of realistic intelligence (AI) systems. One way to approach imagination would be simulation of its properties and operations. We developed two models "Brain Network Hierarchy of Languages," and "Semantical Holographic Calculus" and simulation system ScriptWriter that emulate the process of imagination through an automatic animation of English texts. The purpose of this paper is to demonstrate the model and present "ScriptWriter" system http://nvo.sdsc.edu/NVO/JCSG/get_SRB_mime_file2.cgi//home/tamara.sdsc/test/demo.zip?F=/home/tamara.sdsc/test/demo.zip&M=application/x-gtar for simulation of the imagination.

Subliminal speech priming.

PubMed

Kouider, Sid; Dupoux, Emmanuel

2005-08-01

We present a novel subliminal priming technique that operates in the auditory modality. Masking is achieved by hiding a spoken word within a stream of time-compressed speechlike sounds with similar spectral characteristics. Participants were unable to consciously identify the hidden words, yet reliable repetition priming was found. This effect was unaffected by a change in the speaker's voice and remained restricted to lexical processing. The results show that the speech modality, like the written modality, involves the automatic extraction of abstract word-form representations that do not include nonlinguistic details. In both cases, priming operates at the level of discrete and abstract lexical entries and is little influenced by overlap in form or semantics.

A Tri-network Model of Human Semantic Processing

PubMed Central

Xu, Yangwen; He, Yong; Bi, Yanchao

2017-01-01

Humans process the meaning of the world via both verbal and nonverbal modalities. It has been established that widely distributed cortical regions are involved in semantic processing, yet the global wiring pattern of this brain system has not been considered in the current neurocognitive semantic models. We review evidence from the brain-network perspective, which shows that the semantic system is topologically segregated into three brain modules. Revisiting previous region-based evidence in light of these new network findings, we postulate that these three modules support multimodal experiential representation, language-supported representation, and semantic control. A tri-network neurocognitive model of semantic processing is proposed, which generates new hypotheses regarding the network basis of different types of semantic processes. PMID:28955266

Enhanced semantic interoperability by profiling health informatics standards.

PubMed

López, Diego M; Blobel, Bernd

2009-01-01

Several standards applied to the healthcare domain support semantic interoperability. These standards are far from being completely adopted in health information system development, however. The objective of this paper is to provide a method and suggest the necessary tooling for reusing standard health information models, by that way supporting the development of semantically interoperable systems and components. The approach is based on the definition of UML Profiles. UML profiling is a formal modeling mechanism to specialize reference meta-models in such a way that it is possible to adapt those meta-models to specific platforms or domains. A health information model can be considered as such a meta-model. The first step of the introduced method identifies the standard health information models and tasks in the software development process in which healthcare information models can be reused. Then, the selected information model is formalized as a UML Profile. That Profile is finally applied to system models, annotating them with the semantics of the information model. The approach is supported on Eclipse-based UML modeling tools. The method is integrated into a comprehensive framework for health information systems development, and the feasibility of the approach is demonstrated in the analysis, design, and implementation of a public health surveillance system, reusing HL7 RIM and DIMs specifications. The paper describes a method and the necessary tooling for reusing standard healthcare information models. UML offers several advantages such as tooling support, graphical notation, exchangeability, extensibility, semi-automatic code generation, etc. The approach presented is also applicable for harmonizing different standard specifications.

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%.

Using semantic technologies and the OSU ontology for modelling context and activities in multi-sensory surveillance systems

NASA Astrophysics Data System (ADS)

Gómez A, Héctor F.; Martínez-Tomás, Rafael; Arias Tapia, Susana A.; Rincón Zamorano, Mariano

2014-04-01

Automatic systems that monitor human behaviour for detecting security problems are a challenge today. Previously, our group defined the Horus framework, which is a modular architecture for the integration of multi-sensor monitoring stages. In this work, structure and technologies required for high-level semantic stages of Horus are proposed, and the associated methodological principles established with the aim of recognising specific behaviours and situations. Our methodology distinguishes three semantic levels of events: low level (compromised with sensors), medium level (compromised with context), and high level (target behaviours). The ontology for surveillance and ubiquitous computing has been used to integrate ontologies from specific domains and together with semantic technologies have facilitated the modelling and implementation of scenes and situations by reusing components. A home context and a supermarket context were modelled following this approach, where three suspicious activities were monitored via different virtual sensors. The experiments demonstrate that our proposals facilitate the rapid prototyping of this kind of systems.

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

PubMed Central

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

2012-01-01

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

Semantic processing during morphological priming: an ERP study.

PubMed

Beyersmann, Elisabeth; Iakimova, Galina; Ziegler, Johannes C; Colé, Pascale

2014-09-04

Previous research has yielded conflicting results regarding the onset of semantic processing during morphological priming. The present study was designed to further explore the time-course of morphological processing using event-related potentials (ERPs). We conducted a primed lexical decision study comparing a morphological (LAVAGE - laver [washing - wash]), a semantic (LINGE - laver [laundry - wash]), an orthographic (LAVANDE - laver [lavender - wash]), and an unrelated control condition (HOSPICE - laver [nursing home - wash]), using the same targets across the four priming conditions. The behavioral data showed significant effects of morphological and semantic priming, with the magnitude of morphological priming being significantly larger than the magnitude of semantic priming. The ERP data revealed significant morphological but no semantic priming at 100-250 ms. Furthermore, a reduction of the N400 amplitude in the morphological condition compared to the semantic and orthographic condition demonstrates that the morphological priming effect was not entirely due to the semantic or orthographic overlap between the prime and the target. The present data reflect an early process of semantically blind morphological decomposition, and a later process of morpho-semantic decomposition, which we discuss in the context of recent morphological processing theories. Copyright © 2014 Elsevier B.V. All rights reserved.

ERP responses to lexical-semantic processing in typically developing toddlers, in adults, and in toddlers at risk for language and learning impairment.

PubMed

Cantiani, Chiara; Riva, Valentina; Piazza, Caterina; Melesi, Giulia; Mornati, Giulia; Bettoni, Roberta; Marino, Cecilia; Molteni, Massimo

2017-08-01

Children begin to establish lexical-semantic representations during their first year of life, resulting in a rapid growth of vocabulary around 18-24 months of age. The neural mechanisms underlying this initial ability to map words onto conceptual representations remain relatively unknown. In the present study, the electrophysiological underpinnings of these mechanisms are explored during the critical phase of lexical acquisition using a picture-word matching paradigm. Event-Related Potentials (ERPs) elicited by words (either congruous or incongruous with the previous picture context) and pseudo-words are investigated in 20-month-old toddlers (N = 20) and compared to those elicited in a sample of adults (N = 20), reflecting the final and efficient system, and a sample of toddlers at familial risk for language and learning impairment (LLI, N = 15). The results suggest that the architecture underlying spoken word representation and processing is constant throughout development, even if some differences between children and adults emerged. Interestingly, children seem to be faster than adults in processing incongruent words, probably because relying on a different and more superficial strategy. This early strategy does not seem to be present in children at risk for LLI. In addition, both groups of children do not show different and specific electrophysiological underpinnings in response to real but incongruent words and unknown words, suggesting that during the critical phase of lexical acquisition any potential word is processed in a similar way. Overall, children at risk for LLI turned out to be sensitive to verbal incongruity of the lexical-semantic context, although some differences from typically developing children emerged, reflecting slower processing and less automatic responses. Taken together, the findings of this study pave the way to further research to investigate these effects in clinical and at-risk populations with the general purpose of disentangling the underlying mechanisms of lexical acquisition, and potentially predicting later language (dis)abilities. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

Zeng, Tao; Mao, Wen; Lu, Qing

2016-05-25

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

Modulation of task demands suggests that semantic processing interferes with the formation of episodic associations

PubMed Central

Long, Nicole M.; Kahana, Michael J.

2016-01-01

Although episodic and semantic memory share overlapping neural mechanisms, it remains unclear how our pre-existing semantic associations modulate the formation of new, episodic associations. When freely recalling recently studied words, people rely on both episodic and semantic associations, shown through temporal and semantic clustering of responses. We asked whether orienting participants toward semantic associations interferes with or facilitates the formation of episodic associations. We compared electroencephalographic (EEG) activity recorded during the encoding of subsequently recalled words that were either temporally or semantically clustered. Participants studied words with or without a concurrent semantic orienting task. We identified a neural signature of successful episodic association formation whereby high frequency EEG activity (HFA, 44 – 100 Hz) overlying left prefrontal regions increased for subsequently temporally clustered words, but only for those words studied without a concurrent semantic orienting task. To confirm that this disruption in the formation of episodic associations was driven by increased semantic processing, we measured the neural correlates of subsequent semantic clustering. We found that HFA increased for subsequently semantically clustered words only for lists with a concurrent semantic orienting task. This dissociation suggests that increased semantic processing of studied items interferes with the neural processes that support the formation of novel episodic associations. PMID:27617775

Modulation of task demands suggests that semantic processing interferes with the formation of episodic associations.

PubMed

Long, Nicole M; Kahana, Michael J

2017-02-01

Although episodic and semantic memory share overlapping neural mechanisms, it remains unclear how our pre-existing semantic associations modulate the formation of new, episodic associations. When freely recalling recently studied words, people rely on both episodic and semantic associations, shown through temporal and semantic clustering of responses. We asked whether orienting participants toward semantic associations interferes with or facilitates the formation of episodic associations. We compared electroencephalographic (EEG) activity recorded during the encoding of subsequently recalled words that were either temporally or semantically clustered. Participants studied words with or without a concurrent semantic orienting task. We identified a neural signature of successful episodic association formation whereby high-frequency EEG activity (HFA, 44-100 Hz) overlying left prefrontal regions increased for subsequently temporally clustered words, but only for those words studied without a concurrent semantic orienting task. To confirm that this disruption in the formation of episodic associations was driven by increased semantic processing, we measured the neural correlates of subsequent semantic clustering. We found that HFA increased for subsequently semantically clustered words only for lists with a concurrent semantic orienting task. This dissociation suggests that increased semantic processing of studied items interferes with the neural processes that support the formation of novel episodic associations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Neural Substrates of Processing Anger in Language: Contributions of Prosody and Semantics.

PubMed

Castelluccio, Brian C; Myers, Emily B; Schuh, Jillian M; Eigsti, Inge-Marie

2016-12-01

Emotions are conveyed primarily through two channels in language: semantics and prosody. While many studies confirm the role of a left hemisphere network in processing semantic emotion, there has been debate over the role of the right hemisphere in processing prosodic emotion. Some evidence suggests a preferential role for the right hemisphere, and other evidence supports a bilateral model. The relative contributions of semantics and prosody to the overall processing of affect in language are largely unexplored. The present work used functional magnetic resonance imaging to elucidate the neural bases of processing anger conveyed by prosody or semantic content. Results showed a robust, distributed, bilateral network for processing angry prosody and a more modest left hemisphere network for processing angry semantics when compared to emotionally neutral stimuli. Findings suggest the nervous system may be more responsive to prosodic cues in speech than to the semantic content of speech.

The benefits of sensorimotor knowledge: body-object interaction facilitates semantic processing.

PubMed

Siakaluk, Paul D; Pexman, Penny M; Sears, Christopher R; Wilson, Kim; Locheed, Keri; Owen, William J

2008-04-05

This article examined the effects of body-object interaction (BOI) on semantic processing. BOI measures perceptions of the ease with which a human body can physically interact with a word's referent. In Experiment 1, BOI effects were examined in 2 semantic categorization tasks (SCT) in which participants decided if words are easily imageable. Responses were faster and more accurate for high BOI words (e.g., mask) than for low BOI words (e.g., ship). In Experiment 2, BOI effects were examined in a semantic lexical decision task (SLDT), which taps both semantic feedback and semantic processing. The BOI effect was larger in the SLDT than in the SCT, suggesting that BOI facilitates both semantic feedback and semantic processing. The findings are consistent with the embodied cognition perspective (e.g., Barsalou's, 1999, Perceptual Symbols Theory), which proposes that sensorimotor interactions with the environment are incorporated in semantic knowledge. 2008 Cognitive Science Society, Inc.

Semantic integration of data on transcriptional regulation

PubMed Central

Baitaluk, Michael; Ponomarenko, Julia

2010-01-01

Motivation: Experimental and predicted data concerning gene transcriptional regulation are distributed among many heterogeneous sources. However, there are no resources to integrate these data automatically or to provide a ‘one-stop shop’ experience for users seeking information essential for deciphering and modeling gene regulatory networks. Results: IntegromeDB, a semantic graph-based ‘deep-web’ data integration system that automatically captures, integrates and manages publicly available data concerning transcriptional regulation, as well as other relevant biological information, is proposed in this article. The problems associated with data integration are addressed by ontology-driven data mapping, multiple data annotation and heterogeneous data querying, also enabling integration of the user's data. IntegromeDB integrates over 100 experimental and computational data sources relating to genomics, transcriptomics, genetics, and functional and interaction data concerning gene transcriptional regulation in eukaryotes and prokaryotes. Availability: IntegromeDB is accessible through the integrated research environment BiologicalNetworks at http://www.BiologicalNetworks.org Contact: baitaluk@sdsc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20427517

A System for the Semantic Multimodal Analysis of News Audio-Visual Content

NASA Astrophysics Data System (ADS)

Mezaris, Vasileios; Gidaros, Spyros; Papadopoulos, GeorgiosTh; Kasper, Walter; Steffen, Jörg; Ordelman, Roeland; Huijbregts, Marijn; de Jong, Franciska; Kompatsiaris, Ioannis; Strintzis, MichaelG

2010-12-01

News-related content is nowadays among the most popular types of content for users in everyday applications. Although the generation and distribution of news content has become commonplace, due to the availability of inexpensive media capturing devices and the development of media sharing services targeting both professional and user-generated news content, the automatic analysis and annotation that is required for supporting intelligent search and delivery of this content remains an open issue. In this paper, a complete architecture for knowledge-assisted multimodal analysis of news-related multimedia content is presented, along with its constituent components. The proposed analysis architecture employs state-of-the-art methods for the analysis of each individual modality (visual, audio, text) separately and proposes a novel fusion technique based on the particular characteristics of news-related content for the combination of the individual modality analysis results. Experimental results on news broadcast video illustrate the usefulness of the proposed techniques in the automatic generation of semantic annotations.

A vectorial semantics approach to personality assessment.

PubMed

Neuman, Yair; Cohen, Yochai

2014-04-23

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.

Semantic Segmentation and Unregistered Building Detection from Uav Images Using a Deconvolutional Network

NASA Astrophysics Data System (ADS)

Ham, S.; Oh, Y.; Choi, K.; Lee, I.

2018-05-01

Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

Semantic integration of data on transcriptional regulation.

PubMed

Baitaluk, Michael; Ponomarenko, Julia

2010-07-01

Experimental and predicted data concerning gene transcriptional regulation are distributed among many heterogeneous sources. However, there are no resources to integrate these data automatically or to provide a 'one-stop shop' experience for users seeking information essential for deciphering and modeling gene regulatory networks. IntegromeDB, a semantic graph-based 'deep-web' data integration system that automatically captures, integrates and manages publicly available data concerning transcriptional regulation, as well as other relevant biological information, is proposed in this article. The problems associated with data integration are addressed by ontology-driven data mapping, multiple data annotation and heterogeneous data querying, also enabling integration of the user's data. IntegromeDB integrates over 100 experimental and computational data sources relating to genomics, transcriptomics, genetics, and functional and interaction data concerning gene transcriptional regulation in eukaryotes and prokaryotes. IntegromeDB is accessible through the integrated research environment BiologicalNetworks at http://www.BiologicalNetworks.org baitaluk@sdsc.edu Supplementary data are available at Bioinformatics online.

A Vectorial Semantics Approach to Personality Assessment

NASA Astrophysics Data System (ADS)

Neuman, Yair; Cohen, Yochai

2014-04-01

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.

A Vectorial Semantics Approach to Personality Assessment

PubMed Central

Neuman, Yair; Cohen, Yochai

2014-01-01

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy. PMID:24755833

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning.

PubMed

Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Rebholz-Schuhmann, Dietrich; Schofield, Paul N; Gkoutos, Georgios V

2011-01-01

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.

"What is relevant in a text document?": An interpretable machine learning approach

PubMed Central

Arras, Leila; Horn, Franziska; Montavon, Grégoire; Müller, Klaus-Robert

2017-01-01

Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text’s category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications. PMID:28800619

The semantic anatomical network: Evidence from healthy and brain-damaged patient populations.

PubMed

Fang, Yuxing; Han, Zaizhu; Zhong, Suyu; Gong, Gaolang; Song, Luping; Liu, Fangsong; Huang, Ruiwang; Du, Xiaoxia; Sun, Rong; Wang, Qiang; He, Yong; Bi, Yanchao

2015-09-01

Semantic processing is central to cognition and is supported by widely distributed gray matter (GM) regions and white matter (WM) tracts. The exact manner in which GM regions are anatomically connected to process semantics remains unknown. We mapped the semantic anatomical network (connectome) by conducting diffusion imaging tractography in 48 healthy participants across 90 GM "nodes," and correlating the integrity of each obtained WM edge and semantic performance across 80 brain-damaged patients. Fifty-three WM edges were obtained whose lower integrity associated with semantic deficits and together with their linked GM nodes constitute a semantic WM network. Graph analyses of this network revealed three structurally segregated modules that point to distinct semantic processing components and identified network hubs and connectors that are central in the communication across the subnetworks. Together, our results provide an anatomical framework of human semantic network, advancing the understanding of the structural substrates supporting semantic processing. © 2015 Wiley Periodicals, Inc.

Conceptual Model Formalization in a Semantic Interoperability Service Framework: Transforming Relational Database Schemas to OWL.

PubMed

Bravo, Carlos; Suarez, Carlos; González, Carolina; López, Diego; Blobel, Bernd

2014-01-01

Healthcare information is distributed through multiple heterogeneous and autonomous systems. Access to, and sharing of, distributed information sources are a challenging task. To contribute to meeting this challenge, this paper presents a formal, complete and semi-automatic transformation service from Relational Databases to Web Ontology Language. The proposed service makes use of an algorithm that allows to transform several data models of different domains by deploying mainly inheritance rules. The paper emphasizes the relevance of integrating the proposed approach into an ontology-based interoperability service to achieve semantic interoperability.

Effective Web and Desktop Retrieval with Enhanced Semantic Spaces

NASA Astrophysics Data System (ADS)

Daoud, Amjad M.

We describe the design and implementation of the NETBOOK prototype system for collecting, structuring and efficiently creating semantic vectors for concepts, noun phrases, and documents from a corpus of free full text ebooks available on the World Wide Web. Automatic generation of concept maps from correlated index terms and extracted noun phrases are used to build a powerful conceptual index of individual pages. To ensure scalabilty of our system, dimension reduction is performed using Random Projection [13]. Furthermore, we present a complete evaluation of the relative effectiveness of the NETBOOK system versus the Google Desktop [8].

Covert colour processing in colour agnosia.

PubMed

Nijboer, Tanja C W; van Zandvoort, Martine J E; de Haan, Edward H F

2006-01-01

Patients with colour agnosia can perceive colours and are able to match coloured patches on hue, but are unable to identify or categorise colours. It is a rare condition and there is as yet no agreement on the clinical definition or a generally accepted explanation. In line with observations from object agnosia and prosopagnosia, we hypothesised that (some of) these patients might still be able to process colour information at an implicit level. In this study, we investigated this possibility of implicit access to colour semantics and colour names in a man (MAH) who suffers from developmental colour agnosia. We designed two experimental computer tasks: an associative colour priming task with a lexical decision response and a reversed Stroop task. The results of these experiments suggest that there is indeed automatic processing of colour, although MAH was unable to explicitly use colour information.

Automatic guidance of attention during real-world visual search

PubMed Central

Seidl-Rathkopf, Katharina N.; Turk-Browne, Nicholas B.; Kastner, Sabine

2015-01-01

Looking for objects in cluttered natural environments is a frequent task in everyday life. This process can be difficult, as the features, locations, and times of appearance of relevant objects are often not known in advance. A mechanism by which attention is automatically biased toward information that is potentially relevant may thus be helpful. Here we tested for such a mechanism across five experiments by engaging participants in real-world visual search and then assessing attentional capture for information that was related to the search set but was otherwise irrelevant. Isolated objects captured attention while preparing to search for objects from the same category embedded in a scene, as revealed by lower detection performance (Experiment 1A). This capture effect was driven by a central processing bottleneck rather than the withdrawal of spatial attention (Experiment 1B), occurred automatically even in a secondary task (Experiment 2A), and reflected enhancement of matching information rather than suppression of non-matching information (Experiment 2B). Finally, attentional capture extended to objects that were semantically associated with the target category (Experiment 3). We conclude that attention is efficiently drawn towards a wide range of information that may be relevant for an upcoming real-world visual search. This mechanism may be adaptive, allowing us to find information useful for our behavioral goals in the face of uncertainty. PMID:25898897

An investigation into semantic and phonological processing in individuals with Williams syndrome.

PubMed

Lee, Cheryl S; Binder, Katherine S

2014-02-01

The current study examined semantic and phonological processing in individuals with Williams syndrome (WS). Previous research in language processing in individuals with WS suggests a complex linguistic system characterized by "deviant" semantic organization and differential phonological processing. Two experiments explored these representations in individuals with WS. The first experiment analyzed the relative typicality and frequency of participants' responses to a semantic and phonological fluency task. The second experiment tapped into online language processing through a semantic priming task and an online sentence reading task measuring the effects of word frequency. Thirteen participants with WS were matched to a group of participants on reading grade level and a group of participants on chronological age. The results of the semantic fluency task, semantic priming task, and word frequency task suggest that semantic organization in individuals with WS appears commensurate with their reading level rather than deviant. The pattern of results suggests that individuals with WS do not appear to have deviant semantic organization, while confirming that online tasks that tap into these processes are a promising direction in investigations that include atypically developing populations. These findings for the phonological tasks warrant further research into phonological processing in individuals with WS.

Automatic video summarization driven by a spatio-temporal attention model

NASA Astrophysics Data System (ADS)

Barland, R.; Saadane, A.

2008-02-01

According to the literature, automatic video summarization techniques can be classified in two parts, following the output nature: "video skims", which are generated using portions of the original video and "key-frame sets", which correspond to the images, selected from the original video, having a significant semantic content. The difference between these two categories is reduced when we consider automatic procedures. Most of the published approaches are based on the image signal and use either pixel characterization or histogram techniques or image decomposition by blocks. However, few of them integrate properties of the Human Visual System (HVS). In this paper, we propose to extract keyframes for video summarization by studying the variations of salient information between two consecutive frames. For each frame, a saliency map is produced simulating the human visual attention by a bottom-up (signal-dependent) approach. This approach includes three parallel channels for processing three early visual features: intensity, color and temporal contrasts. For each channel, the variations of the salient information between two consecutive frames are computed. These outputs are then combined to produce the global saliency variation which determines the key-frames. Psychophysical experiments have been defined and conducted to analyze the relevance of the proposed key-frame extraction algorithm.

Extraction of UMLS® Concepts Using Apache cTAKES™ for German Language.

PubMed

Becker, Matthias; Böckmann, Britta

2016-01-01

Automatic information extraction of medical concepts and classification with semantic standards from medical reports is useful for standardization and for clinical research. This paper presents an approach for an UMLS concept extraction with a customized natural language processing pipeline for German clinical notes using Apache cTAKES. The objectives are, to test the natural language processing tool for German language if it is suitable to identify UMLS concepts and map these with SNOMED-CT. The German UMLS database and German OpenNLP models extended the natural language processing pipeline, so the pipeline can normalize to domain ontologies such as SNOMED-CT using the German concepts. For testing, the ShARe/CLEF eHealth 2013 training dataset translated into German was used. The implemented algorithms are tested with a set of 199 German reports, obtaining a result of average 0.36 F1 measure without German stemming, pre- and post-processing of the reports.

3D models mapping optimization through an integrated parameterization approach: cases studies from Ravenna

NASA Astrophysics Data System (ADS)

Cipriani, L.; Fantini, F.; Bertacchi, S.

2014-06-01

Image-based modelling tools based on SfM algorithms gained great popularity since several software houses provided applications able to achieve 3D textured models easily and automatically. The aim of this paper is to point out the importance of controlling models parameterization process, considering that automatic solutions included in these modelling tools can produce poor results in terms of texture utilization. In order to achieve a better quality of textured models from image-based modelling applications, this research presents a series of practical strategies aimed at providing a better balance between geometric resolution of models from passive sensors and their corresponding (u,v) map reference systems. This aspect is essential for the achievement of a high-quality 3D representation, since "apparent colour" is a fundamental aspect in the field of Cultural Heritage documentation. Complex meshes without native parameterization have to be "flatten" or "unwrapped" in the (u,v) parameter space, with the main objective to be mapped with a single image. This result can be obtained by using two different strategies: the former automatic and faster, while the latter manual and time-consuming. Reverse modelling applications provide automatic solutions based on splitting the models by means of different algorithms, that produce a sort of "atlas" of the original model in the parameter space, in many instances not adequate and negatively affecting the overall quality of representation. Using in synergy different solutions, ranging from semantic aware modelling techniques to quad-dominant meshes achieved using retopology tools, it is possible to obtain a complete control of the parameterization process.

Building a semi-automatic ontology learning and construction system for geosciences

NASA Astrophysics Data System (ADS)

Babaie, H. A.; Sunderraman, R.; Zhu, Y.

2013-12-01

We are developing an ontology learning and construction framework that allows continuous, semi-automatic knowledge extraction, verification, validation, and maintenance by potentially a very large group of collaborating domain experts in any geosciences field. The system brings geoscientists from the side-lines to the center stage of ontology building, allowing them to collaboratively construct and enrich new ontologies, and merge, align, and integrate existing ontologies and tools. These constantly evolving ontologies can more effectively address community's interests, purposes, tools, and change. The goal is to minimize the cost and time of building ontologies, and maximize the quality, usability, and adoption of ontologies by the community. Our system will be a domain-independent ontology learning framework that applies natural language processing, allowing users to enter their ontology in a semi-structured form, and a combined Semantic Web and Social Web approach that lets direct participation of geoscientists who have no skill in the design and development of their domain ontologies. A controlled natural language (CNL) interface and an integrated authoring and editing tool automatically convert syntactically correct CNL text into formal OWL constructs. The WebProtege-based system will allow a potentially large group of geoscientists, from multiple domains, to crowd source and participate in the structuring of their knowledge model by sharing their knowledge through critiquing, testing, verifying, adopting, and updating of the concept models (ontologies). We will use cloud storage for all data and knowledge base components of the system, such as users, domain ontologies, discussion forums, and semantic wikis that can be accessed and queried by geoscientists in each domain. We will use NoSQL databases such as MongoDB as a service in the cloud environment. MongoDB uses the lightweight JSON format, which makes it convenient and easy to build Web applications using just HTML5 and Javascript, thereby avoiding cumbersome server side coding present in the traditional approaches. The JSON format used in MongoDB is also suitable for storing and querying RDF data. We will store the domain ontologies and associated linked data in JSON/RDF formats. Our Web interface will be built upon the open source and configurable WebProtege ontology editor. We will develop a simplified mobile version of our user interface which will automatically detect the hosting device and adjust the user interface layout to accommodate different screen sizes. We will also use the Semantic Media Wiki that allows the user to store and query the data within the wiki pages. By using HTML 5, JavaScript, and WebGL, we aim to create an interactive, dynamic, and multi-dimensional user interface that presents various geosciences data sets in a natural and intuitive way.

Social anhedonia associated with poor evaluative processing but not with poor cognitive control.

PubMed

Martin, Elizabeth A; Kerns, John G

2010-07-30

Emotion researchers have distinguished between automatic vs. controlled processing of evaluative information. There is suggestive evidence that social anhedonia might be associated with problems in controlled evaluative processing. The current study examined whether college students with elevated social anhedonia would exhibit an increased processing effect on tasks involving either evaluative processing or cognitive control. On an evaluative processing task, affective primes and targets could be either congruent or incongruent and participants judged the valence of targets. On a cognitive control task, participants completed the color-naming Stroop task. Compared to control participants (n=47), people with elevated social anhedonia (n=27) exhibited an increased evaluative processing effect as they were slower and made more errors for incongruent than for congruent trials on the evaluative processing task. In contrast, there were no group differences on the Stroop task or on a semantic priming task. Overall, these results suggest that people with elevated social anhedonia might have problems with some aspects of evaluative processing. Copyright 2009 Elsevier Ltd. All rights reserved.

Semantic Service Design for Collaborative Business Processes in Internetworked Enterprises

NASA Astrophysics Data System (ADS)

Bianchini, Devis; Cappiello, Cinzia; de Antonellis, Valeria; Pernici, Barbara

Modern collaborating enterprises can be seen as borderless organizations whose processes are dynamically transformed and integrated with the ones of their partners (Internetworked Enterprises, IE), thus enabling the design of collaborative business processes. The adoption of Semantic Web and service-oriented technologies for implementing collaboration in such distributed and heterogeneous environments promises significant benefits. IE can model their own processes independently by using the Software as a Service paradigm (SaaS). Each enterprise maintains a catalog of available services and these can be shared across IE and reused to build up complex collaborative processes. Moreover, each enterprise can adopt its own terminology and concepts to describe business processes and component services. This brings requirements to manage semantic heterogeneity in process descriptions which are distributed across different enterprise systems. To enable effective service-based collaboration, IEs have to standardize their process descriptions and model them through component services using the same approach and principles. For enabling collaborative business processes across IE, services should be designed following an homogeneous approach, possibly maintaining a uniform level of granularity. In the paper we propose an ontology-based semantic modeling approach apt to enrich and reconcile semantics of process descriptions to facilitate process knowledge management and to enable semantic service design (by discovery, reuse and integration of process elements/constructs). The approach brings together Semantic Web technologies, techniques in process modeling, ontology building and semantic matching in order to provide a comprehensive semantic modeling framework.

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

PubMed

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

2013-07-01

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

Evidence of semantic processing impairments in behavioural variant frontotemporal dementia and Parkinson's disease.

PubMed

Cousins, Katheryn A Q; Grossman, Murray

2017-12-01

Category-specific impairments caused by brain damage can provide important insights into how semantic concepts are organized in the brain. Recent research has demonstrated that disease to sensory and motor cortices can impair perceptual feature knowledge important to the representation of semantic concepts. This evidence supports the grounded cognition theory of semantics, the view that lexical knowledge is partially grounded in perceptual experience and that sensory and motor regions support semantic representations. Less well understood, however, is how heteromodal semantic hubs work to integrate and process semantic information. Although the majority of semantic research to date has focused on how sensory cortical areas are important for the representation of semantic features, new research explores how semantic memory is affected by neurodegeneration in regions important for semantic processing. Here, we review studies that demonstrate impairments to abstract noun knowledge in behavioural variant frontotemporal degeneration (bvFTD) and to action verb knowledge in Parkinson's disease, and discuss how these deficits relate to disease of the semantic selection network. Findings demonstrate that semantic selection processes are supported by the left inferior frontal gyrus (LIFG) and basal ganglia, and that disease to these regions in bvFTD and Parkinson's disease can lead to categorical impairments for abstract nouns and action verbs, respectively.

Individual Variability in the Semantic Processing of English Compound Words

ERIC Educational Resources Information Center

Schmidtke, Daniel; Van Dyke, Julie A.; Kuperman, Victor

2018-01-01

Semantic transparency effects during compound word recognition provide critical insight into the organization of semantic knowledge and the nature of semantic processing. The past 25 years of psycholinguistic research on compound semantic transparency has produced discrepant effects, leaving the existence and nature of its influence unresolved. In…

Semantic Memory in the Clinical Progression of Alzheimer Disease.

PubMed

Tchakoute, Christophe T; Sainani, Kristin L; Henderson, Victor W

2017-09-01

Semantic memory measures may be useful in tracking and predicting progression of Alzheimer disease. We investigated relationships among semantic memory tasks and their 1-year predictive value in women with Alzheimer disease. We conducted secondary analyses of a randomized clinical trial of raloxifene in 42 women with late-onset mild-to-moderate Alzheimer disease. We assessed semantic memory with tests of oral confrontation naming, category fluency, semantic recognition and semantic naming, and semantic density in written narrative discourse. We measured global cognition (Alzheimer Disease Assessment Scale, cognitive subscale), dementia severity (Clinical Dementia Rating sum of boxes), and daily function (Activities of Daily Living Inventory) at baseline and 1 year. At baseline and 1 year, most semantic memory scores correlated highly or moderately with each other and with global cognition, dementia severity, and daily function. Semantic memory task performance at 1 year had worsened one-third to one-half standard deviation. Factor analysis of baseline test scores distinguished processes in semantic and lexical retrieval (semantic recognition, semantic naming, confrontation naming) from processes in lexical search (semantic density, category fluency). The semantic-lexical retrieval factor predicted global cognition at 1 year. Considered separately, baseline confrontation naming and category fluency predicted dementia severity, while semantic recognition and a composite of semantic recognition and semantic naming predicted global cognition. No individual semantic memory test predicted daily function. Semantic-lexical retrieval and lexical search may represent distinct aspects of semantic memory. Semantic memory processes are sensitive to cognitive decline and dementia severity in Alzheimer disease.

Incorporating Semantics into Data Driven Workflows for Content Based Analysis

NASA Astrophysics Data System (ADS)

Argüello, M.; Fernandez-Prieto, M. J.

Finding meaningful associations between text elements and knowledge structures within clinical narratives in a highly verbal domain, such as psychiatry, is a challenging goal. The research presented here uses a small corpus of case histories and brings into play pre-existing knowledge, and therefore, complements other approaches that use large corpus (millions of words) and no pre-existing knowledge. The paper describes a variety of experiments for content-based analysis: Linguistic Analysis using NLP-oriented approaches, Sentiment Analysis, and Semantically Meaningful Analysis. Although it is not standard practice, the paper advocates providing automatic support to annotate the functionality as well as the data for each experiment by performing semantic annotation that uses OWL and OWL-S. Lessons learnt can be transmitted to legacy clinical databases facing the conversion of clinical narratives according to prominent Electronic Health Records standards.

Live Social Semantics

NASA Astrophysics Data System (ADS)

Alani, Harith; Szomszor, Martin; Cattuto, Ciro; van den Broeck, Wouter; Correndo, Gianluca; Barrat, Alain

Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web 2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment.

A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

NASA Astrophysics Data System (ADS)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.

Scene Semantic Segmentation from Indoor Rgb-D Images Using Encode-Decoder Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Wang, Z.; Li, T.; Pan, L.; Kang, Z.

2017-09-01

With increasing attention for the indoor environment and the development of low-cost RGB-D sensors, indoor RGB-D images are easily acquired. However, scene semantic segmentation is still an open area, which restricts indoor applications. The depth information can help to distinguish the regions which are difficult to be segmented out from the RGB images with similar color or texture in the indoor scenes. How to utilize the depth information is the key problem of semantic segmentation for RGB-D images. In this paper, we propose an Encode-Decoder Fully Convolutional Networks for RGB-D image classification. We use Multiple Kernel Maximum Mean Discrepancy (MK-MMD) as a distance measure to find common and special features of RGB and D images in the network to enhance performance of classification automatically. To explore better methods of applying MMD, we designed two strategies; the first calculates MMD for each feature map, and the other calculates MMD for whole batch features. Based on the result of classification, we use the full connect CRFs for the semantic segmentation. The experimental results show that our method can achieve a good performance on indoor RGB-D image semantic segmentation.

Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.

Knowledge Provenance in Semantic Wikis

NASA Astrophysics Data System (ADS)

Ding, L.; Bao, J.; McGuinness, D. L.

2008-12-01

Collaborative online environments with a technical Wiki infrastructure are becoming more widespread. One of the strengths of a Wiki environment is that it is relatively easy for numerous users to contribute original content and modify existing content (potentially originally generated by others). As more users begin to depend on informational content that is evolving by Wiki communities, it becomes more important to track the provenance of the information. Semantic Wikis expand upon traditional Wiki environments by adding some computationally understandable encodings of some of the terms and relationships in Wikis. We have developed a semantic Wiki environment that expands a semantic Wiki with provenance markup. Provenance of original contributions as well as modifications is encoded using the provenance markup component of the Proof Markup Language. The Wiki environment provides the provenance markup automatically, thus users are not required to make specific encodings of author, contribution date, and modification trail. Further, our Wiki environment includes a search component that understands the provenance primitives and thus can be used to provide a provenance-aware search facility. We will describe the knowledge provenance infrastructure of our Semantic Wiki and show how it is being used as the foundation of our group web site as well as a number of project web sites.

Learning to segment mouse embryo cells

NASA Astrophysics Data System (ADS)

León, Juan; Pardo, Alejandro; Arbeláez, Pablo

2017-11-01

Recent advances in microscopy enable the capture of temporal sequences during cell development stages. However, the study of such sequences is a complex task and time consuming task. In this paper we propose an automatic strategy to adders the problem of semantic and instance segmentation of mouse embryos using NYU's Mouse Embryo Tracking Database. We obtain our instance proposals as refined predictions from the generalized hough transform, using prior knowledge of the embryo's locations and their current cell stage. We use two main approaches to learn the priors: Hand crafted features and automatic learned features. Our strategy increases the baseline jaccard index from 0.12 up to 0.24 using hand crafted features and 0.28 by using automatic learned ones.

Investigating the Potential of Deep Neural Networks for Large-Scale Classification of Very High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Postadjian, T.; Le Bris, A.; Sahbi, H.; Mallet, C.

2017-05-01

Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.

Automated software system for checking the structure and format of ACM SIG documents

NASA Astrophysics Data System (ADS)

Mirza, Arsalan Rahman; Sah, Melike

2017-04-01

Microsoft (MS) Office Word is one of the most commonly used software tools for creating documents. MS Word 2007 and above uses XML to represent the structure of MS Word documents. Metadata about the documents are automatically created using Office Open XML (OOXML) syntax. We develop a new framework, which is called ADFCS (Automated Document Format Checking System) that takes the advantage of the OOXML metadata, in order to extract semantic information from MS Office Word documents. In particular, we develop a new ontology for Association for Computing Machinery (ACM) Special Interested Group (SIG) documents for representing the structure and format of these documents by using OWL (Web Ontology Language). Then, the metadata is extracted automatically in RDF (Resource Description Framework) according to this ontology using the developed software. Finally, we generate extensive rules in order to infer whether the documents are formatted according to ACM SIG standards. This paper, introduces ACM SIG ontology, metadata extraction process, inference engine, ADFCS online user interface, system evaluation and user study evaluations.

Probabilistic Perception, Empathy, and Dynamic Homeostasis: Insights in Autism Spectrum Disorders and Conduct Disorders

PubMed Central

Guilé, Jean Marc

2013-01-01

Homeostasis is not a permanent and stable state but instead results from conflicting forces. Therefore, infants have to engage in dynamic exchanges with their environment, in biological, cognitive, and affective domains. Empathy is an adaptive response to these environmental challenges, which contributes to reaching proper dynamic homeostasis and development. Empathy relies on implicit interactive processes, namely probabilistic perception and synchrony, which will be reviewed in the article. If typically-developed neonates are fully equipped to automatically and synchronously interact with their human environment, conduct disorders (CD) and autism spectrum disorders (ASD) present with impairments in empathetic communication, e.g., emotional arousal and facial emotion processing. In addition sensorimotor resonance is lacking in ASD, and emotional concern and semantic empathy are impaired in CD with Callous-Unemotional traits. PMID:24479115

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

PubMed

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

2017-03-01

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

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

PubMed Central

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

2017-01-01

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

Automatic analysis of medical dialogue in the home hemodialysis domain: structure induction and summarization.

PubMed

Lacson, Ronilda C; Barzilay, Regina; Long, William J

2006-10-01

Spoken medical dialogue is a valuable source of information for patients and caregivers. This work presents a first step towards automatic analysis and summarization of spoken medical dialogue. We first abstract a dialogue into a sequence of semantic categories using linguistic and contextual features integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). We then describe and implement a summarizer that utilizes this automatically induced structure. Our evaluation results indicate that automatically generated summaries exhibit high resemblance to summaries written by humans. In addition, task-based evaluation shows that physicians can reasonably answer questions related to patient care by looking at the automatically generated summaries alone, in contrast to the physicians' performance when they were given summaries from a naïve summarizer (p<0.05). This work demonstrates the feasibility of automatically structuring and summarizing spoken medical dialogue.

Logic-based assessment of the compatibility of UMLS ontology sources

PubMed Central

2011-01-01

Background The UMLS Metathesaurus (UMLS-Meta) is currently the most comprehensive effort for integrating independently-developed medical thesauri and ontologies. UMLS-Meta is being used in many applications, including PubMed and ClinicalTrials.gov. The integration of new sources combines automatic techniques, expert assessment, and auditing protocols. The automatic techniques currently in use, however, are mostly based on lexical algorithms and often disregard the semantics of the sources being integrated. Results In this paper, we argue that UMLS-Meta’s current design and auditing methodologies could be significantly enhanced by taking into account the logic-based semantics of the ontology sources. We provide empirical evidence suggesting that UMLS-Meta in its 2009AA version contains a significant number of errors; these errors become immediately apparent if the rich semantics of the ontology sources is taken into account, manifesting themselves as unintended logical consequences that follow from the ontology sources together with the information in UMLS-Meta. We then propose general principles and specific logic-based techniques to effectively detect and repair such errors. Conclusions Our results suggest that the methodologies employed in the design of UMLS-Meta are not only very costly in terms of human effort, but also error-prone. The techniques presented here can be useful for both reducing human effort in the design and maintenance of UMLS-Meta and improving the quality of its contents. PMID:21388571

Simplifying the Reuse and Interoperability of Geoscience Data Sets and Models with Semantic Metadata that is Human-Readable and Machine-actionable

NASA Astrophysics Data System (ADS)

Peckham, S. D.

2017-12-01

Standardized, deep descriptions of digital resources (e.g. data sets, computational models, software tools and publications) make it possible to develop user-friendly software systems that assist scientists with the discovery and appropriate use of these resources. Semantic metadata makes it possible for machines to take actions on behalf of humans, such as automatically identifying the resources needed to solve a given problem, retrieving them and then automatically connecting them (despite their heterogeneity) into a functioning workflow. Standardized model metadata also helps model users to understand the important details that underpin computational models and to compare the capabilities of different models. These details include simplifying assumptions on the physics, governing equations and the numerical methods used to solve them, discretization of space (the grid) and time (the time-stepping scheme), state variables (input or output), model configuration parameters. This kind of metadata provides a "deep description" of a computational model that goes well beyond other types of metadata (e.g. author, purpose, scientific domain, programming language, digital rights, provenance, execution) and captures the science that underpins a model. A carefully constructed, unambiguous and rules-based schema to address this problem, called the Geoscience Standard Names ontology will be presented that utilizes Semantic Web best practices and technologies. It has also been designed to work across science domains and to be readable by both humans and machines.

Semantic richness effects in lexical decision: The role of feedback.

PubMed

Yap, Melvin J; Lim, Gail Y; Pexman, Penny M

2015-11-01

Across lexical processing tasks, it is well established that words with richer semantic representations are recognized faster. This suggests that the lexical system has access to meaning before a word is fully identified, and is consistent with a theoretical framework based on interactive and cascaded processing. Specifically, semantic richness effects are argued to be produced by feedback from semantic representations to lower-level representations. The present study explores the extent to which richness effects are mediated by feedback from lexical- to letter-level representations. In two lexical decision experiments, we examined the joint effects of stimulus quality and four semantic richness dimensions (imageability, number of features, semantic neighborhood density, semantic diversity). With the exception of semantic diversity, robust additive effects of stimulus quality and richness were observed for the targeted dimensions. Our results suggest that semantic feedback does not typically reach earlier levels of representation in lexical decision, and further reinforces the idea that task context modulates the processing dynamics of early word recognition processes.

Syntax does not necessarily precede semantics in sentence processing: ERP evidence from Chinese.

PubMed

Zhang, Yaxu; Li, Ping; Piao, Qiuhong; Liu, Youyi; Huang, Yongjing; Shu, Hua

2013-07-01

Two event-related potential experiments were conducted to examine whether the processing of syntactic category or syntactic subcategorization frame always needs to temporally precede semantic processing during the reading of Chinese sentences of object-subject-verb construction. The sentences contained (a) no anomalies, (b) semantic only anomalies, (c) syntactic category plus semantic anomalies, or (d) transitivity plus semantic anomalies. In both experiments, all three types of anomalies elicited a broad negativity between 300 and 500 ms. This negativity included an N400 effect, given its distribution. Moreover, syntactic category plus semantic anomalies elicited a P600 response, whereas the other two types of anomalies did not. The finding of N400 effects suggests that semantic integration can be attempted even when the processing of syntactic category or syntactic subcategorization frame is unsuccessful. Thus, syntactic processing is not a necessary prerequisite for the initiation of semantic integration in Chinese. Copyright © 2013 Elsevier Inc. All rights reserved.

The Function of Semantics in Automated Language Processing.

ERIC Educational Resources Information Center

Pacak, Milos; Pratt, Arnold W.

This paper is a survey of some of the major semantic models that have been developed for automated semantic analysis of natural language. Current approaches to semantic analysis and logical interference are based mainly on models of human cognitive processes such as Quillian's semantic memory, Simmon's Protosynthex III and others. All existing…

Attractor Dynamics and Semantic Neighborhood Density: Processing Is Slowed by Near Neighbors and Speeded by Distant Neighbors

ERIC Educational Resources Information Center

Mirman, Daniel; Magnuson, James S.

2008-01-01

The authors investigated semantic neighborhood density effects on visual word processing to examine the dynamics of activation and competition among semantic representations. Experiment 1 validated feature-based semantic representations as a basis for computing semantic neighborhood density and suggested that near and distant neighbors have…

Automatically exposing OpenLifeData via SADI semantic Web Services.

PubMed

González, Alejandro Rodríguez; Callahan, Alison; Cruz-Toledo, José; Garcia, Adrian; Egaña Aranguren, Mikel; Dumontier, Michel; Wilkinson, Mark D

2014-01-01

Two distinct trends are emerging with respect to how data is shared, collected, and analyzed within the bioinformatics community. First, Linked Data, exposed as SPARQL endpoints, promises to make data easier to collect and integrate by moving towards the harmonization of data syntax, descriptive vocabularies, and identifiers, as well as providing a standardized mechanism for data access. Second, Web Services, often linked together into workflows, normalize data access and create transparent, reproducible scientific methodologies that can, in principle, be re-used and customized to suit new scientific questions. Constructing queries that traverse semantically-rich Linked Data requires substantial expertise, yet traditional RESTful or SOAP Web Services cannot adequately describe the content of a SPARQL endpoint. We propose that content-driven Semantic Web Services can enable facile discovery of Linked Data, independent of their location. We use a well-curated Linked Dataset - OpenLifeData - and utilize its descriptive metadata to automatically configure a series of more than 22,000 Semantic Web Services that expose all of its content via the SADI set of design principles. The OpenLifeData SADI services are discoverable via queries to the SHARE registry and easy to integrate into new or existing bioinformatics workflows and analytical pipelines. We demonstrate the utility of this system through comparison of Web Service-mediated data access with traditional SPARQL, and note that this approach not only simplifies data retrieval, but simultaneously provides protection against resource-intensive queries. We show, through a variety of different clients and examples of varying complexity, that data from the myriad OpenLifeData can be recovered without any need for prior-knowledge of the content or structure of the SPARQL endpoints. We also demonstrate that, via clients such as SHARE, the complexity of federated SPARQL queries is dramatically reduced.

About Edible Restaurants: Conflicts between Syntax and Semantics as Revealed by ERPs

PubMed Central

Kos, Miriam; Vosse, Theo; van den Brink, Daniëlle; Hagoort, Peter

2010-01-01

In order to investigate conflicts between semantics and syntax, we recorded ERPs, while participants read Dutch sentences. Sentences containing conflicts between syntax and semantics (Fred eats in a sandwich…/Fred eats a restaurant…) elicited an N400. These results show that conflicts between syntax and semantics not necessarily lead to P600 effects and are in line with the processing competition account. According to this parallel account the syntactic and semantic processing streams are fully interactive and information from one level can influence the processing at another level. The relative strength of the cues of the processing streams determines which level is affected most strongly by the conflict. The processing competition account maintains the distinction between the N400 as index for semantic processing and the P600 as index for structural processing. PMID:21833277

Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation

PubMed Central

Huang, Chung-Chi; Lu, Zhiyong

2016-01-01

Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as ‘CHEMICAL-1 compared to CHEMICAL-2.’ With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical–disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked order covering diverse bio-entity relations. To assess the potential utility of our automated top-ranked patterns of a given relation in semantic search, we performed a pilot study on frequently sought semantic relations in PubMed and observed improved literature retrieval effectiveness based on post-hoc human relevance evaluation. Further investigation in larger tests and in real-world scenarios is warranted. PMID:27016698

Intelligence related upper alpha desynchronization in a semantic memory task.

PubMed

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

2005-07-30

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

Towards an Effective Theory of Reformulation. Part 1; Semantics

NASA Technical Reports Server (NTRS)

Benjamin, D. Paul

1992-01-01

This paper describes an investigation into the structure of representations of sets of actions, utilizing semigroup theory. The goals of this project are twofold: to shed light on the relationship between tasks and representations, leading to a classification of tasks according to the representations they admit; and to develop techniques for automatically transforming representations so as to improve problem-solving performance. A method is demonstrated for automatically generating serial algorithms for representations whose actions form a finite group. This method is then extended to representations whose actions form a finite inverse semigroup.

Memory Lane Is a Two-Way Street.

ERIC Educational Resources Information Center

Sprenger, Marilee

1998-01-01

Our memories are not necessarily "bad," but stored in different areas. By understanding the five memory lanes (semantic, episodic, procedural, automatic, and emotional), a high school English teacher discovered why her students could not do fractions (to calculate grades) in English class. Paper-and-pencil tests can be redesigned to assess memory…

Analysis and Defense of Vulnerabilities in Binary Code

DTIC Science & Technology

2008-09-29

language . We demonstrate our techniques by automatically generating input filters from vulnerable binary programs. vi Acknowledgments I thank my wife, family...21 2.2 The Vine Intermediate Language . . . . . . . . . . . . . . . . . . . . . . 21 ix 2.2.1 Normalized Memory...The Traditional Weakest Precondition Semantics . . . . . . . . . . . . . 44 3.2.1 The Guarded Command Language . . . . . . . . . . . . . . . . . 44

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…

Semantic Similarity Graphs of Mathematics Word Problems: Can Terminology Detection Help?

ERIC Educational Resources Information Center

John, Rogers Jeffrey Leo; Passonneau, Rebecca J.; McTavish, Thomas S.

2015-01-01

Curricula often lack metadata to characterize the relatedness of concepts. To investigate automatic methods for generating relatedness metadata for a mathematics curriculum, we first address the task of identifying which terms in the vocabulary from mathematics word problems are associated with the curriculum. High chance-adjusted interannotator…

Automatic Summary Assessment for Intelligent Tutoring Systems

ERIC Educational Resources Information Center

He, Yulan; Hui, Siu Cheung; Quan, Tho Thanh

2009-01-01

Summary writing is an important part of many English Language Examinations. As grading students' summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to…

Enabling Creative Learning Design through Semantic Technologies

ERIC Educational Resources Information Center

Charlton, Patricia; Magoulas, George; Laurillard, Diana

2012-01-01

The paper advocates an approach to learning design that considers it as creating digital artefacts that can be extended, modified and used for different purposes. This is realised through an "act becoming artefact" cycle, where users' actions in the authors' software environment, named Learning Designer, are automatically interpreted on…

Free Recall Test Experience Potentiates Strategy-Driven Effects of Value on Memory

ERIC Educational Resources Information Center

Cohen, Michael S.; Rissman, Jesse; Hovhannisyan, Mariam; Castel, Alan D.; Knowlton, Barbara J.

2017-01-01

People tend to show better memory for information that is deemed valuable or important. By one mechanism, individuals selectively engage deeper, semantic encoding strategies for high value items (Cohen, Rissman, Suthana, Castel, & Knowlton, 2014). By another mechanism, information paired with value or reward is automatically strengthened in…

Automatically Assessing Graph-Based Diagrams

ERIC Educational Resources Information Center

Thomas, Pete; Smith, Neil; Waugh, Kevin

2008-01-01

To date there has been very little work on the machine understanding of imprecise diagrams, such as diagrams drawn by students in response to assessment questions. Imprecise diagrams exhibit faults such as missing, extraneous and incorrectly formed elements. The semantics of imprecise diagrams are difficult to determine. While there have been…

Visualizing Topic Flow in Students' Essays

ERIC Educational Resources Information Center

O'Rourke, Stephen T.; Calvo, Rafael A.; McNamara, Danielle S.

2011-01-01

Visualizing how the parts of a document relate to each other and producing automatically generated quality measures that people can understand are means that writers can use to improve the quality of their compositions. This paper presents a novel document visualization technique and a measure of quality based on the average semantic distance…

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

PubMed Central

Rose, Johann Christian; Kicherer, Anna; Wieland, Markus; Klingbeil, Lasse; Töpfer, Reinhard; Kuhlmann, Heiner

2016-01-01

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter. PMID:27983669

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions.

PubMed

Rose, Johann Christian; Kicherer, Anna; Wieland, Markus; Klingbeil, Lasse; Töpfer, Reinhard; Kuhlmann, Heiner

2016-12-15

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.

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

The Semantic Distance Task: Quantifying Semantic Distance with Semantic Network Path Length

ERIC Educational Resources Information Center

Kenett, Yoed N.; Levi, Effi; Anaki, David; Faust, Miriam

2017-01-01

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We…

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

PubMed

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

2014-01-01

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

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

PubMed

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

2016-02-01

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

Multiple Meanings Are Not Necessarily a Disadvantage in Semantic Processing: Evidence from Homophone Effects in Semantic Categorisation

ERIC Educational Resources Information Center

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

2007-01-01

The ambiguity disadvantage (slower processing of ambiguous words relative to unambiguous words) has been taken as evidence for a distributed semantic representational system like that embodied in parallel distributed processing (PDP) models. In the present study, we investigated whether semantic ambiguity slows meaning activation, as PDP models…

Levels of processing with free and cued recall and unilateral temporal lobe epilepsy.

PubMed

Lespinet-Najib, Véronique; N'Kaoua, Bernard; Sauzéon, Hélène; Bresson, Christel; Rougier, Alain; Claverie, Bernard

2004-04-01

This study investigates the role of the temporal lobes in levels-of-processing tasks (phonetic and semantic encoding) according to the nature of recall tasks (free and cued recall). These tasks were administered to 48 patients with unilateral temporal epilepsy (right "RTLE"=24; left "LTLE"=24) and a normal group (n=24). The results indicated that LTLE patients were impaired for semantic processing (free and cued recall) and for phonetic processing (free and cued recall), while for RTLE patients deficits appeared in free recall with semantic processing. It is suggested that the left temporal lobe is involved in all aspects of verbal memory, and that the right temporal lobe is specialized in semantic processing. Moreover, our data seem to indicate that RTLE patients present a retrieval processing impairment (semantic condition), whereas the LTLE group is characterized by encoding difficulties in the phonetic and semantic condition.

Structure before Meaning: Sentence Processing, Plausibility, and Subcategorization

PubMed Central

Kizach, Johannes; Nyvad, Anne Mette; Christensen, Ken Ramshøj

2013-01-01

Natural language processing is a fast and automatized process. A crucial part of this process is parsing, the online incremental construction of a syntactic structure. The aim of this study was to test whether a wh-filler extracted from an embedded clause is initially attached as the object of the matrix verb with subsequent reanalysis, and if so, whether the plausibility of such an attachment has an effect on reaction time. Finally, we wanted to examine whether subcategorization plays a role. We used a method called G-Maze to measure response time in a self-paced reading design. The experiments confirmed that there is early attachment of fillers to the matrix verb. When this attachment is implausible, the off-line acceptability of the whole sentence is significantly reduced. The on-line results showed that G-Maze was highly suited for this type of experiment. In accordance with our predictions, the results suggest that the parser ignores (or has no access to information about) implausibility and attaches fillers as soon as possible to the matrix verb. However, the results also show that the parser uses the subcategorization frame of the matrix verb. In short, the parser ignores semantic information and allows implausible attachments but adheres to information about which type of object a verb can take, ensuring that the parser does not make impossible attachments. We argue that the evidence supports a syntactic parser informed by syntactic cues, rather than one guided by semantic cues or one that is blind, or completely autonomous. PMID:24116101

Structure before meaning: sentence processing, plausibility, and subcategorization.

PubMed

Kizach, Johannes; Nyvad, Anne Mette; Christensen, Ken Ramshøj

2013-01-01

Natural language processing is a fast and automatized process. A crucial part of this process is parsing, the online incremental construction of a syntactic structure. The aim of this study was to test whether a wh-filler extracted from an embedded clause is initially attached as the object of the matrix verb with subsequent reanalysis, and if so, whether the plausibility of such an attachment has an effect on reaction time. Finally, we wanted to examine whether subcategorization plays a role. We used a method called G-Maze to measure response time in a self-paced reading design. The experiments confirmed that there is early attachment of fillers to the matrix verb. When this attachment is implausible, the off-line acceptability of the whole sentence is significantly reduced. The on-line results showed that G-Maze was highly suited for this type of experiment. In accordance with our predictions, the results suggest that the parser ignores (or has no access to information about) implausibility and attaches fillers as soon as possible to the matrix verb. However, the results also show that the parser uses the subcategorization frame of the matrix verb. In short, the parser ignores semantic information and allows implausible attachments but adheres to information about which type of object a verb can take, ensuring that the parser does not make impossible attachments. We argue that the evidence supports a syntactic parser informed by syntactic cues, rather than one guided by semantic cues or one that is blind, or completely autonomous.

OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval.

PubMed

Souvignet, Julien; Declerck, Gunnar; Asfari, Hadyl; Jaulent, Marie-Christine; Bousquet, Cédric

2016-10-01

Efficient searching and coding in databases that use terminological resources requires that they support efficient data retrieval. The Medical Dictionary for Regulatory Activities (MedDRA) is a reference terminology for several countries and organizations to code adverse drug reactions (ADRs) for pharmacovigilance. Ontologies that are available in the medical domain provide several advantages such as reasoning to improve data retrieval. The field of pharmacovigilance does not yet benefit from a fully operational ontology to formally represent the MedDRA terms. Our objective was to build a semantic resource based on formal description logic to improve MedDRA term retrieval and aid the generation of on-demand custom groupings by appropriately and efficiently selecting terms: OntoADR. The method consists of the following steps: (1) mapping between MedDRA terms and SNOMED-CT, (2) generation of semantic definitions using semi-automatic methods, (3) storage of the resource and (4) manual curation by pharmacovigilance experts. We built a semantic resource for ADRs enabling a new type of semantics-based term search. OntoADR adds new search capabilities relative to previous approaches, overcoming the usual limitations of computation using lightweight description logic, such as the intractability of unions or negation queries, bringing it closer to user needs. Our automated approach for defining MedDRA terms enabled the association of at least one defining relationship with 67% of preferred terms. The curation work performed on our sample showed an error level of 14% for this automated approach. We tested OntoADR in practice, which allowed us to build custom groupings for several medical topics of interest. The methods we describe in this article could be adapted and extended to other terminologies which do not benefit from a formal semantic representation, thus enabling better data retrieval performance. Our custom groupings of MedDRA terms were used while performing signal detection, which suggests that the graphical user interface we are currently implementing to process OntoADR could be usefully integrated into specialized pharmacovigilance software that rely on MedDRA. Copyright © 2016 Elsevier Inc. All rights reserved.

Semantic similarity-based alignment between clinical archetypes and SNOMED CT: an application to observations.

PubMed

Meizoso García, María; Iglesias Allones, José Luis; Martínez Hernández, Diego; Taboada Iglesias, María Jesús

2012-08-01

One of the main challenges of eHealth is semantic interoperability of health systems. But, this will only be possible if the capture, representation and access of patient data is standardized. Clinical data models, such as OpenEHR Archetypes, define data structures that are agreed by experts to ensure the accuracy of health information. In addition, they provide an option to normalize clinical data by means of binding terms used in the model definition to standard medical vocabularies. Nevertheless, the effort needed to establish the association between archetype terms and standard terminology concepts is considerable. Therefore, the purpose of this study is to provide an automated approach to bind OpenEHR archetypes terms to the external terminology SNOMED CT, with the capability to do it at a semantic level. This research uses lexical techniques and external terminological tools in combination with context-based techniques, which use information about structural and semantic proximity to identify similarities between terms and so, to find alignments between them. The proposed approach exploits both the structural context of archetypes and the terminology context, in which concepts are logically defined through the relationships (hierarchical and definitional) to other concepts. A set of 25 OBSERVATION archetypes with 477 bound terms was used to test the method. Of these, 342 terms (74.6%) were linked with 96.1% precision, 71.7% recall and 1.23 SNOMED CT concepts on average for each mapping. It has been detected that about one third of the archetype clinical information is grouped logically. Context-based techniques take advantage of this to increase the recall and to validate a 30.4% of the bindings produced by lexical techniques. This research shows that it is possible to automatically map archetype terms to a standard terminology with a high precision and recall, with the help of appropriate contextual and semantic information of both models. Moreover, the semantic-based methods provide a means of validating and disambiguating the resulting bindings. Therefore, this work is a step forward to reduce the human participation in the mapping process. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Lexical Processing in School-Age Children with Autism Spectrum Disorder and Children with Specific Language Impairment: The Role of Semantics.

PubMed

Haebig, Eileen; Kaushanskaya, Margarita; Ellis Weismer, Susan

2015-12-01

Children with autism spectrum disorder (ASD) and specific language impairment (SLI) often have immature lexical-semantic knowledge; however, the organization of lexical-semantic knowledge is poorly understood. This study examined lexical processing in school-age children with ASD, SLI, and typical development, who were matched on receptive vocabulary. Children completed a lexical decision task, involving words with high and low semantic network sizes and nonwords. Children also completed nonverbal updating and shifting tasks. Children responded more accurately to words from high than from low semantic networks; however, follow-up analyses identified weaker semantic network effects in the SLI group. Additionally, updating and shifting abilities predicted lexical processing, demonstrating similarity in the mechanisms which underlie semantic processing in children with ASD, SLI, and typical development.

Lexical Processing in School-Age Children with Autism Spectrum Disorder and Children with Specific Language Impairment: The Role of Semantics

PubMed Central

Haebig, Eileen; Kaushanskaya, Margarita; Weismer, Susan Ellis

2016-01-01

Children with autism spectrum disorder (ASD) and specific language impairment (SLI) often have immature lexical-semantic knowledge; however, the organization of lexical-semantic knowledge is poorly understood. This study examined lexical processing in school-age children with ASD, SLI, and typical development, who were matched on receptive vocabulary. Children completed a lexical decision task, involving words with high and low semantic network sizes and nonwords. Children also completed nonverbal updating and shifting tasks. Children responded more accurately to words from high than from low semantic networks; however, follow-up analyses identified weaker semantic network effects in the SLI group. Additionally, updating and shifting abilities predicted lexical processing, demonstrating similarity in the mechanisms which underlie semantic processing in children with ASD, SLI, and typical development. PMID:26210517

Using Semantic Components to Represent Dynamics of an Interdisciplinary Healthcare Team in a Multi-Agent Decision Support System.

PubMed

Wilk, Szymon; Kezadri-Hamiaz, Mounira; Rosu, Daniela; Kuziemsky, Craig; Michalowski, Wojtek; Amyot, Daniel; Carrier, Marc

2016-02-01

In healthcare organizations, clinical workflows are executed by interdisciplinary healthcare teams (IHTs) that operate in ways that are difficult to manage. Responding to a need to support such teams, we designed and developed the MET4 multi-agent system that allows IHTs to manage patients according to presentation-specific clinical workflows. In this paper, we describe a significant extension of the MET4 system that allows for supporting rich team dynamics (understood as team formation, management and task-practitioner allocation), including selection and maintenance of the most responsible physician and more complex rules of selecting practitioners for the workflow tasks. In order to develop this extension, we introduced three semantic components: (1) a revised ontology describing concepts and relations pertinent to IHTs, workflows, and managed patients, (2) a set of behavioral rules describing the team dynamics, and (3) an instance base that stores facts corresponding to instances of concepts from the ontology and to relations between these instances. The semantic components are represented in first-order logic and they can be automatically processed using theorem proving and model finding techniques. We employ these techniques to find models that correspond to specific decisions controlling the dynamics of IHT. In the paper, we present the design of extended MET4 with a special focus on the new semantic components. We then describe its proof-of-concept implementation using the WADE multi-agent platform and the Z3 solver (theorem prover/model finder). We illustrate the main ideas discussed in the paper with a clinical scenario of an IHT managing a patient with chronic kidney disease.

Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia.

PubMed

Schlegel, Daniel R; Crowner, Chris; Elkin, Peter L

2015-01-01

Clinical terminologies and ontologies are often used in natural language processing/understanding tasks as a method for semantically tagging text. One ontology commonly used for this task is SNOMED CT. Natural language is rich and varied: many different combinations of words may be used to express the same idea. It is therefore essential that ontologies and terminologies have a rich set of synonyms. One source of synonyms is Wikipedia. We examine methods for aligning concepts in SNOMED CT with articles in Wikipedia so that newly-found synonyms may be added to SNOMED CT. Our experiments show promising results and provide guidance to researchers who wish to use Wikipedia for similar tasks.

Content-based TV sports video retrieval using multimodal analysis

NASA Astrophysics Data System (ADS)

Yu, Yiqing; Liu, Huayong; Wang, Hongbin; Zhou, Dongru

2003-09-01

In this paper, we propose content-based video retrieval, which is a kind of retrieval by its semantical contents. Because video data is composed of multimodal information streams such as video, auditory and textual streams, we describe a strategy of using multimodal analysis for automatic parsing sports video. The paper first defines the basic structure of sports video database system, and then introduces a new approach that integrates visual stream analysis, speech recognition, speech signal processing and text extraction to realize video retrieval. The experimental results for TV sports video of football games indicate that the multimodal analysis is effective for video retrieval by quickly browsing tree-like video clips or inputting keywords within predefined domain.

Improving semantic scene understanding using prior information

NASA Astrophysics Data System (ADS)

Laddha, Ankit; Hebert, Martial

2016-05-01

Perception for ground robot mobility requires automatic generation of descriptions of the robot's surroundings from sensor input (cameras, LADARs, etc.). Effective techniques for scene understanding have been developed, but they are generally purely bottom-up in that they rely entirely on classifying features from the input data based on learned models. In fact, perception systems for ground robots have a lot of information at their disposal from knowledge about the domain and the task. For example, a robot in urban environments might have access to approximate maps that can guide the scene interpretation process. In this paper, we explore practical ways to combine such prior information with state of the art scene understanding approaches.

Differential electrophysiological signatures of semantic and syntactic scene processing.

PubMed

Võ, Melissa L-H; Wolfe, Jeremy M

2013-09-01

In sentence processing, semantic and syntactic violations elicit differential brain responses observable in event-related potentials: An N400 signals semantic violations, whereas a P600 marks inconsistent syntactic structure. Does the brain register similar distinctions in scene perception? To address this question, we presented participants with semantic inconsistencies, in which an object was incongruent with a scene's meaning, and syntactic inconsistencies, in which an object violated structural rules. We found a clear dissociation between semantic and syntactic processing: Semantic inconsistencies produced negative deflections in the N300-N400 time window, whereas mild syntactic inconsistencies elicited a late positivity resembling the P600 found for syntactic inconsistencies in sentence processing. Extreme syntactic violations, such as a hovering beer bottle defying gravity, were associated with earlier perceptual processing difficulties reflected in the N300 response, but failed to produce a P600 effect. We therefore conclude that different neural populations are active during semantic and syntactic processing of scenes, and that syntactically impossible object placements are processed in a categorically different manner than are syntactically resolvable object misplacements.

High-speed data search

NASA Technical Reports Server (NTRS)

Driscoll, James N.

1994-01-01

The high-speed data search system developed for KSC incorporates existing and emerging information retrieval technology to help a user intelligently and rapidly locate information found in large textual databases. This technology includes: natural language input; statistical ranking of retrieved information; an artificial intelligence concept called semantics, where 'surface level' knowledge found in text is used to improve the ranking of retrieved information; and relevance feedback, where user judgements about viewed information are used to automatically modify the search for further information. Semantics and relevance feedback are features of the system which are not available commercially. The system further demonstrates focus on paragraphs of information to decide relevance; and it can be used (without modification) to intelligently search all kinds of document collections, such as collections of legal documents medical documents, news stories, patents, and so forth. The purpose of this paper is to demonstrate the usefulness of statistical ranking, our semantic improvement, and relevance feedback.

Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model.

PubMed

Liu, Dan; Liu, Xuejun; Wu, Yiguang

2018-04-24

This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results.

Monitoring Data-Structure Evolution in Distributed Message-Passing Programs

NASA Technical Reports Server (NTRS)

Sarukkai, Sekhar R.; Beers, Andrew; Woodrow, Thomas S. (Technical Monitor)

1996-01-01

Monitoring the evolution of data structures in parallel and distributed programs, is critical for debugging its semantics and performance. However, the current state-of-art in tracking and presenting data-structure information on parallel and distributed environments is cumbersome and does not scale. In this paper we present a methodology that automatically tracks memory bindings (not the actual contents) of static and dynamic data-structures of message-passing C programs, using PVM. With the help of a number of examples we show that in addition to determining the impact of memory allocation overheads on program performance, graphical views can help in debugging the semantics of program execution. Scalable animations of virtual address bindings of source-level data-structures are used for debugging the semantics of parallel programs across all processors. In conjunction with light-weight core-files, this technique can be used to complement traditional debuggers on single processors. Detailed information (such as data-structure contents), on specific nodes, can be determined using traditional debuggers after the data structure evolution leading to the semantic error is observed graphically.

Word add-in for ontology recognition: semantic enrichment of scientific literature.

PubMed

Fink, J Lynn; Fernicola, Pablo; Chandran, Rahul; Parastatidis, Savas; Wade, Alex; Naim, Oscar; Quinn, Gregory B; Bourne, Philip E

2010-02-24

In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles. The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit. The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.

Component Models for Semantic Web Languages

NASA Astrophysics Data System (ADS)

Henriksson, Jakob; Aßmann, Uwe

Intelligent applications and agents on the Semantic Web typically need to be specified with, or interact with specifications written in, many different kinds of formal languages. Such languages include ontology languages, data and metadata query languages, as well as transformation languages. As learnt from years of experience in development of complex software systems, languages need to support some form of component-based development. Components enable higher software quality, better understanding and reusability of already developed artifacts. Any component approach contains an underlying component model, a description detailing what valid components are and how components can interact. With the multitude of languages developed for the Semantic Web, what are their underlying component models? Do we need to develop one for each language, or is a more general and reusable approach achievable? We present a language-driven component model specification approach. This means that a component model can be (automatically) generated from a given base language (actually, its specification, e.g. its grammar). As a consequence, we can provide components for different languages and simplify the development of software artifacts used on the Semantic Web.

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases

PubMed Central

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

2017-01-01

ABSTRACT Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model. In the array database, all EO images are stored as a space-time data cube together with their Level 2 products generated by the EO-IU subsystem. The GUI allows users to (a) develop a conceptual world model based on a graphically supported query pipeline as a combination of spatial and temporal operators and/or standard algorithms and (b) create, save and share within the client-server architecture complex semantic queries/decision rules, suitable for SCBIR and/or spatiotemporal EO image analytics, consistent with the conceptual world model. PMID:29098143

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

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

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

iPad: Semantic annotation and markup of radiological images.

PubMed

Rubin, Daniel L; Rodriguez, Cesar; Shah, Priyanka; Beaulieu, Chris

2008-11-06

Radiological images contain a wealth of information,such as anatomy and pathology, which is often not explicit and computationally accessible. Information schemes are being developed to describe the semantic content of images, but such schemes can be unwieldy to operationalize because there are few tools to enable users to capture structured information easily as part of the routine research workflow. We have created iPad, an open source tool enabling researchers and clinicians to create semantic annotations on radiological images. iPad hides the complexity of the underlying image annotation information model from users, permitting them to describe images and image regions using a graphical interface that maps their descriptions to structured ontologies semi-automatically. Image annotations are saved in a variety of formats,enabling interoperability among medical records systems, image archives in hospitals, and the Semantic Web. Tools such as iPad can help reduce the burden of collecting structured information from images, and it could ultimately enable researchers and physicians to exploit images on a very large scale and glean the biological and physiological significance of image content.

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

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

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

An individual differences approach to semantic cognition: Divergent effects of age on representation, retrieval and selection.

PubMed

Hoffman, Paul

2018-05-25

Semantic cognition refers to the appropriate use of acquired knowledge about the world. This requires representation of knowledge as well as control processes which ensure that currently-relevant aspects of knowledge are retrieved and selected. Although these abilities can be impaired selectively following brain damage, the relationship between them in healthy individuals is unclear. It is also commonly assumed that semantic cognition is preserved in later life, because older people have greater reserves of knowledge. However, this claim overlooks the possibility of decline in semantic control processes. Here, semantic cognition was assessed in 100 young and older adults. Despite having a broader knowledge base, older people showed specific impairments in semantic control, performing more poorly than young people when selecting among competing semantic representations. Conversely, they showed preserved controlled retrieval of less salient information from the semantic store. Breadth of semantic knowledge was positively correlated with controlled retrieval but was unrelated to semantic selection ability, which was instead correlated with non-semantic executive function. These findings indicate that three distinct elements contribute to semantic cognition: semantic representations that accumulate throughout the lifespan, processes for controlled retrieval of less salient semantic information, which appear age-invariant, and mechanisms for selecting task-relevant aspects of semantic knowledge, which decline with age and may relate more closely to domain-general executive control.

Electrophysiological effects of semantic context in picture and word naming.

PubMed

Janssen, Niels; Carreiras, Manuel; Barber, Horacio A

2011-08-01

Recent language production studies have started to use electrophysiological measures to investigate the time course of word selection processes. An important contribution with respect to this issue comes from studies that have relied on an effect of semantic context in the semantic blocking task. Here we used this task to further establish the empirical pattern associated with the effect of semantic context, and whether the effect arises during output processing. Electrophysiological and reaction time measures were co-registered while participants overtly named picture and word stimuli in the semantic blocking task. The results revealed inhibitory reaction time effects of semantic context for both words and pictures, and a corresponding electrophysiological effect that could not be interpreted in terms of output processes. These data suggest that the electrophysiological effect of semantic context in the semantic blocking task does not reflect output processes, and therefore undermine an interpretation of this effect in terms of word selection. Copyright © 2011 Elsevier Inc. All rights reserved.

Different Loci of Semantic Interference in Picture Naming vs. Word-Picture Matching Tasks.

PubMed

Harvey, Denise Y; Schnur, Tatiana T

2016-01-01

Naming pictures and matching words to pictures belonging to the same semantic category impairs performance relative to when stimuli come from different semantic categories (i.e., semantic interference). Despite similar semantic interference phenomena in both picture naming and word-picture matching tasks, the locus of interference has been attributed to different levels of the language system - lexical in naming and semantic in word-picture matching. Although both tasks involve access to shared semantic representations, the extent to which interference originates and/or has its locus at a shared level remains unclear, as these effects are often investigated in isolation. We manipulated semantic context in cyclical picture naming and word-picture matching tasks, and tested whether factors tapping semantic-level (generalization of interference to novel category items) and lexical-level processes (interactions with lexical frequency) affected the magnitude of interference, while also assessing whether interference occurs at a shared processing level(s) (transfer of interference across tasks). We found that semantic interference in naming was sensitive to both semantic- and lexical-level processes (i.e., larger interference for novel vs. old and low- vs. high-frequency stimuli), consistent with a semantically mediated lexical locus. Interference in word-picture matching exhibited stable interference for old and novel stimuli and did not interact with lexical frequency. Further, interference transferred from word-picture matching to naming. Together, these experiments provide evidence to suggest that semantic interference in both tasks originates at a shared processing stage (presumably at the semantic level), but that it exerts its effect at different loci when naming pictures vs. matching words to pictures.

Different Loci of Semantic Interference in Picture Naming vs. Word-Picture Matching Tasks

PubMed Central

Harvey, Denise Y.; Schnur, Tatiana T.

2016-01-01

Naming pictures and matching words to pictures belonging to the same semantic category impairs performance relative to when stimuli come from different semantic categories (i.e., semantic interference). Despite similar semantic interference phenomena in both picture naming and word-picture matching tasks, the locus of interference has been attributed to different levels of the language system – lexical in naming and semantic in word-picture matching. Although both tasks involve access to shared semantic representations, the extent to which interference originates and/or has its locus at a shared level remains unclear, as these effects are often investigated in isolation. We manipulated semantic context in cyclical picture naming and word-picture matching tasks, and tested whether factors tapping semantic-level (generalization of interference to novel category items) and lexical-level processes (interactions with lexical frequency) affected the magnitude of interference, while also assessing whether interference occurs at a shared processing level(s) (transfer of interference across tasks). We found that semantic interference in naming was sensitive to both semantic- and lexical-level processes (i.e., larger interference for novel vs. old and low- vs. high-frequency stimuli), consistent with a semantically mediated lexical locus. Interference in word-picture matching exhibited stable interference for old and novel stimuli and did not interact with lexical frequency. Further, interference transferred from word-picture matching to naming. Together, these experiments provide evidence to suggest that semantic interference in both tasks originates at a shared processing stage (presumably at the semantic level), but that it exerts its effect at different loci when naming pictures vs. matching words to pictures. PMID:27242621

Automatic extraction of road features in urban environments using dense ALS data

NASA Astrophysics Data System (ADS)

Soilán, Mario; Truong-Hong, Linh; Riveiro, Belén; Laefer, Debra

2018-02-01

This paper describes a methodology that automatically extracts semantic information from urban ALS data for urban parameterization and road network definition. First, building façades are segmented from the ground surface by combining knowledge-based information with both voxel and raster data. Next, heuristic rules and unsupervised learning are applied to the ground surface data to distinguish sidewalk and pavement points as a means for curb detection. Then radiometric information was employed for road marking extraction. Using high-density ALS data from Dublin, Ireland, this fully automatic workflow was able to generate a F-score close to 95% for pavement and sidewalk identification with a resolution of 20 cm and better than 80% for road marking detection.

An Investigation into Semantic and Phonological Processing in Individuals with Williams Syndrome

ERIC Educational Resources Information Center

Lee, Cheryl S.; Binder, Katherine S.

2014-01-01

Purpose: The current study examined semantic and phonological processing in individuals with Williams syndrome (WS). Previous research in language processing in individuals with WS suggests a complex linguistic system characterized by "deviant" semantic organization and differential phonological processing. Method: Two experiments…

Semantic Processing of Mathematical Gestures

ERIC Educational Resources Information Center

Lim, Vanessa K.; Wilson, Anna J.; Hamm, Jeff P.; Phillips, Nicola; Iwabuchi, Sarina J.; Corballis, Michael C.; Arzarello, Ferdinando; Thomas, Michael O. J.

2009-01-01

Objective: To examine whether or not university mathematics students semantically process gestures depicting mathematical functions (mathematical gestures) similarly to the way they process action gestures and sentences. Semantic processing was indexed by the N400 effect. Results: The N400 effect elicited by words primed with mathematical gestures…

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.

Neural correlates of combinatorial semantic processing of literal and figurative noun noun compound words.

PubMed

Forgács, Bálint; Bohrn, Isabel; Baudewig, Jürgen; Hofmann, Markus J; Pléh, Csaba; Jacobs, Arthur M

2012-11-15

The right hemisphere's role in language comprehension is supported by results from several neuropsychology and neuroimaging studies. Special interest surrounds right temporoparietal structures, which are thought to be involved in processing novel metaphorical expressions, primarily due to the coarse semantic coding of concepts. In this event related fMRI experiment we aimed at assessing the extent of semantic distance processing in the comprehension of figurative meaning to clarify the role of the right hemisphere. Four categories of German noun noun compound words were presented in a semantic decision task: a) conventional metaphors; b) novel metaphors; c) conventional literal, and; d) novel literal expressions, controlled for length, frequency, imageability, arousal, and emotional valence. Conventional literal and metaphorical compounds increased BOLD signal change in right temporoparietal regions, suggesting combinatorial semantic processing, in line with the coarse semantic coding theory, but at odds with the graded salience hypothesis. Both novel literal and novel metaphorical expressions increased activity in left inferior frontal areas, presumably as a result of phonetic, morphosyntactic, and semantic unification processes, challenging predictions regarding right hemispheric involvement in processing unusual meanings. Meanwhile, both conventional and novel metaphorical expressions induced BOLD signal change in left hemispherical regions, suggesting that even novel metaphor processing involves more than linking semantically distant concepts. Copyright © 2012 Elsevier Inc. All rights reserved.

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

ERIC Educational Resources Information Center

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

2012-01-01

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

The role of attention in subliminal semantic processing: A mouse tracking study.

PubMed

Xiao, Kunchen; Yamauchi, Takashi

2017-01-01

Recent evidence suggests that top-down attention facilitates unconscious semantic processing. To clarify the role of attention in unconscious semantic processing, we traced trajectories of the computer mouse in a semantic priming task and scrutinized the extent to which top-down attention enhances unconscious semantic processing in four different stimulus-onset asynchrony (SOA: 50, 200, 500, or 1000ms) conditions. Participants judged whether a target digit (e.g., "6") was larger or smaller than five, preceded by a masked priming digit (e.g., "9"). The pre-prime duration changed randomly from trial to trial to disrupt participants' top-down attention in an uncued condition (in a cued condition, a green square cue was presented to facilitate participants' top-down attention). The results show that top-down attention modifies the time course of subliminal semantic processing, and the temporal attention window lasts more than 1000ms; attention facilitated by the cue may amplify semantic priming to some extent, yet the amplification effect of attention is relatively minor.

Semantic Processing Persists despite Anomalous Syntactic Category: ERP Evidence from Chinese Passive Sentences.

PubMed

Yang, Yang; Wu, Fuyun; Zhou, Xiaolin

2015-01-01

The syntax-first model and the parallel/interactive models make different predictions regarding whether syntactic category processing has a temporal and functional primacy over semantic processing. To further resolve this issue, an event-related potential experiment was conducted on 24 Chinese speakers reading Chinese passive sentences with the passive marker BEI (NP1 + BEI + NP2 + Verb). This construction was selected because it is the most-commonly used Chinese passive and very much resembles German passives, upon which the syntax-first hypothesis was primarily based. We manipulated semantic consistency (consistent vs. inconsistent) and syntactic category (noun vs. verb) of the critical verb, yielding four conditions: CORRECT (correct sentences), SEMANTIC (semantic anomaly), SYNTACTIC (syntactic category anomaly), and COMBINED (combined anomalies). Results showed both N400 and P600 effects for sentences with semantic anomaly, with syntactic category anomaly, or with combined anomalies. Converging with recent findings of Chinese ERP studies on various constructions, our study provides further evidence that syntactic category processing does not precede semantic processing in reading Chinese.

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

PubMed

Yang, Jie

2014-09-01

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

Mimicking aphasic semantic errors in normal speech production: evidence from a novel experimental paradigm.

PubMed

Hodgson, Catherine; Lambon Ralph, Matthew A

2008-01-01

Semantic errors are commonly found in semantic dementia (SD) and some forms of stroke aphasia and provide insights into semantic processing and speech production. Low error rates are found in standard picture naming tasks in normal controls. In order to increase error rates and thus provide an experimental model of aphasic performance, this study utilised a novel method- tempo picture naming. Experiment 1 showed that, compared to standard deadline naming tasks, participants made more errors on the tempo picture naming tasks. Further, RTs were longer and more errors were produced to living items than non-living items a pattern seen in both semantic dementia and semantically-impaired stroke aphasic patients. Experiment 2 showed that providing the initial phoneme as a cue enhanced performance whereas providing an incorrect phonemic cue further reduced performance. These results support the contention that the tempo picture naming paradigm reduces the time allowed for controlled semantic processing causing increased error rates. This experimental procedure would, therefore, appear to mimic the performance of aphasic patients with multi-modal semantic impairment that results from poor semantic control rather than the degradation of semantic representations observed in semantic dementia [Jefferies, E. A., & Lambon Ralph, M. A. (2006). Semantic impairment in stoke aphasia vs. semantic dementia: A case-series comparison. Brain, 129, 2132-2147]. Further implications for theories of semantic cognition and models of speech processing are discussed.

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

PubMed

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

1995-09-01

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

Automatic Verification of Serializers.

DTIC Science & Technology

1980-03-01

31 2.5 Using semaphores to implement sei ;alizers ......................... 32 2.6 A comparison of...of concurrency control, while Hewitt has concentrated on more primitive control of concurrency in a context where programs communicate by passing...translation oflserializers into clusters and semaphores is given as a possible implementation strategy. Chapter 3 presents a simple semantic model that supl

Semantic Similarity Measures for the Generation of Science Tests in Basque

ERIC Educational Resources Information Center

Aldabe, Itziar; Maritxalar, Montse

2014-01-01

The work we present in this paper aims to help teachers create multiple-choice science tests. We focus on a scientific vocabulary-learning scenario taking place in a Basque-language educational environment. In this particular scenario, we explore the option of automatically generating Multiple-Choice Questions (MCQ) by means of Natural Language…

Automatic Line Network Extraction from Aerial Imagery of Urban Areas through Knowledge-Based Image Analysis.

DTIC Science & Technology

1988-01-19

approach for the analysis of aerial images. In this approach image analysis is performed ast three levels of abstraction, namely iconic or low-level... image analysis , symbolic or medium-level image analysis , and semantic or high-level image analysis . Domain dependent knowledge about prototypical urban

Using Novel Word Context Measures to Predict Human Ratings of Lexical Proficiency

ERIC Educational Resources Information Center

Berger, Cynthia M.; Crossley, Scott A.; Kyle, Kristopher

2017-01-01

This study introduces a model of lexical proficiency based on novel computational indices related to word context. The indices come from an updated version of the Tool for the Automatic Analysis of Lexical Sophistication (TAALES) and include associative, lexical, and semantic measures of word context. Human ratings of holistic lexical proficiency…

Representing System Behaviors and Expert Behaviors for Intelligent Tutoring. Technical Report No. 108.

ERIC Educational Resources Information Center

Towne, Douglas M.; And Others

Simulation-based software tools that can infer system behaviors from a deep model of the system have the potential for automatically building the semantic representations required to support intelligent tutoring in fault diagnosis. The Intelligent Maintenance Training System (IMTS) is such a resource, designed for use in training troubleshooting…

Semantic photo synthesis

NASA Astrophysics Data System (ADS)

Johnson, Matthew; Brostow, G. J.; Shotton, J.; Kwatra, V.; Cipolla, R.

2007-02-01

Composite images are synthesized from existing photographs by artists who make concept art, e.g. storyboards for movies or architectural planning. Current techniques allow an artist to fabricate such an image by digitally splicing parts of stock photographs. While these images serve mainly to "quickly" convey how a scene should look, their production is laborious. We propose a technique that allows a person to design a new photograph with substantially less effort. This paper presents a method that generates a composite image when a user types in nouns, such as "boat" and "sand." The artist can optionally design an intended image by specifying other constraints. Our algorithm formulates the constraints as queries to search an automatically annotated image database. The desired photograph, not a collage, is then synthesized using graph-cut optimization, optionally allowing for further user interaction to edit or choose among alternative generated photos. Our results demonstrate our contributions of (1) a method of creating specific images with minimal human effort, and (2) a combined algorithm for automatically building an image library with semantic annotations from any photo collection.

The Role of Simple Semantics in the Process of Artificial Grammar Learning.

PubMed

Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

2017-10-01

This study investigated the effect of semantic information on artificial grammar learning (AGL). Recursive grammars of different complexity levels (regular language, mirror language, copy language) were investigated in a series of AGL experiments. In the with-semantics condition, participants acquired semantic information prior to the AGL experiment; in the without-semantics control condition, participants did not receive semantic information. It was hypothesized that semantics would generally facilitate grammar acquisition and that the learning benefit in the with-semantics conditions would increase with increasing grammar complexity. Experiment 1 showed learning effects for all grammars but no performance difference between conditions. Experiment 2 replicated the absence of a semantic benefit for all grammars even though semantic information was more prominent during grammar acquisition as compared to Experiment 1. Thus, we did not find evidence for the idea that semantics facilitates grammar acquisition, which seems to support the view of an independent syntactic processing component.

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.

Effects of donepezil on verbal memory after semantic processing in healthy older adults.

PubMed

FitzGerald, David B; Crucian, Gregory P; Mielke, Jeannine B; Shenal, Brian V; Burks, David; Womack, Kyle B; Ghacibeh, Georges; Drago, Valeria; Foster, Paul S; Valenstein, Edward; Heilman, Kenneth M

2008-06-01

To learn if acetylcholinesterase inhibitors alter verbal recall by improving semantic encoding in a double-blind randomized placebo-controlled trial. Cholinergic supplementation has been shown to improve delayed recall in adults with Alzheimer disease. With functional magnetic resonance imaging, elderly adults, when compared with younger participants, have reduced cortical activation with semantic processing. There have been no studies investigating the effects of cholinergic supplementation on semantic encoding in healthy elderly adults. Twenty elderly participants (mean age 71.5, SD+/-5.2) were recruited. All underwent memory testing before and after receiving donepezil (5 mg, n=11 or 10 mg, n=1) or placebo (n=8) for 6 weeks. Memory was tested using a Levels of Processing task, where a series of words are presented serially. Subjects were either asked to count consonants in a word (superficially process) or decide if the word was "pleasant" or "unpleasant" (semantically process). After 6 weeks of donepezil or placebo treatment, immediate and delayed recall of superficially and semantically processed words was compared with baseline performance. Immediate and delayed recall of superficially processed words did not show significant changes in either treatment group. With semantic processing, both immediate and delayed recall performance improved in the donepezil group. Our results suggest that when using semantic encoding, older normal subjects may be aided by anticholinesterase treatment. However, this treatment does not improve recall of superficially encoded words.

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 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.

Less Is More: Semantic Information Survives Interocular Suppression When Attention Is Diverted.

PubMed

Eo, Kangyong; Cha, Oakyoon; Chong, Sang Chul; Kang, Min-Suk

2016-05-18

The extent of unconscious semantic processing has been debated. It is well established that semantic information is registered in the absence of awareness induced by inattention. However, it has been debated whether semantic information of invisible stimuli is processed during interocular suppression, a procedure that renders one eye's view invisible by presenting a dissimilar stimulus to the other eye. Inspired by recent evidence demonstrating that reduced attention attenuates interocular suppression, we tested a counterintuitive hypothesis that attention withdrawn from the suppressed target location facilitates semantic processing in the absence of awareness induced by interocular suppression. We obtained an electrophysiological marker of semantic processing (N400 component) while human participants' spatial attention was being manipulated with a cueing paradigm during interocular suppression. We found that N400 modulation was absent when participants' attention was directed to the target location, but present when diverted elsewhere. In addition, the correlation analysis across participants indicated that the N400 amplitude was reduced with more attention being directed to the target location. Together, these results indicate that inattention attenuates interocular suppression and thereby makes semantic processing available unconsciously, reconciling conflicting evidence in the literature. We discuss a tight link among interocular suppression, attention, and conscious awareness. Interocular suppression offers a powerful means of studying the extent of unconscious processing by rendering a salient stimulus presented to one eye invisible. Here, we provide evidence that attention is a determining factor for unconscious semantic processing. An electrophysiological marker for semantic processing (N400 component) was present when attention was diverted away from the suppressed stimulus but absent when attention was directed to that stimulus, indicating that inattention facilitates unconscious semantic processing during the interocular suppression. Although contrary to the common sense assumption that attention facilitates information processing, this result is in accordance with recent studies showing that attention modulates interocular suppression but is not necessary for semantic processing. Our finding reconciles the conflicting evidence and advances theories of consciousness. Copyright © 2016 the authors 0270-6474/16/365489-09$15.00/0.

Constructing Concept Schemes From Astronomical Telegrams Via Natural Language Clustering

NASA Astrophysics Data System (ADS)

Graham, Matthew; Zhang, M.; Djorgovski, S. G.; Donalek, C.; Drake, A. J.; Mahabal, A.

2012-01-01

The rapidly emerging field of time domain astronomy is one of the most exciting and vibrant new research frontiers, ranging in scientific scope from studies of the Solar System to extreme relativistic astrophysics and cosmology. It is being enabled by a new generation of large synoptic digital sky surveys - LSST, PanStarrs, CRTS - that cover large areas of sky repeatedly, looking for transient objects and phenomena. One of the biggest challenges facing these is the automated classification of transient events, a process that needs machine-processible astronomical knowledge. Semantic technologies enable the formal representation of concepts and relations within a particular domain. ATELs (http://www.astronomerstelegram.org) are a commonly-used means for reporting and commenting upon new astronomical observations of transient sources (supernovae, stellar outbursts, blazar flares, etc). However, they are loose and unstructured and employ scientific natural language for description: this makes automated processing of them - a necessity within the next decade with petascale data rates - a challenge. Nevertheless they represent a potentially rich corpus of information that could lead to new and valuable insights into transient phenomena. This project lies in the cutting-edge field of astrosemantics, a branch of astroinformatics, which applies semantic technologies to astronomy. The ATELs have been used to develop an appropriate concept scheme - a representation of the information they contain - for transient astronomy using hierarchical clustering of processed natural language. This allows us to automatically organize ATELs based on the vocabulary used. We conclude that we can use simple algorithms to process and extract meaning from astronomical textual data.

The subcortical role of language processing. High level linguistic features such as ambiguity-resolution and the human brain; an fMRI study.

PubMed

Ketteler, Daniel; Kastrau, Frank; Vohn, Rene; Huber, Walter

2008-02-15

In the present study, we were interested in the neurofunctional representations of ambiguity processing by using functional magnetic resonance imaging (fMRI). Twelve right-handed, healthy adults aged between 21 and 29 years (6 male, 6 female) underwent an ambiguity resolution task with 4 different conditions (dominant vs. non-dominant; dominant vs. distractor; non-dominant vs. distractor; distractor vs. distractor). After subtraction of the corresponding control task (distractor vs. distractor) we found significant activation especially in the thalamus and some parts of the basal ganglia (caudate nucleus, putamen). Our findings implicate a participation of the thalamus and other basal ganglia circuits in high level linguistic functions and match with theoretical considerations on this highly controversial topic. Subcortical neural circuits probably become activated when the language processing system cannot rely entirely on automatic mechanisms but has to recruit controlled processes as well. Furthermore, we found broad activation in the inferior parietal lobule, the prefrontal gyrus, pre-SMA and SMA and the cingulate cortex. This might reflect a strategic semantic search mechanism which probably can be illustrated with connectionist models of language processing. According to this, we hypothesize a neuroregulatory role for the thalamus and basal ganglia in regulating and monitoring the release of preformulated language segments for motor programming and semantic verification. According to our findings there is strong evidence, that especially the thalamus, the caudate nucleus, the cingulate cortex, the inferior parietal lobule and the prefrontal cortex are responsible for an accurate ambiguity resolution in the human brain.

Working memory and semantic involvement in sentence processing: a case of pure progressive amnesia.

PubMed

Fossard, Marion; Rigalleau, François; Puel, Michèle; Nespoulous, Jean-Luc; Viallard, Gérard; Démonet, Jean-François; Cardebat, Dominique

2006-01-01

ED, a 83-year-old woman, meets the criteria of pure progressive amnesia, with gradual impairment of episodic and autobiographical memory, sparing of semantic processing and strong working memory (WM) deficit. The dissociation between disturbed WM and spared semantic processing permitted testing the role of WM in processing anaphors like pronouns or repeated names. Results showed a globally normal anaphoric behavior in two experiments requiring anaphoric processing in sentence production and comprehension. We suggest that preserved semantic processing in ED would have compensated for working memory deficit in anaphoric processing.

Visual and semantic processing of living things and artifacts: an FMRI study.

PubMed

Zannino, Gian Daniele; Buccione, Ivana; Perri, Roberta; Macaluso, Emiliano; Lo Gerfo, Emanuele; Caltagirone, Carlo; Carlesimo, Giovanni A

2010-03-01

We carried out an fMRI study with a twofold purpose: to investigate the relationship between networks dedicated to semantic and visual processing and to address the issue of whether semantic memory is subserved by a unique network or by different subsystems, according to semantic category or feature type. To achieve our goals, we administered a word-picture matching task, with within-category foils, to 15 healthy subjects during scanning. Semantic distance between the target and the foil and semantic domain of the target-foil pairs were varied orthogonally. Our results suggest that an amodal, undifferentiated network for the semantic processing of living things and artifacts is located in the anterolateral aspects of the temporal lobes; in fact, activity in this substrate was driven by semantic distance, not by semantic category. By contrast, activity in ventral occipito-temporal cortex was driven by category, not by semantic distance. We interpret the latter finding as the effect exerted by systematic differences between living things and artifacts at the level of their structural representations and possibly of their lower-level visual features. Finally, we attempt to reconcile contrasting data in the neuropsychological and functional imaging literature on semantic substrate and category specificity.

Automatic Summarization of MEDLINE Citations for Evidence–Based Medical Treatment: A Topic-Oriented Evaluation

PubMed Central

Fiszman, Marcelo; Demner-Fushman, Dina; Kilicoglu, Halil; Rindflesch, Thomas C.

2009-01-01

As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for fifty-three diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p < 0.01) and the increase in the overall score of clinical usefulness was 0.39 (p < 0.05). PMID:19022398

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.

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.

UBioLab: a web-LABoratory for Ubiquitous in-silico experiments.

PubMed

Bartocci, E; Di Berardini, M R; Merelli, E; Vito, L

2012-03-01

The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists -for what concerns their management and visualization- and for bioinformaticians -for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle -and possibly to handle in a transparent and uniform way- aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features -as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques- give evidence of an effort in such a direction. The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) "type" of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.

Characterizing Cognitive Performance in a Large Longitudinal Study of Aging with Computerized Semantic Indices of Verbal Fluency

PubMed Central

Pakhomov, Serguei VS; Eberly, Lynn; Knopman, David

2016-01-01

A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia. PMID:27245645

Out of the Corner of My Eye: Foveal Semantic Load Modulates Parafoveal Processing in Reading.

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

Payne, Brennan R.; Stites, Mallory C.; Federmeier, Kara D.

In two experiments, we examined the impact of foveal semantic expectancy and congruity on parafoveal word processing during reading. Experiment 1 utilized an eye-tracking gaze contingent display change paradigm, and Experiment 2 measured event-related brain potentials (ERP) in a modified RSVP paradigm to track the time-course of foveal semantic influences on convert attentional allocation to parafoveal word processing. Furthermore, eye-tracking and ERP data converged to reveal graded effects of semantic foveal load on parafoveal processing.

Out of the Corner of My Eye: Foveal Semantic Load Modulates Parafoveal Processing in Reading.

DOE PAGES

Payne, Brennan R.; Stites, Mallory C.; Federmeier, Kara D.

2016-07-18

In two experiments, we examined the impact of foveal semantic expectancy and congruity on parafoveal word processing during reading. Experiment 1 utilized an eye-tracking gaze contingent display change paradigm, and Experiment 2 measured event-related brain potentials (ERP) in a modified RSVP paradigm to track the time-course of foveal semantic influences on convert attentional allocation to parafoveal word processing. Furthermore, eye-tracking and ERP data converged to reveal graded effects of semantic foveal load on parafoveal processing.

Differential interference effects of negative emotional states on subsequent semantic and perceptual processing

PubMed Central

Gorlick, Marissa A.; Mather, Mara

2012-01-01

Past studies have revealed that encountering negative events interferes with cognitive processing of subsequent stimuli. The present study investigated whether negative events affect semantic and perceptual processing differently. Presentation of negative pictures produced slower reaction times than neutral or positive pictures in tasks that require semantic processing, such as natural/man-made judgments about drawings of objects, commonness judgments about objects, and categorical judgments about pairs of words. In contrast, negative picture presentation did not slow down judgments in subsequent perceptual processing (e.g., color judgments about words, and size judgments about objects). The subjective arousal level of negative pictures did not modulate the interference effects on semantic/perceptual processing. These findings indicate that encountering negative emotional events interferes with semantic processing of subsequent stimuli more strongly than perceptual processing, and that not all types of subsequent cognitive processing are impaired by negative events. PMID:22142207

Differential interference effects of negative emotional states on subsequent semantic and perceptual processing.

PubMed

Sakaki, Michiko; Gorlick, Marissa A; Mather, Mara

2011-12-01

Past studies have revealed that encountering negative events interferes with cognitive processing of subsequent stimuli. The present study investigates whether negative events affect semantic and perceptual processing differently. Presentation of negative pictures produced slower reaction times than neutral or positive pictures in tasks that require semantic processing, such as natural or man-made judgments about drawings of objects, commonness judgments about objects, and categorical judgments about pairs of words. In contrast, negative picture presentation did not slow down judgments in subsequent perceptual processing (e.g., color judgments about words, size judgments about objects). The subjective arousal level of negative pictures did not modulate the interference effects on semantic or perceptual processing. These findings indicate that encountering negative emotional events interferes with semantic processing of subsequent stimuli more strongly than perceptual processing, and that not all types of subsequent cognitive processing are impaired by negative events. (c) 2011 APA, all rights reserved.

Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation.

PubMed

Huang, Chung-Chi; Lu, Zhiyong

2016-01-01

Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as 'CHEMICAL-1 compared to CHEMICAL-2' With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical-disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked order covering diverse bio-entity relations. To assess the potential utility of our automated top-ranked patterns of a given relation in semantic search, we performed a pilot study on frequently sought semantic relations in PubMed and observed improved literature retrieval effectiveness based on post-hoc human relevance evaluation. Further investigation in larger tests and in real-world scenarios is warranted. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition.

PubMed

Tsatsaronis, George; Balikas, Georgios; Malakasiotis, Prodromos; Partalas, Ioannis; Zschunke, Matthias; Alvers, Michael R; Weissenborn, Dirk; Krithara, Anastasia; Petridis, Sergios; Polychronopoulos, Dimitris; Almirantis, Yannis; Pavlopoulos, John; Baskiotis, Nicolas; Gallinari, Patrick; Artiéres, Thierry; Ngomo, Axel-Cyrille Ngonga; Heino, Norman; Gaussier, Eric; Barrio-Alvers, Liliana; Schroeder, Michael; Androutsopoulos, Ion; Paliouras, Georgios

2015-04-30

This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central; produce "exact" and paragraph-sized "ideal" answers (summaries). The results of the systems that participated in the 2013 BIOASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM's MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the "ideal" answers; hence, they produced high quality summaries as answers. Overall, BIOASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.

Spatial Knowledge Infrastructures - Creating Value for Policy Makers and Benefits the Community

NASA Astrophysics Data System (ADS)

Arnold, L. M.

2016-12-01

The spatial data infrastructure is arguably one of the most significant advancements in the spatial sector. It's been a game changer for governments, providing for the coordination and sharing of spatial data across organisations and the provision of accessible information to the broader community of users. Today however, end-users such as policy-makers require far more from these spatial data infrastructures. They want more than just data; they want the knowledge that can be extracted from data and they don't want to have to download, manipulate and process data in order to get the knowledge they seek. It's time for the spatial sector to reduce its focus on data in spatial data infrastructures and take a more proactive step in emphasising and delivering the knowledge value. Nowadays, decision-makers want to be able to query at will the data to meet their immediate need for knowledge. This is a new value proposal for the decision-making consumer and will require a shift in thinking. This paper presents a model for a Spatial Knowledge Infrastructure and underpinning methods that will realise a new real-time approach to delivering knowledge. The methods embrace the new capabilities afforded through the sematic web, domain and process ontologies and natural query language processing. Semantic Web technologies today have the potential to transform the spatial industry into more than just a distribution channel for data. The Semantic Web RDF (Resource Description Framework) enables meaning to be drawn from data automatically. While pushing data out to end-users will remain a central role for data producers, the power of the semantic web is that end-users have the ability to marshal a broad range of spatial resources via a query to extract knowledge from available data. This can be done without actually having to configure systems specifically for the end-user. All data producers need do is make data accessible in RDF and the spatial analytics does the rest.

The Effect of Semantic Transparency on the Processing of Morphologically Derived Words: Evidence from Decision Latencies and Event-Related Potentials

ERIC Educational Resources Information Center

Jared, Debra; Jouravlev, Olessia; Joanisse, Marc F.

2017-01-01

Decomposition theories of morphological processing in visual word recognition posit an early morpho-orthographic parser that is blind to semantic information, whereas parallel distributed processing (PDP) theories assume that the transparency of orthographic-semantic relationships influences processing from the beginning. To test these…

Syntactic processing in the absence of awareness and semantics.

PubMed

Hung, Shao-Min; Hsieh, Po-Jang

2015-10-01

The classical view that multistep rule-based operations require consciousness has recently been challenged by findings that both multiword semantic processing and multistep arithmetic equations can be processed unconsciously. It remains unclear, however, whether pure rule-based cognitive processes can occur unconsciously in the absence of semantics. Here, after presenting 2 words consciously, we suppressed the third with continuous flash suppression. First, we showed that the third word in the subject-verb-verb format (syntactically incongruent) broke suppression significantly faster than the third word in the subject-verb-object format (syntactically congruent). Crucially, the same effect was observed even with sentences composed of pseudowords (pseudo subject-verb-adjective vs. pseudo subject-verb-object) without any semantic information. This is the first study to show that syntactic congruency can be processed unconsciously in the complete absence of semantics. Our findings illustrate how abstract rule-based processing (e.g., syntactic categories) can occur in the absence of visual awareness, even when deprived of semantics. (c) 2015 APA, all rights reserved).

Portable automatic text classification for adverse drug reaction detection via multi-corpus training.

PubMed

Sarker, Abeed; Gonzalez, Graciela

2015-02-01

Automatic detection of adverse drug reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media-where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing (NLP) approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training

PubMed Central

Gonzalez, Graciela

2014-01-01

Objective Automatic detection of Adverse Drug Reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media — where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. Methods One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Results Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Conclusions Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. PMID:25451103

The role of attention in subliminal semantic processing: A mouse tracking study

PubMed Central

Xiao, Kunchen; Yamauchi, Takashi

2017-01-01

Recent evidence suggests that top-down attention facilitates unconscious semantic processing. To clarify the role of attention in unconscious semantic processing, we traced trajectories of the computer mouse in a semantic priming task and scrutinized the extent to which top-down attention enhances unconscious semantic processing in four different stimulus-onset asynchrony (SOA: 50, 200, 500, or 1000ms) conditions. Participants judged whether a target digit (e.g., “6”) was larger or smaller than five, preceded by a masked priming digit (e.g., “9”). The pre-prime duration changed randomly from trial to trial to disrupt participants’ top-down attention in an uncued condition (in a cued condition, a green square cue was presented to facilitate participants’ top-down attention). The results show that top-down attention modifies the time course of subliminal semantic processing, and the temporal attention window lasts more than 1000ms; attention facilitated by the cue may amplify semantic priming to some extent, yet the amplification effect of attention is relatively minor. PMID:28609460

[Effects of punctuation on the processing of syntactically ambiguous Japanese sentences with a semantic bias].

PubMed

Niikuni, Keiyu; Muramoto, Toshiaki

2014-06-01

This study explored the effects of a comma on the processing of structurally ambiguous Japanese sentences with a semantic bias. A previous study has shown that a comma which is incompatible with an ambiguous sentence's semantic bias affects the processing of the sentence, but the effects of a comma that is compatible with the bias are unclear. In the present study, we examined the role of a comma compatible with the sentence's semantic bias using the self-paced reading method, which enabled us to determine the reading times for the region of the sentence where readers would be expected to solve the ambiguity using semantic information (the "target region"). The results show that a comma significantly increases the reading time of the punctuated word but decreases the reading time in the target region. We concluded that even if the semantic information provided might be sufficient for disambiguation, the insertion of a comma would affect the processing cost of the ambiguity, indicating that readers use both the comma and semantic information in parallel for sentence processing.

How "mere" is the mere ownership effect in memory? Evidence for semantic organization processes.

PubMed

Englert, Julia; Wentura, Dirk

2016-11-01

Memory is better for items arbitrarily assigned to the self than for items assigned to another person (mere ownership effect, MOE). In a series of six experiments, we investigated the role of semantic processes for the MOE. Following successful replication, we investigated whether the MOE was contingent upon semantic processing: For meaningless stimuli, there was no MOE. Testing for a potential role of semantic elaboration using meaningful stimuli in an encoding task without verbal labels, we found evidence of spontaneous semantic processing irrespective of self- or other-assignment. When semantic organization was manipulated, the MOE vanished if a semantic classification task was added to the self/other assignment but persisted for a perceptual classification task. Furthermore, we found greater clustering of self-assigned than of other-assigned items in free recall. Taken together, these results suggest that the MOE could be based on the organizational principle of a "me" versus "not-me" categorization. Copyright © 2016 Elsevier Inc. All rights reserved.

Neural changes associated with semantic processing in healthy aging despite intact behavioral performance.

PubMed

Lacombe, Jacinthe; Jolicoeur, Pierre; Grimault, Stephan; Pineault, Jessica; Joubert, Sven

2015-10-01

Semantic memory recruits an extensive neural network including the left inferior prefrontal cortex (IPC) and the left temporoparietal region, which are involved in semantic control processes, as well as the anterior temporal lobe region (ATL) which is considered to be involved in processing semantic information at a central level. However, little is known about the underlying neuronal integrity of the semantic network in normal aging. Young and older healthy adults carried out a semantic judgment task while their cortical activity was recorded using magnetoencephalography (MEG). Despite equivalent behavioral performance, young adults activated the left IPC to a greater extent than older adults, while the latter group recruited the temporoparietal region bilaterally and the left ATL to a greater extent than younger adults. Results indicate that significant neuronal changes occur in normal aging, mainly in regions underlying semantic control processes, despite an apparent stability in performance at the behavioral level. Copyright © 2015 Elsevier Inc. All rights reserved.

Visual discrimination predicts naming and semantic association accuracy in Alzheimer disease.

PubMed

Harnish, Stacy M; Neils-Strunjas, Jean; Eliassen, James; Reilly, Jamie; Meinzer, Marcus; Clark, John Greer; Joseph, Jane

2010-12-01

Language impairment is a common symptom of Alzheimer disease (AD), and is thought to be related to semantic processing. This study examines the contribution of another process, namely visual perception, on measures of confrontation naming and semantic association abilities in persons with probable AD. Twenty individuals with probable mild-moderate Alzheimer disease and 20 age-matched controls completed a battery of neuropsychologic measures assessing visual perception, naming, and semantic association ability. Visual discrimination tasks that varied in the degree to which they likely accessed stored structural representations were used to gauge whether structural processing deficits could account for deficits in naming and in semantic association in AD. Visual discrimination abilities of nameable objects in AD strongly predicted performance on both picture naming and semantic association ability, but lacked the same predictive value for controls. Although impaired, performance on visual discrimination tests of abstract shapes and novel faces showed no significant relationship with picture naming and semantic association. These results provide additional evidence to support that structural processing deficits exist in AD, and may contribute to object recognition and naming deficits. Our findings suggest that there is a common deficit in discrimination of pictures using nameable objects, picture naming, and semantic association of pictures in AD. Disturbances in structural processing of pictured items may be associated with lexical-semantic impairment in AD, owing to degraded internal storage of structural knowledge.

Remote semantic memory for public figures in HIV infection, alcoholism, and their comorbidity.

PubMed

Fama, Rosemary; Rosenbloom, Margaret J; Sassoon, Stephanie A; Thompson, Megan A; Pfefferbaum, Adolf; Sullivan, Edith V

2011-02-01

Impairments in component processes of working and episodic memory mark both HIV infection and chronic alcoholism, with compounded deficits often observed in individuals comorbid for these conditions. Remote semantic memory processes, however, have only seldom been studied in these diagnostic groups. Examination of remote semantic memory could provide insight into the underlying processes associated with storage and retrieval of learned information over extended time periods while elucidating spared and impaired cognitive functions in these clinical groups. We examined component processes of remote semantic memory in HIV infection and chronic alcoholism in 4 subject groups (HIV, ALC, HIV + ALC, and age-matched healthy adults) using a modified version of the Presidents Test. Free recall, recognition, and sequencing of presidential candidates and election dates were assessed. In addition, component processes of working, episodic, and semantic memory were assessed with ancillary cognitive tests. The comorbid group (HIV + ALC) was significantly impaired on sequencing of remote semantic information compared with age-matched healthy adults. Free recall of remote semantic information was also modestly impaired in the HIV + ALC group, but normal performance for recognition of this information was observed. Few differences were observed between the single diagnosis groups (HIV, ALC) and healthy adults, although examination of the component processes underlying remote semantic memory scores elicited differences between the HIV and ALC groups. Selective remote memory processes were related to lifetime alcohol consumption in the ALC group and to viral load and depression level in the HIV group. Hepatitis C diagnosis was associated with lower remote semantic memory scores in all 3 clinical groups. Education level did not account for group differences reported. This study provides behavioral support for the existence of adverse effects associated with the comorbidity of HIV infection and chronic alcoholism on selective component processes of memory function, with untoward effects exacerbated by Hepatitis C infection. The pattern of remote semantic memory function in HIV + ALC is consistent with those observed in neurological conditions primarily affecting frontostriatal pathways and suggests that remote memory dysfunction in HIV + ALC may be a result of impaired retrieval processes rather than loss of remote semantic information per se. Copyright © 2010 by the Research Society on Alcoholism.

Semantic Annotation of Complex Text Structures in Problem Reports

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Throop, David R.; Fleming, Land D.

2011-01-01

Text analysis is important for effective information retrieval from databases where the critical information is embedded in text fields. Aerospace safety depends on effective retrieval of relevant and related problem reports for the purpose of trend analysis. The complex text syntax in problem descriptions has limited statistical text mining of problem reports. The presentation describes an intelligent tagging approach that applies syntactic and then semantic analysis to overcome this problem. The tags identify types of problems and equipment that are embedded in the text descriptions. The power of these tags is illustrated in a faceted searching and browsing interface for problem report trending that combines automatically generated tags with database code fields and temporal information.

Proving refinement transformations using extended denotational semantics

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

Winter, V.L.; Boyle, J.M.

1996-04-01

TAMPR is a fully automatic transformation system based on syntactic rewrites. Our approach in a correctness proof is to map the transformation into an axiomatized mathematical domain where formal (and automated) reasoning can be performed. This mapping is accomplished via an extended denotational semantic paradigm. In this approach, the abstract notion of a program state is distributed between an environment function and a store function. Such a distribution introduces properties that go beyond the abstract state that is being modeled. The reasoning framework needs to be aware of these properties in order to successfully complete a correctness proof. This papermore » discusses some of our experiences in proving the correctness of TAMPR transformations.« less

Service composition towards increasing end-user accessibility.

PubMed

Kaklanis, Nikolaos; Votis, Konstantinos; Tzovaras, Dimitrios

2015-01-01

This paper presents the Cloud4all Service Synthesizer Tool, a framework that enables efficient orchestration of accessibility services, as well as their combination into complex forms, providing more advanced functionalities towards increasing the accessibility of end-users with various types of functional limitations. The supported services are described formally within an ontology, enabling, thus, semantic service composition. The proposed service composition approach is based on semantic matching between services specifications on the one hand and user needs/preferences and current context of use on the other hand. The use of automatic composition of accessibility services can significantly enhance end-users' accessibility, especially in cases where assistive solutions are not available in their device.

Parametric Accuracy: Building Information Modeling Process Applied to the Cultural Heritage Preservation

NASA Astrophysics Data System (ADS)

Garagnani, S.; Manferdini, A. M.

2013-02-01

Since their introduction, modeling tools aimed to architectural design evolved in today's "digital multi-purpose drawing boards" based on enhanced parametric elements able to originate whole buildings within virtual environments. Semantic splitting and elements topology are features that allow objects to be "intelligent" (i.e. self-aware of what kind of element they are and with whom they can interact), representing this way basics of Building Information Modeling (BIM), a coordinated, consistent and always up to date workflow improved in order to reach higher quality, reliability and cost reductions all over the design process. Even if BIM was originally intended for new architectures, its attitude to store semantic inter-related information can be successfully applied to existing buildings as well, especially if they deserve particular care such as Cultural Heritage sites. BIM engines can easily manage simple parametric geometries, collapsing them to standard primitives connected through hierarchical relationships: however, when components are generated by existing morphologies, for example acquiring point clouds by digital photogrammetry or laser scanning equipment, complex abstractions have to be introduced while remodeling elements by hand, since automatic feature extraction in available software is still not effective. In order to introduce a methodology destined to process point cloud data in a BIM environment with high accuracy, this paper describes some experiences on monumental sites documentation, generated through a plug-in written for Autodesk Revit and codenamed GreenSpider after its capability to layout points in space as if they were nodes of an ideal cobweb.

Temporal and Spatial Patterns of Neural Activity Associated with Information Selection in Open-ended Creativity.

PubMed

Zhou, Siyuan; Chen, Shi; Wang, Shuang; Zhao, Qingbai; Zhou, Zhijin; Lu, Chunming

2018-02-10

Novel information selection is a crucial process in creativity and was found to be associated with frontal-temporal functional connectivity in the right brain in closed-ended creativity. Since it has distinct cognitive processing from closed-ended creativity, the information selection in open-ended creativity might be underlain by different neural activity. To address this issue, a creative generation task of Chinese two-part allegorical sayings was adopted, and the trials were classified into novel and normal solutions according to participants' self-ratings. The results showed that (1) novel solutions induced a higher lower alpha power in the temporal area, which might be associated with the automatic, unconscious mental process of retrieving extensive semantic information, and (2) upper alpha power in both frontal and temporal areas and frontal-temporal alpha coherence were higher in novel solutions than in normal solutions, which might reflect the selective inhibition of semantic information. Furthermore, lower alpha power in the temporal area showed a reduction with time, while the frontal-temporal and temporal-temporal coherence in the upper alpha band appeared to increase from the early to the middle phase. These dynamic changes in neural activity might reflect the transformation from divergent thinking to convergent thinking in the creative progress. The advantage of the right brain in frontal-temporal connectivity was not found in the present work, which might result from the diversity of solutions in open-ended creativity. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Subliminal presentation of other faces (but not own face) primes behavioral and evoked cortical processing of empathy for pain.

PubMed

Ibáñez, Agustín; Hurtado, Esteban; Lobos, Alejandro; Escobar, Josefina; Trujillo, Natalia; Baez, Sandra; Huepe, David; Manes, Facundo; Decety, Jean

2011-06-29

Current research on empathy for pain emphasizes the overlap in the neural response between the first-hand experience of pain and its perception in others. However, recent studies suggest that the perception of the pain of others may reflect the processing of a threat or negative arousal rather than an automatic pro-social response. It can thus be suggested that pain processing of other-related, but not self-related, information could imply danger rather than empathy, due to the possible threat represented in the expressions of others (especially if associated with pain stimuli). To test this hypothesis, two experiments considering subliminal stimuli were designed. In Experiment 1, neutral and semantic pain expressions previously primed with own or other faces were presented to participants. When other-face priming was used, only the detection of semantic pain expressions was facilitated. In Experiment 2, pictures with pain and neutral scenarios previously used in ERP and fMRI research were used in a categorization task. Those pictures were primed with own or other faces following the same procedure as in Experiment 1 while ERPs were recorded. Early (N1) and late (P3) cortical responses between pain and no-pain were modulated only in the other-face priming condition. These results support the threat value of pain hypothesis and suggest the necessity for the inclusion of own- versus other-related information in future empathy for pain research. Copyright © 2011 Elsevier B.V. All rights reserved.

The priming of priming: Evidence that the N400 reflects context-dependent post-retrieval word integration in working memory.

PubMed

Steinhauer, Karsten; Royle, Phaedra; Drury, John E; Fromont, Lauren A

2017-06-09

Which cognitive processes are reflected by the N400 in ERPs is still controversial. Various recent articles (Lau et al., 2008; Brouwer et al., 2012) have revived the idea that only lexical pre-activation processes (such as automatic spreading activation, ASA) are strongly supported, while post-lexical integrative processes are not. Challenging this view, the present ERP study replicates a behavioral study by McKoon and Ratcliff (1995) who demonstrated that a prime-target pair such as finger - hand shows stronger priming when a majority of other pairs in the list share the analogous semantic relationship (here: part-whole), even at short stimulus onset asynchronies (250ms). We created lists with four different types of semantic relationship (synonyms, part-whole, category-member, and opposites) and compared priming for pairs in a consistent list with those in an inconsistent list as well as unrelated items. Highly significant N400 reductions were found for both relatedness priming (unrelated vs. inconsistent) and relational priming (inconsistent vs. consistent). These data are taken as strong evidence that N400 priming effects are not exclusively carried by ASA-like mechanisms during lexical retrieval but also include post-lexical integration in working memory. We link the present findings to a neurocomputational model for relational reasoning (Knowlton et al., 2012) and to recent discussions of context-dependent conceptual activations (Yee and Thompson-Schill, 2016). Copyright © 2017 Elsevier B.V. All rights reserved.

ERP evidence of distinct processes underlying semantic facilitation and interference in word production.

PubMed

Python, Grégoire; Fargier, Raphaël; Laganaro, Marina

2018-02-01

In everyday conversations, we take advantage of lexical-semantic contexts to facilitate speech production, but at the same time, we also have to reduce interference and inhibit semantic competitors. The blocked cyclic naming paradigm (BCNP) has been used to investigate such context effects. Typical results on production latencies showed semantic facilitation (or no effect) during the first presentation cycle, and interference emerging in subsequent cycles. Even if semantic contexts might be just as facilitative as interfering, previous BCNP studies focused on interference, which was interpreted as reflecting lemma selection and self-monitoring processes. Facilitation in the first cycle was rarely considered/analysed, although it potentially informs on word production to the same extent as interference. Here we contrasted the event-related potential (ERP) signatures of both semantic facilitation and interference in a BCNP. ERPs differed between homogeneous and heterogeneous blocks from about 365 msec post picture onset in the first cycle (facilitation) and in an earlier time-window (270 msec post picture onset) in the third cycle (interference). Three different analyses of the ERPs converge towards distinct processes underlying semantic facilitation and interference (post-lexical vs lexical respectively). The loci of semantic facilitation and interference are interpreted in the context of different theoretical frameworks of language production: the post-lexical locus of semantic facilitation involves interactive phonological-semantic processes and/or self-monitoring, whereas the lexical locus of semantic interference is in line with selection through increased lexical competition. Copyright © 2017 Elsevier Ltd. All rights reserved.

An investigation of time course of category and semantic priming.

PubMed

Ray, Suchismita

2008-04-01

Low semantically similar exemplars in a category demonstrate the category-priming effect through priming of the category (i.e., exemplar-category-exemplar), whereas high semantically similar exemplars in the same category demonstrate the semantic-priming effect (i.e., direct activation of one high semantically similar exemplar by another). The author asked whether the category- and semantic-priming effects are based on a common memory process. She examined this question by testing the time courses of category- and semantic-priming effects. She tested participants on either category- or semantic-priming paradigm at 2 different time intervals (6 min and 42 min) by using a lexical decision task using exemplars from categories. Results showed that the time course of category priming was different from that of semantic priming. The author concludes that these 2 priming effects are based on 2 separate memory processes.

Sub-Lexical Phonological and Semantic Processing of Semantic Radicals: A Primed Naming Study

ERIC Educational Resources Information Center

Zhou, Lin; Peng, Gang; Zheng, Hong-Ying; Su, I-Fan; Wang, William S.-Y.

2013-01-01

Most sinograms (i.e., Chinese characters) are phonograms (phonetic compounds). A phonogram is composed of a semantic radical and a phonetic radical, with the former usually implying the meaning of the phonogram, and the latter providing cues to its pronunciation. This study focused on the sub-lexical processing of semantic radicals which are…

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

PubMed

Robertson, Erin K; Köhler, Stefan

2007-11-05

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

Semantic-based crossmodal processing during visual suppression.

PubMed

Cox, Dustin; Hong, Sang Wook

2015-01-01

To reveal the mechanisms underpinning the influence of auditory input on visual awareness, we examine, (1) whether purely semantic-based multisensory integration facilitates the access to visual awareness for familiar visual events, and (2) whether crossmodal semantic priming is the mechanism responsible for the semantic auditory influence on visual awareness. Using continuous flash suppression, we rendered dynamic and familiar visual events (e.g., a video clip of an approaching train) inaccessible to visual awareness. We manipulated the semantic auditory context of the videos by concurrently pairing them with a semantically matching soundtrack (congruent audiovisual condition), a semantically non-matching soundtrack (incongruent audiovisual condition), or with no soundtrack (neutral video-only condition). We found that participants identified the suppressed visual events significantly faster (an earlier breakup of suppression) in the congruent audiovisual condition compared to the incongruent audiovisual condition and video-only condition. However, this facilitatory influence of semantic auditory input was only observed when audiovisual stimulation co-occurred. Our results suggest that the enhanced visual processing with a semantically congruent auditory input occurs due to audiovisual crossmodal processing rather than semantic priming, which may occur even when visual information is not available to visual awareness.

Ways of making-sense: Local gamma synchronization reveals differences between semantic processing induced by music and language.

PubMed

Barraza, Paulo; Chavez, Mario; Rodríguez, Eugenio

2016-01-01

Similar to linguistic stimuli, music can also prime the meaning of a subsequent word. However, it is so far unknown what is the brain dynamics underlying the semantic priming effect induced by music, and its relation to language. To elucidate these issues, we compare the brain oscillatory response to visual words that have been semantically primed either by a musical excerpt or by an auditory sentence. We found that semantic violation between music-word pairs triggers a classical ERP N400, and induces a sustained increase of long-distance theta phase synchrony, along with a transient increase of local gamma activity. Similar results were observed after linguistic semantic violation except for gamma activity, which increased after semantic congruence between sentence-word pairs. Our findings indicate that local gamma activity is a neural marker that signals different ways of semantic processing between music and language, revealing the dynamic and self-organized nature of the semantic processing. Copyright © 2015 Elsevier Inc. All rights reserved.

Grasping the invisible: semantic processing of abstract words.

PubMed

Zdrazilova, Lenka; Pexman, Penny M

2013-12-01

The problem of how abstract word meanings are represented has been a challenging one. In the present study, we extended the semantic richness approach (e.g., Yap, Tan, Pexman, & Hargreaves in Psychonomic Bulletin & Review 18:742-750, 2011) to abstract words, examining the effects of six semantic richness variables on lexical-semantic processing for 207 abstract nouns. The candidate richness dimensions were context availability (CA), sensory experience rating (SER), valence, arousal, semantic neighborhood (SN), and number of associates (NoA). The behavioral tasks were lexical decision (LDT) and semantic categorization (SCT). Our results showed that the semantic richness variables were significantly related to both LDT and SCT latencies, even after lexical and orthographic factors were controlled. The patterns of richness effects varied across tasks, with CA effects in the LDT, and SER and valence effects in the SCT. These results provide new insight into how abstract meanings may be grounded, and are consistent with a dynamic, multidimensional framework for semantic processing.

Picture grammars in classification and semantic interpretation of 3D coronary vessels visualisations

NASA Astrophysics Data System (ADS)

Ogiela, M. R.; Tadeusiewicz, R.; Trzupek, M.

2009-09-01

The work presents the new opportunity for making semantic descriptions and analysis of medical structures, especially coronary vessels CT spatial reconstructions, with the use of AI graph-based linguistic formalisms. In the paper there will be discussed the manners of applying methods of computational intelligence to the development of a syntactic semantic description of spatial visualisations of the heart's coronary vessels. Such descriptions may be used for both smart ordering of images while archiving them and for their semantic searches in medical multimedia databases. Presented methodology of analysis can furthermore be used for attaining other goals related performance of computer-assisted semantic interpretation of selected elements and/or the entire 3D structure of the coronary vascular tree. These goals are achieved through the use of graph-based image formalisms based on IE graphs generating grammars that allow discovering and automatic semantic interpretation of irregularities visualised on the images obtained during diagnostic examinations of the heart muscle. The basis for the construction of 3D reconstructions of biological objects used in this work are visualisations obtained from helical CT scans, yet the method itself may be applied also for other methods of medical 3D images acquisition. The obtained semantic information makes it possible to make a description of the structure focused on the semantics of various morphological forms of the visualised vessels from the point of view of the operation of coronary circulation and the blood supply of the heart muscle. Thanks to these, the analysis conducted allows fast and — to a great degree — automated interpretation of the semantics of various morphological changes in the coronary vascular tree, and especially makes it possible to detect these stenoses in the lumen of the vessels that can cause critical decrease in blood supply to extensive or especially important fragments of the heart muscle.

A Semantic Analysis of XML Schema Matching for B2B Systems Integration

ERIC Educational Resources Information Center

Kim, Jaewook

2011-01-01

One of the most critical steps to integrating heterogeneous e-Business applications using different XML schemas is schema matching, which is known to be costly and error-prone. Many automatic schema matching approaches have been proposed, but the challenge is still daunting because of the complexity of schemas and immaturity of technologies in…

Web-Based Essay Critiquing System and EFL Students' Writing: A Quantitative and Qualitative Investigation

ERIC Educational Resources Information Center

Lee, Cynthia; Wong, Kelvin C. K.; Cheung, William K.; Lee, Fion S. L.

2009-01-01

The paper first describes a web-based essay critiquing system developed by the authors using latent semantic analysis (LSA), an automatic text analysis technique, to provide students with immediate feedback on content and organisation for revision whenever there is an internet connection. It reports on its effectiveness in enhancing adult EFL…

Using Wmatrix to Explore Discourse of Economic Growth

ERIC Educational Resources Information Center

Hu, Chunyu

2015-01-01

Growth is a concept of particular interest for economic discourse. This paper sets out to explore a small corpus of economic growth, which consists of articles from "The Economist". The corpus software used in this study is a web-based tool Wmatrix, an automatic tagging software able to assign semantic field (domain) tags, and to permit…

Automatic Evaluation for E-Learning Using Latent Semantic Analysis: A Use Case

ERIC Educational Resources Information Center

Farrus, Mireia; Costa-jussa, Marta R.

2013-01-01

Assessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. The current paper aims at analysing and discussing some of the most state-of-the-art assessment systems in education. Later, this work presents a specific use case developed for the Universitat Oberta de…