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.

Semantic relatedness between words in each individual brain: an event-related potential study.

PubMed

Hata, Masahiro; Homae, Fumitaka; Hagiwara, Hiroko

2011-08-26

The relationship between 2 words is judged by the meanings of words. Here, we examined how the semantic relatedness of words is structured in each individual brain. During measurements of event-related potentials (ERPs), participants performed semantic-relatedness judgments of word pairs. For each participant, we divided word pairs into 2 groups--related and unrelated pairs--and compared their ERPs. All of the participants showed a significant N400 effect. However, when we applied an identical grouping of pairs, this effect was observed only in half the number of the participants. These results show that our single-subject analysis of N400 extracted semantic relatedness of words in the individual brain. Future studies using this analysis will clarify the organization of the mental lexicon. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Effects of Forward and Backward Contextual Elaboration on Lexical Inferences: Evidence from a Semantic Relatedness Judgment Task

ERIC Educational Resources Information Center

Hamada, Akira

2015-01-01

Three experiments examined whether the process of lexical inferences differs according to the direction of contextual elaboration using a semantic relatedness judgment task. In Experiment 1, Japanese university students read English sentences where target unknown words were semantically elaborated by prior contextual information (forward lexical…

Examining lateralized semantic access using pictures.

PubMed

Lovseth, Kyle; Atchley, Ruth Ann

2010-03-01

A divided visual field (DVF) experiment examined the semantic processing strategies employed by the cerebral hemispheres to determine if strategies observed with written word stimuli generalize to other media for communicating semantic information. We employed picture stimuli and vary the degree of semantic relatedness between the picture pairs. Participants made an on-line semantic relatedness judgment in response to sequentially presented pictures. We found that when pictures are presented to the right hemisphere responses are generally more accurate than the left hemisphere for semantic relatedness judgments for picture pairs. Furthermore, consistent with earlier DVF studies employing words, we conclude that the RH is better at accessing or maintaining access to information that has a weak or more remote semantic relationship. We also found evidence of faster access for pictures presented to the LH in the strongly-related condition. Overall, these results are consistent with earlier DVF word studies that argue that the cerebral hemispheres each play an important and separable role during semantic retrieval. Copyright 2009 Elsevier Inc. All rights reserved.

A Computational Linguistic Measure of Clustering Behavior on Semantic Verbal Fluency Task Predicts Risk of Future Dementia in the Nun Study

PubMed Central

Pakhomov, Serguei V.S.; Hemmy, Laura S.

2014-01-01

Generative semantic verbal fluency (SVF) tests show early and disproportionate decline relative to other abilities in individuals developing Alzheimer’s disease. Optimal performance on SVF tests depends on the efficiency of using clustered organization of semantically related items and the ability to switch between clusters. Traditional approaches to clustering and switching have relied on manual determination of clusters. We evaluated a novel automated computational linguistic approach for quantifying clustering behavior. Our approach is based on Latent Semantic Analysis (LSA) for computing strength of semantic relatedness between pairs of words produced in response to SVF test. The mean size of semantic clusters (MCS) and semantic chains (MChS) are calculated based on pairwise relatedness values between words. We evaluated the predictive validity of these measures on a set of 239 participants in the Nun Study, a longitudinal study of aging. All were cognitively intact at baseline assessment, measured with the CERAD battery, and were followed in 18 month waves for up to 20 years. The onset of either dementia or memory impairment were used as outcomes in Cox proportional hazards models adjusted for age and education and censored at follow up waves 5 (6.3 years) and 13 (16.96 years). Higher MCS was associated with 38% reduction in dementia risk at wave 5 and 26% reduction at wave 13, but not with the onset of memory impairment. Higher (+1 SD) MChS was associated with 39% dementia risk reduction at wave 5 but not wave 13, and association with memory impairment was not significant. Higher traditional SVF scores were associated with 22–29% memory impairment and 35–40% dementia risk reduction. SVF scores were not correlated with either MCS or MChS. Our study suggests that an automated approach to measuring clustering behavior can be used to estimate dementia risk in cognitively normal individuals. PMID:23845236

A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the nun study.

PubMed

Pakhomov, Serguei V S; Hemmy, Laura S

2014-06-01

Generative semantic verbal fluency (SVF) tests show early and disproportionate decline relative to other abilities in individuals developing Alzheimer's disease. Optimal performance on SVF tests depends on the efficiency of using clustered organization of semantically related items and the ability to switch between clusters. Traditional approaches to clustering and switching have relied on manual determination of clusters. We evaluated a novel automated computational linguistic approach for quantifying clustering behavior. Our approach is based on Latent Semantic Analysis (LSA) for computing strength of semantic relatedness between pairs of words produced in response to SVF test. The mean size of semantic clusters (MCS) and semantic chains (MChS) are calculated based on pairwise relatedness values between words. We evaluated the predictive validity of these measures on a set of 239 participants in the Nun Study, a longitudinal study of aging. All were cognitively intact at baseline assessment, measured with the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery, and were followed in 18-month waves for up to 20 years. The onset of either dementia or memory impairment were used as outcomes in Cox proportional hazards models adjusted for age and education and censored at follow-up waves 5 (6.3 years) and 13 (16.96 years). Higher MCS was associated with 38% reduction in dementia risk at wave 5 and 26% reduction at wave 13, but not with the onset of memory impairment. Higher [+1 standard deviation (SD)] MChS was associated with 39% dementia risk reduction at wave 5 but not wave 13, and association with memory impairment was not significant. Higher traditional SVF scores were associated with 22-29% memory impairment and 35-40% dementia risk reduction. SVF scores were not correlated with either MCS or MChS. Our study suggests that an automated approach to measuring clustering behavior can be used to estimate dementia risk in cognitively normal individuals. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

Hanauer, John B.; Brooks, Patricia J.

2005-01-01

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

Multiple Influences of Semantic Memory on Sentence Processing: Distinct Effects of Semantic Relatedness on Violations of Real-World Event/State Knowledge and Animacy Selection Restrictions

ERIC Educational Resources Information Center

Paczynski, Martin; Kuperberg, Gina R.

2012-01-01

We aimed to determine whether semantic relatedness between an incoming word and its preceding context can override expectations based on two types of stored knowledge: real-world knowledge about the specific events and states conveyed by a verb, and the verb's broader selection restrictions on the animacy of its argument. We recorded event-related…

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.

Real Time Filtering of Tweets Using Wikipedia Concepts and Google Tri-gram Semantic Relatedness

DTIC Science & Technology

2015-11-20

Real Time Filtering of Tweets Using Wikipedia Concepts and Google Tri-gram Semantic Relatedness Anh Dang1, Raheleh Makki1, Abidalrahman Moh’d1...of a topic that the user is interested in receiving relevant posts in real-time. Our proposed approach extracts Wikipedia concepts for profiles and...group name “DALTREC”. Our proposed approach for this year’s filtering task is based on using Wikipedia and Google Trigram for calculating the semantic

Effects of semantic relatedness on recall of stimuli preceding emotional oddballs.

PubMed

Smith, Ryan M; Beversdorf, David Q

2008-07-01

Semantic and episodic memory networks function as highly interconnected systems, both relying on the hippocampal/medial temporal lobe complex (HC/MTL). Episodic memory encoding triggers the retrieval of semantic information, serving to incorporate contextual relationships between the newly acquired memory and existing semantic representations. While emotional material augments episodic memory encoding at the time of stimulus presentation, interactions between emotion and semantic memory that contribute to subsequent episodic recall are not well understood. Using a modified oddball task, we examined the modulatory effects of negative emotion on semantic interactions with episodic memory by measuring the free-recall of serially presented neutral or negative words varying in semantic relatedness. We found increased free-recall for words related to and preceding emotionally negative oddballs, suggesting that negative emotion can indirectly facilitate episodic free-recall by enhancing semantic contributions during encoding. Our findings demonstrate the ability of emotion and semantic memory to interact to mutually enhance free-recall.

Affective Priming in a Lexical Decision Task: Is There an Effect of Words' Concreteness?

ERIC Educational Resources Information Center

Ferré, Pilar; Sánchez-Casas, Rosa

2014-01-01

Affective priming occurs when responses to a target are facilitated when it is preceded by a prime congruent in valence. We conducted two experiments in order to test whether this is a genuine emotional effect or rather it can be accounted for by semantic relatedness between primes and targets. With this aim, semantic relatedness and emotional…

Using a Search Engine-Based Mutually Reinforcing Approach to Assess the Semantic Relatedness of Biomedical Terms

PubMed Central

Hsu, Yi-Yu; Chen, Hung-Yu; Kao, Hung-Yu

2013-01-01

Background Determining the semantic relatedness of two biomedical terms is an important task for many text-mining applications in the biomedical field. Previous studies, such as those using ontology-based and corpus-based approaches, measured semantic relatedness by using information from the structure of biomedical literature, but these methods are limited by the small size of training resources. To increase the size of training datasets, the outputs of search engines have been used extensively to analyze the lexical patterns of biomedical terms. Methodology/Principal Findings In this work, we propose the Mutually Reinforcing Lexical Pattern Ranking (ReLPR) algorithm for learning and exploring the lexical patterns of synonym pairs in biomedical text. ReLPR employs lexical patterns and their pattern containers to assess the semantic relatedness of biomedical terms. By combining sentence structures and the linking activities between containers and lexical patterns, our algorithm can explore the correlation between two biomedical terms. Conclusions/Significance The average correlation coefficient of the ReLPR algorithm was 0.82 for various datasets. The results of the ReLPR algorithm were significantly superior to those of previous methods. PMID:24348899

Is prospective memory enhanced by cue-action semantic relatedness and enactment at encoding?

PubMed

Pereira, Antonina; Ellis, Judi; Freeman, Jayne

2012-09-01

Benefits and costs on prospective memory performance, of enactment at encoding and a semantic association between a cue-action word pair, were investigated in two experiments. Findings revealed superior performance for both younger and older adults following enactment, in contrast to verbal encoding, and when cue-action semantic relatedness was high. Although younger adults outperformed older adults, age did not moderate benefits of cue-action relatedness or enactment. Findings from a second experiment revealed that the inclusion of an instruction to perform a prospective memory task led to increments in response latency to items from the ongoing activity in which that task was embedded, relative to latencies when the ongoing task only was performed. However, this task interference 'cost' did not differ as a function of either cue-action relatedness or enactment. We argue that the high number of cue-action pairs employed here influenced meta-cognitive consciousness, hence determining attention allocation, in all experimental conditions. Copyright © 2012 Elsevier Inc. All rights reserved.

The role of semantic and phonological factors in word recognition: an ERP cross-modal priming study of derivational morphology.

PubMed

Kielar, Aneta; Joanisse, Marc F

2011-01-01

Theories of morphological processing differ on the issue of how lexical and grammatical information are stored and accessed. A key point of contention is whether complex forms are decomposed during recognition (e.g., establish+ment), compared to forms that cannot be analyzed into constituent morphemes (e.g., apartment). In the present study, we examined these issues with respect to English derivational morphology by measuring ERP responses during a cross-modal priming lexical decision task. ERP priming effects for semantically and phonologically transparent derived words (government-govern) were compared to those of semantically opaque derived words (apartment-apart) as well as "quasi-regular" items that represent intermediate cases of morphological transparency (dresser-dress). Additional conditions independently manipulated semantic and phonological relatedness in non-derived words (semantics: couch-sofa; phonology: panel-pan). The degree of N400 ERP priming to morphological forms varied depending on the amount of semantic and phonological overlap between word types, rather than respecting a bivariate distinction between derived and opaque forms. Moreover, these effects could not be accounted for by semantic or phonological relatedness alone. The findings support the theory that morphological relatedness is graded rather than absolute, and depend on the joint contribution of form and meaning overlap. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

PubMed

Hamilton, A Cris; Martin, Randi C

2007-01-01

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

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.

Memory for emotional words: The role of semantic relatedness, encoding task and affective valence.

PubMed

Ferré, Pilar; Fraga, Isabel; Comesaña, Montserrat; Sánchez-Casas, Rosa

2015-01-01

Emotional stimuli have been repeatedly demonstrated to be better remembered than neutral ones. The aim of the present study was to test whether this advantage in memory is mainly produced by the affective content of the stimuli or it can be rather accounted for by factors such as semantic relatedness or type of encoding task. The valence of the stimuli (positive, negative and neutral words that could be either semantically related or unrelated) as well as the type of encoding task (focused on either familiarity or emotionality) was manipulated. The results revealed an advantage in memory for emotional words (either positive or negative) regardless of semantic relatedness. Importantly, this advantage was modulated by the encoding task, as it was reliable only in the task which focused on emotionality. These findings suggest that congruity with the dimension attended at encoding might contribute to the superiority in memory for emotional words, thus offering us a more complex picture of the underlying mechanisms behind the advantage for emotional information in memory.

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

ERIC Educational Resources Information Center

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

2011-01-01

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

Thematic relatedness production norms for 100 object concepts.

PubMed

Jouravlev, Olessia; McRae, Ken

2016-12-01

Knowledge of thematic relations is an area of increased interest in semantic memory research because it is crucial to many cognitive processes. One methodological issue that researchers face is how to identify pairs of thematically related concepts that are well-established in semantic memory for most people. In this article, we review existing methods of assessing thematic relatedness and provide thematic relatedness production norming data for 100 object concepts. In addition, 1,174 related concept pairs obtained from the production norms were classified as reflecting one of the five subtypes of relations: attributive, argument, coordinate, locative, and temporal. The database and methodology will be useful for researchers interested in the effects of thematic knowledge on language processing, analogical reasoning, similarity judgments, and memory. These data will also benefit researchers interested in investigating potential processing differences among the five types of semantic relations.

An RT distribution analysis of relatedness proportion effects in lexical decision and semantic categorization reveals different mechanisms.

PubMed

de Wit, Bianca; Kinoshita, Sachiko

2015-01-01

The magnitude of the semantic priming effect is known to increase as the proportion of related prime-target pairs in an experiment increases. This relatedness proportion (RP) effect was studied in a lexical decision task at a short prime-target stimulus onset asynchrony (240 ms), which is widely assumed to preclude strategic prospective usage of the prime. The analysis of the reaction time (RT) distribution suggested that the observed RP effect reflected a modulation of a retrospective semantic matching process. The pattern of the RP effect on the RT distribution found here is contrasted to that reported in De Wit and Kinoshita's (2014) semantic categorization study, and it is concluded that the RP effect is driven by different underlying mechanisms in lexical decision and semantic categorization.

The N400 and the P300 are not all that independent.

PubMed

Arbel, Yael; Spencer, Kevin M; Donchin, Emanuel

2011-06-01

This study assessed whether two ERP components that are elicited by unexpected events interact. The conditions that are known to elicit the N400 and the P300 ERP components were applied separately and in combination to terminal-words in sentences. Each sentence ended with a terminal-word that was highly expected, semantically unexpected, physically deviant, or both semantically unexpected and physically deviant. In addition, we varied the level of semantic relatedness between the unexpected terminal-words and the expected exemplars. Physically deviant words elicited a P300, whereas semantically unexpected words elicited an N400, whose amplitude was sensitive to the level of semantic relatedness. Words that were both semantically unexpected and physically deviant elicited both an N400 with enhanced amplitude, and a P300 with reduced amplitude. These results suggest an interaction between the processes manifested by the two components. Copyright © 2010 Society for Psychophysiological Research.

Examining Lateralized Semantic Access Using Pictures

ERIC Educational Resources Information Center

Lovseth, Kyle; Atchley, Ruth Ann

2010-01-01

A divided visual field (DVF) experiment examined the semantic processing strategies employed by the cerebral hemispheres to determine if strategies observed with written word stimuli generalize to other media for communicating semantic information. We employed picture stimuli and vary the degree of semantic relatedness between the picture pairs.…

Calculating semantic relatedness for biomedical use in a knowledge-poor environment.

PubMed

Rybinski, Maciej; Aldana-Montes, José

2014-01-01

Computing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a 'closed' problem. We propose a method in which computation for two input texts is based on the idea of comparing the vocabulary associated with the best-fit documents related to those texts. As keyterm extraction is a costly process, it is done in a preprocessing step on a 'per-document' basis in order to limit the on-line processing. The actual computations are executed in a compact vector space, limited by the most informative extraction results. The method has been evaluated on five direct benchmarks by calculating correlation coefficients w.r.t. average human answers. It also has been used on Gene - Disease and Disease- Disease data pairs to highlight its potential use as a data analysis tool. Apart from comparisons with reported results, some interesting features of the method have been studied, i.e. the relationship between result quality, efficiency and applicable trimming threshold for size reduction. Experimental evaluation shows that the presented method obtains results that are comparable with current state of the art methods, even surpassing them on a majority of the benchmarks. Additionally, a possible usage scenario for the method is showcased with a real-world data experiment. Our method improves flexibility of the existing methods without a notable loss of quality. It is a legitimate alternative to the costly construction of specialized knowledge-rich resources.

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

Determining similarity of scientific entities in annotation datasets

PubMed Central

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

2015-01-01

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

Determining similarity of scientific entities in annotation datasets.

PubMed

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

2015-01-01

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

Opposite ERP effects for conscious and unconscious semantic processing under continuous flash suppression.

PubMed

Yang, Yung-Hao; Zhou, Jifan; Li, Kuei-An; Hung, Tifan; Pegna, Alan J; Yeh, Su-Ling

2017-09-01

We examined whether semantic processing occurs without awareness using continuous flash suppression (CFS). In two priming tasks, participants were required to judge whether a target was a word or a non-word, and to report whether the masked prime was visible. Experiment 1 manipulated the lexical congruency between the prime-target pairs and Experiment 2 manipulated their semantic relatedness. Despite the absence of behavioral priming effects (Experiment 1), the ERP results revealed that an N4 component was sensitive to the prime-target lexical congruency (Experiment 1) and semantic relatedness (Experiment 2) when the prime was rendered invisible under CFS. However, these results were reversed with respect to those that emerged when the stimuli were perceived consciously. Our findings suggest that some form of lexical and semantic processing can occur during CFS-induced unawareness, but are associated with different electrophysiological outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

Relatedness Proportion Effects in Semantic Categorization: Reconsidering the Automatic Spreading Activation Process

ERIC Educational Resources Information Center

de Wit, Bianca; Kinoshita, Sachiko

2014-01-01

Semantic priming effects at a short prime-target stimulus onset asynchrony are commonly explained in terms of an automatic spreading activation process. According to this view, the proportion of related trials should have no impact on the size of the semantic priming effect. Using a semantic categorization task ("Is this a living…

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

PubMed

Chen, Xuqian; Liao, Yuanlan; Chen, Xianzhe

2017-08-01

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

The Influence of Semantic Property and Grammatical Class on Semantic Selection

ERIC Educational Resources Information Center

Yang, Fan-pei Gloria; Khodaparast, Navid; Bradley, Kailyn; Fang, Min-Chieh; Bernstein, Ari; Krawczyk, Daniel C.

2013-01-01

Research to-date has not successfully demonstrated consistent neural distinctions for different types of ambiguity or explored the effect of grammatical class on semantic selection. We conducted a relatedness judgment task using event-related functional magnetic resonance imaging (fMRI) to further explore these topics. Participants judged…

Are Judgments of Semantic Relatedness Systematically Impaired in Alzheimer's Disease?

ERIC Educational Resources Information Center

Hornberger, M.; Bell, B.; Graham, K. S.; Rogers, T. T.

2009-01-01

We employed a triadic comparison task in patients with Alzheimer's disease (AD) and healthy controls to contrast (a) multidimensional scaling (MDS) and accuracy-based assessments of semantic memory, and (b) degraded-store versus degraded-access accounts of semantic impairment in Alzheimer's disease (AD). Similar to other studies using triadic…

Phasic Affective Modulation of Semantic Priming

ERIC Educational Resources Information Center

Topolinski, Sascha; Deutsch, Roland

2013-01-01

The present research demonstrates that very brief variations in affect, being around 1 s in length and changing from trial to trial independently from semantic relatedness of primes and targets, modulate the amount of semantic priming. Implementing consonant and dissonant chords (Experiments 1 and 5), naturalistic sounds (Experiment 2), and visual…

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

PubMed Central

Bosworth, Rain G.; Emmorey, Karen

2010-01-01

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

Orthographic effects in spoken word recognition: Evidence from Chinese.

PubMed

Qu, Qingqing; Damian, Markus F

2017-06-01

Extensive evidence from alphabetic languages demonstrates a role of orthography in the processing of spoken words. Because alphabetic systems explicitly code speech sounds, such effects are perhaps not surprising. However, it is less clear whether orthographic codes are involuntarily accessed from spoken words in languages with non-alphabetic systems, in which the sound-spelling correspondence is largely arbitrary. We investigated the role of orthography via a semantic relatedness judgment task: native Mandarin speakers judged whether or not spoken word pairs were related in meaning. Word pairs were either semantically related, orthographically related, or unrelated. Results showed that relatedness judgments were made faster for word pairs that were semantically related than for unrelated word pairs. Critically, orthographic overlap on semantically unrelated word pairs induced a significant increase in response latencies. These findings indicate that orthographic information is involuntarily accessed in spoken-word recognition, even in a non-alphabetic language such as Chinese.

Opposing Effects of Semantic Diversity in Lexical and Semantic Relatedness Decisions

PubMed Central

2015-01-01

Semantic ambiguity has often been divided into 2 forms: homonymy, referring to words with 2 unrelated interpretations (e.g., bark), and polysemy, referring to words associated with a number of varying but semantically linked uses (e.g., twist). Typically, polysemous words are thought of as having a fixed number of discrete definitions, or “senses,” with each use of the word corresponding to one of its senses. In this study, we investigated an alternative conception of polysemy, based on the idea that polysemous variation in meaning is a continuous, graded phenomenon that occurs as a function of contextual variation in word usage. We quantified this contextual variation using semantic diversity (SemD), a corpus-based measure of the degree to which a particular word is used in a diverse set of linguistic contexts. In line with other approaches to polysemy, we found a reaction time (RT) advantage for high SemD words in lexical decision, which occurred for words of both high and low imageability. When participants made semantic relatedness decisions to word pairs, however, responses were slower to high SemD pairs, irrespective of whether these were related or unrelated. Again, this result emerged irrespective of the imageability of the word. The latter result diverges from previous findings using homonyms, in which ambiguity effects have only been found for related word pairs. We argue that participants were slower to respond to high SemD words because their high contextual variability resulted in noisy, underspecified semantic representations that were more difficult to compare with one another. We demonstrated this principle in a connectionist computational model that was trained to activate distributed semantic representations from orthographic inputs. Greater variability in the orthography-to-semantic mappings of high SemD words resulted in a lower degree of similarity for related pairs of this type. At the same time, the representations of high SemD unrelated pairs were less distinct from one another. In addition, the model demonstrated more rapid semantic activation for high SemD words, thought to underpin the processing advantage in lexical decision. These results support the view that polysemous variation in word meaning can be conceptualized in terms of graded variation in distributed semantic representations. PMID:25751041

Exploiting salient semantic analysis for information retrieval

NASA Astrophysics Data System (ADS)

Luo, Jing; Meng, Bo; Quan, Changqin; Tu, Xinhui

2016-11-01

Recently, many Wikipedia-based methods have been proposed to improve the performance of different natural language processing (NLP) tasks, such as semantic relatedness computation, text classification and information retrieval. Among these methods, salient semantic analysis (SSA) has been proven to be an effective way to generate conceptual representation for words or documents. However, its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use SSA to improve the information retrieval performance, and propose a SSA-based retrieval method under the language model framework. First, SSA model is adopted to build conceptual representations for documents and queries. Then, these conceptual representations and the bag-of-words (BOW) representations can be used in combination to estimate the language models of queries and documents. The proposed method is evaluated on several standard text retrieval conference (TREC) collections. Experiment results on standard TREC collections show the proposed models consistently outperform the existing Wikipedia-based retrieval methods.

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

PubMed

Meade, Melissa E; Fernandes, Myra A

2016-07-01

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

The Influence of refractoriness upon comprehension of non-verbal auditory stimuli.

PubMed

Crutch, Sebastian J; Warrington, Elizabeth K

2008-01-01

An investigation of non-verbal auditory comprehension in two patients with global aphasia following stroke is reported. The primary aim of the investigation was to establish whether refractory access disorders can affect non-verbal input modalities. All previous reports of refractoriness, a cognitive syndrome characterized by response inconsistency, sensitivity to temporal factors and insensitivity to item frequency, have involved comprehension tasks which have a verbal component. Two main experiments are described. The first consists of a novel sound-to-picture and sound-to-word matching task in which comprehension of environmental sounds is probed under conditions of semantic relatedness and semantic unrelatedness. In addition to the two stroke patients, the performance of a group of 10 control patients with non-vascular pathology is reported, along with evidence of semantic relatedness effects in sound comprehension. The second experiment examines environmental sound comprehension within a repetitive probing paradigm which affords assessment of the effects of semantic relatedness, response consistency and presentation rate. It is demonstrated that the two stroke patients show a significant increase in error rate across multiple probes of the same set of sound stimuli, indicating the presence of refractoriness within this non-verbal domain. The implications of the results are discussed with reference to our current understanding of the mechanisms of refractoriness.

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…

Age-Related Brain Activation Changes during Rule Repetition in Word-Matching.

PubMed

Methqal, Ikram; Pinsard, Basile; Amiri, Mahnoush; Wilson, Maximiliano A; Monchi, Oury; Provost, Jean-Sebastien; Joanette, Yves

2017-01-01

Objective: The purpose of this study was to explore the age-related brain activation changes during a word-matching semantic-category-based task, which required either repeating or changing a semantic rule to be applied. In order to do so, a word-semantic rule-based task was adapted from the Wisconsin Sorting Card Test, involving the repeated feedback-driven selection of given pairs of words based on semantic category-based criteria. Method: Forty healthy adults (20 younger and 20 older) performed a word-matching task while undergoing a fMRI scan in which they were required to pair a target word with another word from a group of three words. The required pairing is based on three word-pair semantic rules which correspond to different levels of semantic control demands: functional relatedness, moderately typical-relatedness (which were considered as low control demands), and atypical-relatedness (high control demands). The sorting period consisted of a continuous execution of the same sorting rule and an inferred trial-by-trial feedback was given. Results: Behavioral performance revealed increases in response times and decreases of correct responses according to the level of semantic control demands (functional vs. typical vs. atypical) for both age groups (younger and older) reflecting graded differences in the repetition of the application of a given semantic rule. Neuroimaging findings of significant brain activation showed two main results: (1) Greater task-related activation changes for the repetition of the application of atypical rules relative to typical and functional rules, and (2) Changes (older > younger) in the inferior prefrontal regions for functional rules and more extensive and bilateral activations for typical and atypical rules. Regarding the inter-semantic rules comparison, only task-related activation differences were observed for functional > typical (e.g., inferior parietal and temporal regions bilaterally) and atypical > typical (e.g., prefrontal, inferior parietal, posterior temporal, and subcortical regions). Conclusion: These results suggest that healthy cognitive aging relies on the adaptive changes of inferior prefrontal resources involved in the repetitive execution of semantic rules, thus reflecting graded differences in support of task demands.

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

PubMed Central

Jackson, Alice F.; Bolger, Donald J.

2014-01-01

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

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

PubMed

Jackson, Alice F; Bolger, Donald J

2014-01-01

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

How are 'Barack Obama' and 'President Elect' differentially stored in the brain? An ERP investigation on the processing of proper and common noun pairs.

PubMed

Proverbio, Alice Mado; Mariani, Serena; Zani, Alberto; Adorni, Roberta

2009-09-23

One of the most debated issues in the cognitive neuroscience of language is whether distinct semantic domains are differentially represented in the brain. Clinical studies described several anomic dissociations with no clear neuroanatomical correlate. Neuroimaging studies have shown that memory retrieval is more demanding for proper than common nouns in that the former are purely arbitrary referential expressions. In this study a semantic relatedness paradigm was devised to investigate neural processing of proper and common nouns. 780 words (arranged in pairs of Italian nouns/adjectives and the first/last names of well known persons) were presented. Half pairs were semantically related ("Woody Allen" or "social security"), while the others were not ("Sigmund Parodi" or "judicial cream"). All items were balanced for length, frequency, familiarity and semantic relatedness. Participants were to decide about the semantic relatedness of the two items in a pair. RTs and N400 data suggest that the task was more demanding for common nouns. The LORETA neural generators for the related-unrelated contrast (for proper names) included the left fusiform gyrus, right medial temporal gyrus, limbic and parahippocampal regions, inferior parietal and inferior frontal areas, which are thought to be involved in the conjoined processing a familiar face with the relevant episodic information. Person name was more emotional and sensory vivid than common noun semantic access. When memory retrieval is not required, proper name access (conspecifics knowledge) is not more demanding. The neural generators of N400 to unrelated items (unknown persons and things) did not differ as a function of lexical class, thus suggesting that proper and common nouns are not treated differently as belonging to different grammatical classes.

How Are ‘Barack Obama’ and ‘President Elect’ Differentially Stored in the Brain? An ERP Investigation on the Processing of Proper and Common Noun Pairs

PubMed Central

Proverbio, Alice Mado; Mariani, Serena; Zani, Alberto; Adorni, Roberta

2009-01-01

Background One of the most debated issues in the cognitive neuroscience of language is whether distinct semantic domains are differentially represented in the brain. Clinical studies described several anomic dissociations with no clear neuroanatomical correlate. Neuroimaging studies have shown that memory retrieval is more demanding for proper than common nouns in that the former are purely arbitrary referential expressions. In this study a semantic relatedness paradigm was devised to investigate neural processing of proper and common nouns. Methodology/Principal Findings 780 words (arranged in pairs of Italian nouns/adjectives and the first/last names of well known persons) were presented. Half pairs were semantically related (“Woody Allen” or “social security”), while the others were not (“Sigmund Parodi” or “judicial cream”). All items were balanced for length, frequency, familiarity and semantic relatedness. Participants were to decide about the semantic relatedness of the two items in a pair. RTs and N400 data suggest that the task was more demanding for common nouns. The LORETA neural generators for the related-unrelated contrast (for proper names) included the left fusiform gyrus, right medial temporal gyrus, limbic and parahippocampal regions, inferior parietal and inferior frontal areas, which are thought to be involved in the conjoined processing a familiar face with the relevant episodic information. Person name was more emotional and sensory vivid than common noun semantic access. Conclusions/Significance When memory retrieval is not required, proper name access (conspecifics knowledge) is not more demanding. The neural generators of N400 to unrelated items (unknown persons and things) did not differ as a function of lexical class, thus suggesting that proper and common nouns are not treated differently as belonging to different grammatical classes. PMID:19774070

The relation between body semantics and spatial body representations.

PubMed

van Elk, Michiel; Blanke, Olaf

2011-11-01

The present study addressed the relation between body semantics (i.e. semantic knowledge about the human body) and spatial body representations, by presenting participants with word pairs, one below the other, referring to body parts. The spatial position of the word pairs could be congruent (e.g. EYE / MOUTH) or incongruent (MOUTH / EYE) with respect to the spatial position of the words' referents. In addition, the spatial distance between the words' referents was varied, resulting in word pairs referring to body parts that are close (e.g. EYE / MOUTH) or far in space (e.g. EYE / FOOT). A spatial congruency effect was observed when subjects made an iconicity judgment (Experiments 2 and 3) but not when making a semantic relatedness judgment (Experiment 1). In addition, when making a semantic relatedness judgment (Experiment 1) reaction times increased with increased distance between the body parts but when making an iconicity judgment (Experiments 2 and 3) reaction times decreased with increased distance. These findings suggest that the processing of body-semantics results in the activation of a detailed visuo-spatial body representation that is modulated by the specific task requirements. We discuss these new data with respect to theories of embodied cognition and body semantics. Copyright © 2011 Elsevier B.V. All rights reserved.

Morphological awareness as a function of semantics, phonology, and orthography and as a predictor of reading comprehension in Chinese.

PubMed

Li, Hong; Dronjic, Vedran; Chen, X I; Li, Yixun; Cheng, Yahua; Wu, Xinchun

2017-09-01

This study investigates the contributions of semantic, phonological, and orthographic factors to morphological awareness of 413 Chinese-speaking students in Grades 2, 4, and 6, and its relationship with reading comprehension. Participants were orally presented with pairs of bimorphemic compounds and asked to judge whether the first morphemes of the words shared a meaning. Morpheme identity (same or different), whole-word semantic relatedness (high or low), orthography (same or different), and phonology (same or different) were manipulated. By Grade 6, children were able to focus on meaning similarities across morphemes while ignoring the distraction of form, but they remained influenced by whole-word semantic relatedness. Children's ability to overcome the distraction of phonology consistently improved with age, but did not reach ceiling, whereas the parallel ability for orthography reached ceiling at Grade 6. Morphological judgment performance was a significant unique predictor of reading comprehension when character naming and vocabulary knowledge were accounted for.

Delineating the Effect of Semantic Congruency on Episodic Memory: The Role of Integration and Relatedness

PubMed Central

Bein, Oded; Livneh, Neta; Reggev, Niv; Gilead, Michael; Goshen-Gottstein, Yonatan; Maril, Anat

2015-01-01

A fundamental challenge in the study of learning and memory is to understand the role of existing knowledge in the encoding and retrieval of new episodic information. The importance of prior knowledge in memory is demonstrated in the congruency effect—the robust finding wherein participants display better memory for items that are compatible, rather than incompatible, with their pre-existing semantic knowledge. Despite its robustness, the mechanism underlying this effect is not well understood. In four studies, we provide evidence that demonstrates the privileged explanatory power of the elaboration-integration account over alternative hypotheses. Furthermore, we question the implicit assumption that the congruency effect pertains to the truthfulness/sensibility of a subject-predicate proposition, and show that congruency is a function of semantic relatedness between item and context words. PMID:25695759

Delineating the effect of semantic congruency on episodic memory: the role of integration and relatedness.

PubMed

Bein, Oded; Livneh, Neta; Reggev, Niv; Gilead, Michael; Goshen-Gottstein, Yonatan; Maril, Anat

2015-01-01

A fundamental challenge in the study of learning and memory is to understand the role of existing knowledge in the encoding and retrieval of new episodic information. The importance of prior knowledge in memory is demonstrated in the congruency effect-the robust finding wherein participants display better memory for items that are compatible, rather than incompatible, with their pre-existing semantic knowledge. Despite its robustness, the mechanism underlying this effect is not well understood. In four studies, we provide evidence that demonstrates the privileged explanatory power of the elaboration-integration account over alternative hypotheses. Furthermore, we question the implicit assumption that the congruency effect pertains to the truthfulness/sensibility of a subject-predicate proposition, and show that congruency is a function of semantic relatedness between item and context words.

Differential Lexical Predictors of Reading Comprehension in Fourth Graders

ERIC Educational Resources Information Center

Swart, Nicole M.; Muijselaar, Marloes M. L.; Steenbeek-Planting, Esther G.; Droop, Mienke; de Jong, Peter F.; Verhoeven, L.

2017-01-01

The mental lexicon plays a central role in reading comprehension (Perfetti & Stafura, 2014). It encompasses the number of lexical entries in spoken and written language (vocabulary breadth), the semantic quality of these entries (vocabulary depth), and the connection strength between lexical representations (semantic relatedness); as such, it…

The processing of semantic relatedness in the brain: Evidence from associative and categorical false recognition effects following transcranial direct current stimulation of the left anterior temporal lobe.

PubMed

Díez, Emiliano; Gómez-Ariza, Carlos J; Díez-Álamo, Antonio M; Alonso, María A; Fernandez, Angel

2017-08-01

A dominant view of the role of the anterior temporal lobe (ATL) in semantic memory is that it serves as an integration hub, specialized in the processing of semantic relatedness by way of mechanisms that bind together information from different brain areas to form coherent amodal representations of concepts. Two recent experiments, using brain stimulation techniques along with the Deese-Roediger-McDermott (DRM) paradigm, have found a consistent false memory reduction effect following stimulation of the ATL, pointing to the importance of the ATL in semantic/conceptual processing. To more precisely identify the specific process being involved, we conducted a DRM experiment in which transcranial direct current stimulation (anode/cathode/sham) was applied over the participants' left ATL during the study of lists of words that were associatively related to their non-presented critical words (e.g., rotten, worm, red, tree, liqueur, unripe, cake, food, eden, peel, for the critical item apple) or categorically related (e.g., pear, banana, peach, orange, cantaloupe, watermelon, strawberry, cherry, kiwi, plum, for the same critical item apple). The results showed that correct recognition was not affected by stimulation. However, an interaction between stimulation condition and type of relation for false memories was found, explained by a significant false recognition reduction effect in the anodal condition for associative lists that was not observed for categorical lists. Results are congruent with previous findings and, more importantly, they help to clarify the nature and locus of false memory reduction effects, suggesting a differential role of the left ATL, and providing critical evidence for understanding the creation of semantic relatedness-based memory illusions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effects of Related and Unrelated Context on Recall and Recognition by Adults with High-Functioning Autism Spectrum Disorder

ERIC Educational Resources Information Center

Bowler, Dermot M.; Gaigg, Sebastian B.; Gardiner, John M.

2008-01-01

Memory in autism spectrum disorder (ASD) is characterised by greater difficulties with recall rather than recognition and with a diminished use of semantic or associative relatedness in the aid of recall. Two experiments are reported that test the effects of item-context relatedness on recall and recognition in adults with high-functioning ASD…

Semantic Connection or Visual Connection: Investigating the True Source of Confusion

ERIC Educational Resources Information Center

Ishii, Tomoko

2015-01-01

It has been repeatedly argued among vocabulary researchers that semantically related words should not be taught simultaneously because they can interfere with each other. However, the question of what types of relatedness cause interference has rarely been examined carefully. In addition, there are disagreements among the past studies that have…

Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec.

PubMed

Zhu, Yongjun; Yan, Erjia; Wang, Fei

2017-07-03

Understanding semantic relatedness and similarity between biomedical terms has a great impact on a variety of applications such as biomedical information retrieval, information extraction, and recommender systems. The objective of this study is to examine word2vec's ability in deriving semantic relatedness and similarity between biomedical terms from large publication data. Specifically, we focus on the effects of recency, size, and section of biomedical publication data on the performance of word2vec. We download abstracts of 18,777,129 articles from PubMed and 766,326 full-text articles from PubMed Central (PMC). The datasets are preprocessed and grouped into subsets by recency, size, and section. Word2vec models are trained on these subtests. Cosine similarities between biomedical terms obtained from the word2vec models are compared against reference standards. Performance of models trained on different subsets are compared to examine recency, size, and section effects. Models trained on recent datasets did not boost the performance. Models trained on larger datasets identified more pairs of biomedical terms than models trained on smaller datasets in relatedness task (from 368 at the 10% level to 494 at the 100% level) and similarity task (from 374 at the 10% level to 491 at the 100% level). The model trained on abstracts produced results that have higher correlations with the reference standards than the one trained on article bodies (i.e., 0.65 vs. 0.62 in the similarity task and 0.66 vs. 0.59 in the relatedness task). However, the latter identified more pairs of biomedical terms than the former (i.e., 344 vs. 498 in the similarity task and 339 vs. 503 in the relatedness task). Increasing the size of dataset does not always enhance the performance. Increasing the size of datasets can result in the identification of more relations of biomedical terms even though it does not guarantee better precision. As summaries of research articles, compared with article bodies, abstracts excel in accuracy but lose in coverage of identifiable relations.

The semantic organisation of proper nouns: the case of people and brand names.

PubMed

Crutch, Sebastian J; Warrington, Elizabeth K

2004-01-01

We describe the performance of a patient (AZ) with a semantic refractory access disorder on a series of experiments probing comprehension of two broad proper noun categories, namely person names and brand names. By inducing and manipulating the semantic relatedness effects which are commonly observed in semantic refractory access patients, we demonstrate that famous person knowledge is primarily organised by occupation, whilst knowledge of brands is organised by product type. For instance, we show that AZ has significantly greater difficulty identifying a famous person from among distractor personalities who have the same occupation (e.g. composers: Beethoven, Mozart, Handel, and Bach) than those who have different occupations (e.g. Beethoven, Picasso, Shakespeare, and Jefferson). We also show that such semantic relatedness effects do not occur when stimuli are grouped by another variable such as nationality. We argue that these semantic distance effects reflect the greater build-up of refractoriness among concepts which are supported by shared neural circuitry. In psychological space, it seems natural that these individuals should be classified in this way. The strength of our findings lie in the fact that this organisation of psychological space is mirrored by neural organisation. Thus, we report a previously undocumented degree of fine-grain organisation within conceptual knowledge of these classes of proper nouns. We also interpret our data as providing the strongest empirical support to date for the semantic module of cognitive models of person recognition.

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

ERIC Educational Resources Information Center

Bosworth, Rain G.; Emmorey, Karen

2010-01-01

Iconicity is a property that pervades the lexicon of many sign languages, including American Sign Language (ASL). Iconic signs exhibit a motivated, nonarbitrary mapping between the form of the sign and its meaning. We investigated whether iconicity enhances semantic priming effects for ASL and whether iconic signs are recognized more quickly than…

Refractory Access Disorders and the Organization of Concrete and Abstract Semantics: Do they Differ?

PubMed Central

Hamilton, A. Cris; Coslett, H. Branch

2010-01-01

Patients with “refractory semantic access deficits” demonstrate several unique features that make them important sources of insight into the organization of semantic representations. Here we attempt to replicate several novel findings from single-case studies reported in the literature. Patient UM– 103 displays the cardinal features of a “refractory semantic access deficit” and showed many of the same effects of semantic relatedness reported in the literature. However, when probing concrete and abstract words, this patient revealed very different patterns of performance compared to two previously reported patients. We discuss the implications of our data for models of semantic organization of abstract and concrete words. PMID:18569737

What Phonological Facilitation Tells about Semantic Interference: A Dual-Task Study

PubMed Central

Ayora, Pauline; Peressotti, Francesca; Alario, F.-Xavier; Mulatti, Claudio; Pluchino, Patrick; Job, Remo; Dell'Acqua, Roberto

2011-01-01

Despite increasing interest in the topic, the extent to which linguistic processing demands attentional resources remains poorly understood. We report an empirical re-examination of claims about lexical processing made on the basis of the picture–word interference task when merged in a dual-task psychological refractory period (PRP) paradigm. Two experiments were conducted in which participants were presented with a tone followed, at varying stimulus onset asynchronies (SOAs), by a picture–word stimulus. In Experiment 1, the phonological relatedness between pictures and words was manipulated. Begin- and end-related words decreased picture naming latencies relative to unrelated words. This effect was additive with SOA effects. In Experiment 2, both the semantic and the phonological relatedness between pictures and words were manipulated. Replicating Experiment 1, effects arising from the phonological manipulation were additive with SOA effects on picture naming latencies. In contrast, effects arising from the semantic manipulation were under additive with SOA effects on picture naming latencies, that is, semantic interference decreased as SOA was decreased. Such contrastive pattern suggests that semantic and phonological effects on picture naming latencies are characterized by distinguishable sources, the former prior to the PRP bottleneck and the latter at the PRP bottleneck or after. The present findings are discussed in relation to current models of language production. PMID:21716584

A test of the symbol interdependency hypothesis with both concrete and abstract stimuli.

PubMed

Malhi, Simritpal Kaur; Buchanan, Lori

2018-01-01

In Experiment 1, the symbol interdependency hypothesis was tested with both concrete and abstract stimuli. Symbolic (i.e., semantic neighbourhood distance) and embodied (i.e., iconicity) factors were manipulated in two tasks-one that tapped symbolic relations (i.e., semantic relatedness judgment) and another that tapped embodied relations (i.e., iconicity judgment). Results supported the symbol interdependency hypothesis in that the symbolic factor was recruited for the semantic relatedness task and the embodied factor was recruited for the iconicity task. Across tasks, and especially in the iconicity task, abstract stimuli resulted in shorter RTs. This finding was in contrast to the concreteness effect where concrete words result in shorter RTs. Experiment 2 followed up on this finding by replicating the iconicity task from Experiment 1 in an ERP paradigm. Behavioural results continued to show a reverse concreteness effect with shorter RTs for abstract stimuli. However, ERP results paralleled the N400 and anterior N700 concreteness effects found in the literature, with more negative amplitudes for concrete stimuli.

A test of the symbol interdependency hypothesis with both concrete and abstract stimuli

PubMed Central

Buchanan, Lori

2018-01-01

In Experiment 1, the symbol interdependency hypothesis was tested with both concrete and abstract stimuli. Symbolic (i.e., semantic neighbourhood distance) and embodied (i.e., iconicity) factors were manipulated in two tasks—one that tapped symbolic relations (i.e., semantic relatedness judgment) and another that tapped embodied relations (i.e., iconicity judgment). Results supported the symbol interdependency hypothesis in that the symbolic factor was recruited for the semantic relatedness task and the embodied factor was recruited for the iconicity task. Across tasks, and especially in the iconicity task, abstract stimuli resulted in shorter RTs. This finding was in contrast to the concreteness effect where concrete words result in shorter RTs. Experiment 2 followed up on this finding by replicating the iconicity task from Experiment 1 in an ERP paradigm. Behavioural results continued to show a reverse concreteness effect with shorter RTs for abstract stimuli. However, ERP results paralleled the N400 and anterior N700 concreteness effects found in the literature, with more negative amplitudes for concrete stimuli. PMID:29590121

Native-Language Phonological Interference in Early Hakka-Mandarin Bilinguals' Visual Recognition of Chinese Two-Character Compounds: Evidence from the Semantic-Relatedness Decision Task

ERIC Educational Resources Information Center

Wu, Shiyu; Ma, Zheng

2017-01-01

Previous research has indicated that, in viewing a visual word, the activated phonological representation in turn activates its homophone, causing semantic interference. Using this mechanism of phonological mediation, this study investigated native-language phonological interference in visual recognition of Chinese two-character compounds by early…

The Role of Semantics in Translation Recognition: Effects of Number of Translations, Dominance of Translations and Semantic Relatedness of Multiple Translations

ERIC Educational Resources Information Center

Laxen, Jannika; Lavaur, Jean-Marc

2010-01-01

This study aims to examine the influence of multiple translations of a word on bilingual processing in three translation recognition experiments during which French-English bilinguals had to decide whether two words were translations of each other or not. In the first experiment, words with only one translation were recognized as translations…

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

PubMed

Wiese, Holger; Schweinberger, Stefan R

2015-01-01

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

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

The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing

PubMed Central

Weber, Kirsten; Lau, Ellen F.; Stillerman, Benjamin; Kuperberg, Gina R.

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions—a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment. PMID:27010386

The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing.

PubMed

Weber, Kirsten; Lau, Ellen F; Stillerman, Benjamin; Kuperberg, Gina R

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions-a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment.

Affective priming with pictures of emotional scenes: the role of perceptual similarity and category relatedness.

PubMed

Avero, Pedro; Calvo, Manuel G

2006-05-01

Prime pictures portraying pleasant or unpleasant scenes were briefly presented (150-ms display; SOAs of 300 or 800 ms), followed by probe pictures either congruent or incongruent in emotional valence. In an evaluative decision task, participants responded whether the probe was emotionally positive or negative. Affective priming was reflected in shorter response latencies for congruent than for incongruent prime-probe pairs. Although this effect was enhanced by perceptual similarity between the prime and the probe, it also occurred for probes that were physically different, and the effect generalized across semantic categories (animals vs. people). It is concluded that affective priming is a genuine phenomenon, in that it occurs as a function of stimulus emotional content, in the absence of both perceptual similarity and semantic category relatedness between the prime and the probe.

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

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

PubMed

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

2014-01-03

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

Unconscious semantic activation depends on feature-specific attention allocation.

PubMed

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

2012-01-01

We examined whether semantic activation by subliminally presented stimuli is dependent upon the extent to which participants assign attention to specific semantic stimulus features and stimulus dimensions. Participants pronounced visible target words that were preceded by briefly presented, masked prime words. Both affective and non-affective semantic congruence of the prime-target pairs were manipulated under conditions that either promoted selective attention for affective stimulus information or selective attention for non-affective semantic stimulus information. In line with our predictions, results showed that affective congruence had a clear impact on word pronunciation latencies only if participants were encouraged to assign attention to the affective stimulus dimension. In contrast, non-affective semantic relatedness of the prime-target pairs produced no priming at all. Our findings are consistent with the hypothesis that unconscious activation of (affective) semantic information is modulated by feature-specific attention allocation. Copyright © 2011 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-10-01

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

Sentence Context Prevails Over Word Association in Aphasia Patients with Spared Comprehension: Evidence from N400 Event-Related Potential

PubMed Central

Khachatryan, Elvira; De Letter, Miet; Vanhoof, Gertie; Goeleven, Ann; Van Hulle, Marc M.

2017-01-01

Behavioral and event-related potential (ERP) studies on aphasia patients showed that lexical information is not lost but rather its integration into the working context is hampered. Studies have been conducted on the processing of sentence-level information (meaningful versus meaningless) and of word-level information (related versus unrelated) in aphasia patients, but we are not aware of any study that assesses the relationship between the two. In healthy subjects the processing of a single word in a sentence context has been studied using the N400 ERP. It was shown that, even when there is only a weak expectation of a final word in a sentence, this expectation will dominate word relatedness. In order to study the effect of semantic relatedness between words in sentence processing in aphasia patients, we conducted a crossed-design ERP study, crossing the factors of word relatedness and sentence congruity. We tested aphasia patients with mild to minimum comprehension deficit and healthy young and older (age-matched with our patients) controls on a semantic anomaly judgment task when simultaneously recording EEG. Our results show that our aphasia patient’s N400 amplitudes in response to the sentences of our crossed-design study were similar to those of our age-matched healthy subjects. However, we detected an increase in the N400 ERP latency in those patients, indicating a delay in the integration of the new word into the working context. Additionally, we observed a positive correlation between comprehension level of those patients and N400 effect in response to meaningful sentences without word relatedness contrasted to meaningless sentences without word relatedness. PMID:28119590

Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency.

PubMed

Pakhomov, Serguei V S; Eberly, Lynn; Knopman, David

2016-08-01

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. Copyright © 2016 Elsevier Ltd. All rights reserved.

Age of Acquisition Effects on Word Processing for Chinese Native Learners’ English: ERP Evidence for the Arbitrary Mapping Hypothesis

PubMed Central

Xue, Jin; Liu, Tongtong; Marmolejo-Ramos, Fernando; Pei, Xuna

2017-01-01

The present study aimed at distinguishing processing of early learned L2 words from late ones for Chinese natives who learn English as a foreign language. Specifically, we examined whether the age of acquisition (AoA) effect arose during the arbitrary mapping from conceptual knowledge onto linguistic units. The behavior and ERP data were collected when 28 Chinese-English bilinguals were asked to perform semantic relatedness judgment on word pairs, which represented three stages of word learning (i.e., primary school, junior and senior high schools). A 3 (AoA: early vs. intermediate vs. late) × 2 (regularity: regular vs. irregular) × 2 (semantic relatedness: related vs. unrelated) × 2 (hemisphere: left vs. right) × 3 (brain area: anterior vs. central vs. posterior) within-subjects design was adopted. Results from the analysis of N100 and N400 amplitudes showed that early learned words had an advantage in processing accuracy and speed; there is a tendency that the AoA effect was more pronounced for irregular word pairs and in the semantic related condition. More important, ERP results showed early acquired words induced larger N100 amplitudes for early AoA words in the parietal area and more negative-going N400 than late acquire words in the frontal and central regions. The results indicate the locus of the AoA effect might derive from the arbitrary mapping between word forms and semantic concepts, and early acquired words have more semantic interconnections than late acquired words. PMID:28572785

The picture superiority effect in categorization: visual or semantic?

PubMed

Job, R; Rumiati, R; Lotto, L

1992-09-01

Two experiments are reported whose aim was to replicate and generalize the results presented by Snodgrass and McCullough (1986) on the effect of visual similarity in the categorization process. For pictures, Snodgrass and McCullough's results were replicated because Ss took longer to discriminate elements from 2 categories when they were visually similar than when they were visually dissimilar. However, unlike Snodgrass and McCullough, an analogous increase was also observed for word stimuli. The pattern of results obtained here can be explained most parsimoniously with reference to the effect of semantic similarity, or semantic and visual relatedness, rather than to visual similarity alone.

Understanding Karma Police: The Perceived Plausibility of Noun Compounds as Predicted by Distributional Models of Semantic Representation

PubMed Central

Günther, Fritz; Marelli, Marco

2016-01-01

Noun compounds, consisting of two nouns (the head and the modifier) that are combined into a single concept, differ in terms of their plausibility: school bus is a more plausible compound than saddle olive. The present study investigates which factors influence the plausibility of attested and novel noun compounds. Distributional Semantic Models (DSMs) are used to obtain formal (vector) representations of word meanings, and compositional methods in DSMs are employed to obtain such representations for noun compounds. From these representations, different plausibility measures are computed. Three of those measures contribute in predicting the plausibility of noun compounds: The relatedness between the meaning of the head noun and the compound (Head Proximity), the relatedness between the meaning of modifier noun and the compound (Modifier Proximity), and the similarity between the head noun and the modifier noun (Constituent Similarity). We find non-linear interactions between Head Proximity and Modifier Proximity, as well as between Modifier Proximity and Constituent Similarity. Furthermore, Constituent Similarity interacts non-linearly with the familiarity with the compound. These results suggest that a compound is perceived as more plausible if it can be categorized as an instance of the category denoted by the head noun, if the contribution of the modifier to the compound meaning is clear but not redundant, and if the constituents are sufficiently similar in cases where this contribution is not clear. Furthermore, compounds are perceived to be more plausible if they are more familiar, but mostly for cases where the relation between the constituents is less clear. PMID:27732599

Effect of semantic coherence on episodic memory processes in schizophrenia.

PubMed

Battal Merlet, Lâle; Morel, Shasha; Blanchet, Alain; Lockman, Hazlin; Kostova, Milena

2014-12-30

Schizophrenia is associated with severe episodic retrieval impairment. The aim of this study was to investigate the possibility that schizophrenia patients could improve their familiarity and/or recollection processes by manipulating the semantic coherence of to-be-learned stimuli and using deep encoding. Twelve schizophrenia patients and 12 healthy controls of comparable age, gender, and educational level undertook an associative recognition memory task. The stimuli consisted of pairs of words that were either related or unrelated to a given semantic category. The process dissociation procedure was used to calculate the estimates of familiarity and recollection processes. Both groups showed enhanced memory performances for semantically related words. However, in healthy controls, semantic relatedness led to enhanced recollection, while in schizophrenia patients, it induced enhanced familiarity. The familiarity estimates for related words were comparable in both groups, indicating that familiarity could be used as a compensatory mechanism in schizophrenia patients. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The role of attention and relatedness in emotionally enhanced memory.

PubMed

Talmi, Deborah; Schimmack, Ulrich; Paterson, Theone; Moscovitch, Morris

2007-02-01

Examining the positive and negative pictures separately revealed that emotionally enhanced memory (EEM) for positive pictures was mediated by attention, with no significant influence of emotional arousal, whereas the reverse was true of negative pictures. Consistent with this finding, in Experiment 2 EEM for negative pictures was found even when task emphasis was manipulated so that equivalent attention was allocated to negative and neutral pictures. The results show that attention and semantic relatedness contribute to EEM, with the extent varying with emotional valence. Negative emotion can influence memory independently of these 2 factors. (c) 2007 APA, all rights reserved.

Beneficial effects of semantic memory support on older adults' episodic memory: Differential patterns of support of item and associative information.

PubMed

Mohanty, Praggyan Pam; Naveh-Benjamin, Moshe; Ratneshwar, Srinivasan

2016-02-01

The effects of two types of semantic memory support-meaningfulness of an item and relatedness between items-in mitigating age-related deficits in item and associative, memory are examined in a marketing context. In Experiment 1, participants studied less (vs. more) meaningful brand logo graphics (pictures) paired with meaningful brand names (words) and later were assessed by item (old/new) and associative (intact/recombined) memory recognition tests. Results showed that meaningfulness of items eliminated age deficits in item memory, while equivalently boosting associative memory for older and younger adults. Experiment 2, in which related and unrelated brand logo graphics and brand name pairs served as stimuli, revealed that relatedness between items eliminated age deficits in associative memory, while improving to the same degree item memory in older and younger adults. Experiment 2 also provided evidence for a probable boundary condition that could reconcile seemingly contradictory extant results. Overall, these experiments provided evidence that although the two types of semantic memory support can improve both item and associative memory in older and younger adults, older adults' memory deficits can be eliminated when the type of support provided is compatible with the type of information required to perform well on the test. (c) 2016 APA, all rights reserved).

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

PubMed

Wiese, Holger

2011-11-01

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

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

Somatotopic Semantic Priming and Prediction in the Motor System

PubMed Central

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

2016-01-01

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

Modulation of alpha oscillations is required for the suppression of semantic interference.

PubMed

Melnik, Natalia; Mapelli, Igor; Özkurt, Tolga Esat

2017-10-01

Recent findings on alpha band oscillations suggest their important role in memory consolidation and suppression of external distractors such as environmental noise. However, less attention was given to the phenomenon of internal distracting information being solely inherent to the stimuli content. Human memory may be prone to internal distractions caused by semantic relatedness between the meaning of words (e.g., atom, neutron, nucleus, etc.) to be encoded, i.e., semantic interference. Our study investigates the brain oscillatory dynamics behind the semantic interference phenomenon, whose possible outcome is known as false memories. In this direction, Deese-Roediger-McDermott word lists were appropriated for a modified Sternberg paradigm in auditory modality. Participants received semantically related and unrelated word lists via headphones while EEG data were acquired. Semantic interference triggered the false memory rates to be higher than those of other types of memory errors. Analysis demonstrated that the upper part of alpha band (∼10-12Hz) power decreases on parieto-occipital channels in the retention interval, prior to the probe item for semantically related condition. Our study elucidates the oscillatory mechanisms behind semantic interference by relying on alpha functional inhibition theory. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrating the automatic and the controlled: Strategies in Semantic Priming in an Attractor Network with Latching Dynamics

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2014-01-01

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we have introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. PMID:24890261

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

On the Form of Bilingual Grammars: The Phonological Component.

ERIC Educational Resources Information Center

Elerick, Charles

This research is based on the assumption that a Spanish/English bilingual is aware of the phonological and semantic relatedness of the many hundreds of pairs of transparently cognate items in the two languages. This awareness is linguistically significant in that it is reflected in the internalized grammar of the bilingual. The bilingual speaker…

Event-related fMRI studies of false memory: An Activation Likelihood Estimation meta-analysis.

PubMed

Kurkela, Kyle A; Dennis, Nancy A

2016-01-29

Over the last two decades, a wealth of research in the domain of episodic memory has focused on understanding the neural correlates mediating false memories, or memories for events that never happened. While several recent qualitative reviews have attempted to synthesize this literature, methodological differences amongst the empirical studies and a focus on only a sub-set of the findings has limited broader conclusions regarding the neural mechanisms underlying false memories. The current study performed a voxel-wise quantitative meta-analysis using activation likelihood estimation to investigate commonalities within the functional magnetic resonance imaging (fMRI) literature studying false memory. The results were broken down by memory phase (encoding, retrieval), as well as sub-analyses looking at differences in baseline (hit, correct rejection), memoranda (verbal, semantic), and experimental paradigm (e.g., semantic relatedness and perceptual relatedness) within retrieval. Concordance maps identified significant overlap across studies for each analysis. Several regions were identified in the general false retrieval analysis as well as multiple sub-analyses, indicating their ubiquitous, yet critical role in false retrieval (medial superior frontal gyrus, left precentral gyrus, left inferior parietal cortex). Additionally, several regions showed baseline- and paradigm-specific effects (hit/perceptual relatedness: inferior and middle occipital gyrus; CRs: bilateral inferior parietal cortex, precuneus, left caudate). With respect to encoding, analyses showed common activity in the left middle temporal gyrus and anterior cingulate cortex. No analysis identified a common cluster of activation in the medial temporal lobe. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effects of childhood hearing loss on organization of semantic memory: typicality and relatedness.

PubMed

Jerger, Susan; Damian, Markus F; Tye-Murray, Nancy; Dougherty, Meaghan; Mehta, Jyutika; Spence, Melanie

2006-12-01

The purpose of this research was to study how early childhood hearing loss affects development of concepts and categories, aspects of semantic knowledge that allow us to group and make inferences about objects with common properties, such as dogs versus cats. We assessed category typicality and out-of-category relatedness effects. The typicality effect refers to performance advantage (faster reaction times, fewer errors) for objects with a higher number of a category's characteristic properties; the out-of-category relatedness effect refers to performance disadvantage (slower reaction times and more errors) for out-of-category objects that share some properties with category members. We applied a new children's speeded category-verification task (vote "yes" if the pictured object is clothing). Stimuli were pictures of typical and atypical category objects (e.g., pants, glove) and related and unrelated out-of-category objects (e.g., necklace, soup). Participants were 30 children with hearing impairment (HI) who were considered successful hearing aid users and who attended regular classes (mainstreamed) with some support services. Ages ranged from 5 to 15 yr (mean = 10 yr 8 mo). Results were related to normative data from . Typical objects consistently showed preferential processing (faster reaction times, fewer errors), and related out-of-category objects consistently showed the converse. Overall, results between HI and normative groups exhibited striking similarity. Variation in speed of classification was influenced primarily by age and age-related competencies, such as vocabulary skill. Audiological status, however, independently influenced performance to a lesser extent, with positive responses becoming faster as degree of hearing loss decreased and negative responses becoming faster as age of identification/amplification/education decreased. There were few errors overall. The presence of a typicality effect indicates that 1) the structure of conceptual representations for at least one category in the HI group was based on characteristic properties with an uneven distribution among members, and 2) typical objects with a higher number of characteristic properties were more easily accessed and/or retrieved. The presence of a relatedness effect indicates that the structure of representational knowledge in the HI group allowed them to appreciate semantic properties and understand that properties may be shared between categories. Speculations linked the association 1) between positive responses and degree of hearing loss to an increase in the quality, accessibility, and retrievability of conceptual representations with better hearing; and 2) between negative responses and age of identification/amplification/education to an improvement in effortful, postretrieval decision-making proficiencies with more schooling and amplified auditory experience. This research establishes the value of our new approach to assessing the organization of semantic memory in children with HI.

Unconscious relational inference recruits the hippocampus.

PubMed

Reber, Thomas P; Luechinger, Roger; Boesiger, Peter; Henke, Katharina

2012-05-02

Relational inference denotes the capacity to encode, flexibly retrieve, and integrate multiple memories to combine past experiences to update knowledge and improve decision-making in new situations. Although relational inference is thought to depend on the hippocampus and consciousness, we now show in young, healthy men that it may occur outside consciousness but still recruits the hippocampus. In temporally distinct and unique subliminal episodes, we presented word pairs that either overlapped ("winter-red", "red-computer") or not. Effects of unconscious relational inference emerged in reaction times recorded during unconscious encoding and in the outcome of decisions made 1 min later at test, when participants judged the semantic relatedness of two supraliminal words. These words were either episodically related through a common word ("winter-computer" related through "red") or unrelated. Hippocampal activity increased during the unconscious encoding of overlapping versus nonoverlapping word pairs and during the unconscious retrieval of episodically related versus unrelated words. Furthermore, hippocampal activity during unconscious encoding predicted the outcome of decisions made at test. Hence, unconscious inference may influence decision-making in new situations.

Taxonomic and thematic organisation of proper name conceptual knowledge.

PubMed

Crutch, Sebastian J; Warrington, Elizabeth K

2011-01-01

We report the investigation of the organisation of proper names in two aphasic patients (NBC and FBI). The performance of both patients on spoken word to written word matching tasks was inconsistent, affected by presentation rate and semantic relatedness of the competing responses, all hallmarks of a refractory semantic access dysphasia. In a series of experiments we explored the semantic relatedness effects within their proper name vocabulary, including brand names and person names. First we demonstrated the interaction between very fine grain organisation and personal experience, with one patient with a special interest in the cinema demonstrating higher error rates when identifying the names of actors working in a similar film genre (e.g., action movies: Arnold Schwarzenegger, Bruce Willis, Sylvester Stallone, Mel Gibson) than those working in different genres (e.g., Arnold Schwarzenegger, Gregory Peck, Robin Williams, Gene Kelly). Second we compared directly two potential principles of semantic organisation - taxonomic and thematic. Furthermore we considered these principles of organisation in the context of the individuals' personal knowledge base. We selected topics matching the interests and experience of each patient, namely cinema and literature (NBC) and naval history (FBI). The stimulus items were arranged in taxonomic arrays (e.g., Jane Austen, Emily Bronte, Agatha Christie), thematic arrays (e.g., Jane Austen, Pride and Prejudice, Mr Darcy), and unrelated arrays (e.g., Jane Austen, Wuthering Heights, Hercule Poirot). We documented that different patterns of taxonomic and thematic organisation were constrained by whether the individual has limited knowledge, moderate knowledge or detailed knowledge of a particular vocabulary. It is suggested that moderate proper name knowledge is primarily organised by taxonomy whereas extensive experience results in a more detailed knowledge base in which theme is a powerful organising principle.

Taxonomic and Thematic Organisation of Proper Name Conceptual Knowledge

PubMed Central

Crutch, Sebastian J.; Warrington, Elizabeth K.

2011-01-01

We report the investigation of the organisation of proper names in two aphasic patients (NBC and FBI). The performance of both patients on spoken word to written word matching tasks was inconsistent, affected by presentation rate and semantic relatedness of the competing responses, all hallmarks of a refractory semantic access dysphasia. In a series of experiments we explored the semantic relatedness effects within their proper name vocabulary, including brand names and person names. First we demonstrated the interaction between very fine grain organisation and personal experience, with one patient with a special interest in the cinema demonstrating higher error rates when identifying the names of actors working in a similar film genre (e.g. action movies: Arnold Schwarzenegger, Bruce Willis, Sylvester Stallone, Mel Gibson) than those working in different genres (e.g. Arnold Schwarzenegger, Gregory Peck, Robin Williams, Gene Kelly). Second we compared directly two potential principles of semantic organisation – taxonomic and thematic. Furthermore we considered these principles of organisation in the context of the individuals' personal knowledge base. We selected topics matching the interests and experience of each patient, namely cinema and literature (NBC) and naval history (FBI). The stimulus items were arranged in taxonomic arrays (e.g. Jane Austen, Emily Bronte, Agatha Christie), thematic arrays (e.g. Jane Austen, Pride and Prejudice, Mr Darcy), and unrelated arrays (e.g. Jane Austen, Wuthering Heights, Hercule Poirot). We documented that different patterns of taxonomic and thematic organisation were constrained by whether the individual has limited knowledge, moderate knowledge or detailed knowledge of a particular vocabulary. It is suggested that moderate proper name knowledge is primarily organised by taxonomy whereas extensive experience results in a more detailed knowledge base in which theme is a powerful organising principle. PMID:22063815

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.

Standards of Coherence in Second Language Reading: Sentence Connectivity and Reading Proficiency

ERIC Educational Resources Information Center

Nahatame, Shingo

2017-01-01

Standards of coherence are one of the major factors that influence reading comprehension. This study investigated the standards of coherence that second language (L2) learners employ when reading. In a pair of experiments, Japanese learners of English read two-sentence texts with varying causal and semantic relatedness between sentences and then…

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.

Face (and Nose) Priming for Book: The Malleability of Semantic Memory

PubMed Central

Coane, Jennifer H.; Balota, David A.

2010-01-01

There are two general classes of models of semantic structure that support semantic priming effects. Feature-overlap models of semantic priming assume that shared features between primes and targets are critical (e.g., cat-DOG). Associative accounts assume that contextual co-occurrence is critical and that the system is organized along associations independent of featural overlap (e.g., leash-DOG). If unrelated concepts can become related as a result of contextual co-occurrence, this would be more supportive of associative accounts and provide insight into the nature of the network underlying “semantic” priming effects. Naturally co-occurring recent associations (e.g., face-BOOK) were tested under conditions that minimize strategic influences (i.e., short stimulus onset asynchrony, low relatedness proportion) in a semantic priming paradigm. Priming for new associations did not differ from the priming found for pre-existing relations (e.g., library-BOOK). Mediated priming (e.g., nose-BOOK) was also found. These results suggest that contextual associations can result in the reorganization of the network that subserves “semantic” priming effects. PMID:20494866

Phasic affective modulation of semantic priming.

PubMed

Topolinski, Sascha; Deutsch, Roland

2013-03-01

The present research demonstrates that very brief variations in affect, being around 1 s in length and changing from trial to trial independently from semantic relatedness of primes and targets, modulate the amount of semantic priming. Implementing consonant and dissonant chords (Experiments 1 and 5), naturalistic sounds (Experiment 2), and visual facial primes (Experiment 3) in an (in)direct semantic priming paradigm, as well as brief facial feedback in a summative priming paradigm (Experiment 4), yielded increased priming effects under brief positive compared to negative affect. Furthermore, this modulation took place on the level of semantic spreading rather than on strategic mechanisms (Experiment 5). Alternative explanations such as distraction, motivation, arousal, and cognitive tuning could be ruled out. This phasic affective modulation constitutes a mechanism overlooked thus far that may contaminate priming effects in all priming paradigms that involve affective stimuli. Furthermore, this mechanism provides a novel explanation for the observation that priming effects are usually larger for positive than for negative stimuli. Finally, it has important implications for linguistic research, by suggesting that association norms may be biased for affective words. (c) 2013 APA, all rights reserved.

Perceptual Simulations and Linguistic Representations Have Differential Effects on Speeded Relatedness Judgments and Recognition Memory

PubMed Central

Tse, Chi-Shing; Kurby, Christopher A.; Du, Feng

2010-01-01

We examined the effect of spatial iconicity (a perceptual simulation of canonical locations of objects) and word-order frequency on language processing and episodic memory of orientation. Participants made speeded relatedness judgments to pairs of words presented in locations typical to their real world arrangements (e.g., ceiling on top and floor on bottom). They then engaged in a surprise orientation recognition task for the word pairs. We replicated Louwerse’s finding (2008) that word-order frequency has a stronger effect on semantic relatedness judgments than spatial iconicity. This is consistent with recent suggestions that linguistic representations have a stronger impact on immediate decisions about verbal materials than perceptual simulations. In contrast, spatial iconicity enhanced episodic memory of orientation to a greater extent than word-order frequency did. This new finding indicates that perceptual simulations have an important role in episodic memory. Results are discussed with respect to theories of perceptual representation and linguistic processing. PMID:19742388

Effects of perceptual and semantic cues on ERP modulations associated with prospective memory.

PubMed

Cousens, Ross; Cutmore, Timothy; Wang, Ya; Wilson, Jennifer; Chan, Raymond C K; Shum, David H K

2015-10-01

Prospective memory involves the formation and execution of intended actions and is essential for autonomous living. In this study (N=32), the effect of the nature of PM cues (semantic versus perceptual) on established event-related potentials (ERPs) elicited in PM tasks (N300 and prospective positivity) was investigated. PM cues defined by their perceptual features clearly elicited the N300 and prospective positivity whereas PM cues defined by semantic relatedness elicited prospective positivity. This calls into question the view that the N300 is a marker of general processes underlying detection of PM cues, but supports existing research showing that prospective positivity represents general post-retrieval processes that follow detection of PM cues. Continued refinement of ERP paradigms for understanding the neural correlates of PM is needed. Copyright © 2015 Elsevier B.V. All rights reserved.

The (un)reliability of item-level semantic priming effects.

PubMed

Heyman, Tom; Bruninx, Anke; Hutchison, Keith A; Storms, Gert

2018-04-05

Many researchers have tried to predict semantic priming effects using a myriad of variables (e.g., prime-target associative strength or co-occurrence frequency). The idea is that relatedness varies across prime-target pairs, which should be reflected in the size of the priming effect (e.g., cat should prime dog more than animal does). However, it is only insightful to predict item-level priming effects if they can be measured reliably. Thus, in the present study we examined the split-half and test-retest reliabilities of item-level priming effects under conditions that should discourage the use of strategies. The resulting priming effects proved extremely unreliable, and reanalyses of three published priming datasets revealed similar cases of low reliability. These results imply that previous attempts to predict semantic priming were unlikely to be successful. However, one study with an unusually large sample size yielded more favorable reliability estimates, suggesting that big data, in terms of items and participants, should be the future for semantic priming research.

Unconscious Congruency Priming from Unpracticed Words Is Modulated by Prime-Target Semantic Relatedness

ERIC Educational Resources Information Center

Ortells, Juan J.; Mari-Beffa, Paloma; Plaza-Ayllon, Vanesa

2013-01-01

Participants performed a 2-choice categorization task on visible word targets that were preceded by novel (unpracticed) prime words. The prime words were presented for 33 ms and followed either immediately (Experiments 1-3) or after a variable delay (Experiments 1 and 4) by a pattern mask. Both subjective and objective measures of prime visibility…

Semantic similarity between old and new items produces false alarms in recognition memory.

PubMed

Montefinese, Maria; Zannino, Gian Daniele; Ambrosini, Ettore

2015-09-01

In everyday life, human beings can report memories of past events that did not occur or that occurred differently from the way they remember them because memory is an imperfect process of reconstruction and is prone to distortion and errors. In this recognition study using word stimuli, we investigated whether a specific operationalization of semantic similarity among concepts can modulate false memories while controlling for the possible effect of associative strength and word co-occurrence in an old-new recognition task. The semantic similarity value of each new concept was calculated as the mean cosine similarity between pairs of vectors representing that new concept and each old concept belonging to the same semantic category. Results showed that, compared with (new) low-similarity concepts, (new) high-similarity concepts had significantly higher probability of being falsely recognized as old, even after partialling out the effect of confounding variables, including associative relatedness and lexical co-occurrence. This finding supports the feature-based view of semantic memory, suggesting that meaning overlap and sharing of semantic features (which are greater when more similar semantic concepts are being processed) have an influence on recognition performance, resulting in more false alarms for new high-similarity concepts. We propose that the associative strength and word co-occurrence among concepts are not sufficient to explain illusory memories but is important to take into account also the effects of feature-based semantic relations, and, in particular, the semantic similarity among concepts.

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.

Auditory conflict and congruence in frontotemporal dementia.

PubMed

Clark, Camilla N; Nicholas, Jennifer M; Agustus, Jennifer L; Hardy, Christopher J D; Russell, Lucy L; Brotherhood, Emilie V; Dick, Katrina M; Marshall, Charles R; Mummery, Catherine J; Rohrer, Jonathan D; Warren, Jason D

2017-09-01

Impaired analysis of signal conflict and congruence may contribute to diverse socio-emotional symptoms in frontotemporal dementias, however the underlying mechanisms have not been defined. Here we addressed this issue in patients with behavioural variant frontotemporal dementia (bvFTD; n = 19) and semantic dementia (SD; n = 10) relative to healthy older individuals (n = 20). We created auditory scenes in which semantic and emotional congruity of constituent sounds were independently probed; associated tasks controlled for auditory perceptual similarity, scene parsing and semantic competence. Neuroanatomical correlates of auditory congruity processing were assessed using voxel-based morphometry. Relative to healthy controls, both the bvFTD and SD groups had impaired semantic and emotional congruity processing (after taking auditory control task performance into account) and reduced affective integration of sounds into scenes. Grey matter correlates of auditory semantic congruity processing were identified in distributed regions encompassing prefrontal, parieto-temporal and insular areas and correlates of auditory emotional congruity in partly overlapping temporal, insular and striatal regions. Our findings suggest that decoding of auditory signal relatedness may probe a generic cognitive mechanism and neural architecture underpinning frontotemporal dementia syndromes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Memory as discrimination: what distraction reveals.

PubMed

Beaman, C Philip; Hanczakowski, Maciej; Hodgetts, Helen M; Marsh, John E; Jones, Dylan M

2013-11-01

Recalling information involves the process of discriminating between relevant and irrelevant information stored in memory. Not infrequently, the relevant information needs to be selected from among a series of related possibilities. This is likely to be particularly problematic when the irrelevant possibilities not only are temporally or contextually appropriate, but also overlap semantically with the target or targets. Here, we investigate the extent to which purely perceptual features that discriminate between irrelevant and target material can be used to overcome the negative impact of contextual and semantic relatedness. Adopting a distraction paradigm, it is demonstrated that when distractors are interleaved with targets presented either visually (Experiment 1) or auditorily (Experiment 2), a within-modality semantic distraction effect occurs; semantically related distractors impact upon recall more than do unrelated distractors. In the semantically related condition, the number of intrusions in recall is reduced, while the number of correctly recalled targets is simultaneously increased by the presence of perceptual cues to relevance (color features in Experiment 1 or speaker's gender in Experiment 2). However, as is demonstrated in Experiment 3, even presenting semantically related distractors in a language and a sensory modality (spoken Welsh) distinct from that of the targets (visual English) is insufficient to eliminate false recalls completely or to restore correct recall to levels seen with unrelated distractors . Together, the study shows how semantic and nonsemantic discriminability shape patterns of both erroneous and correct recall.

Dissociation between arithmetic relatedness and distance effects is modulated by task properties: an ERP study comparing explicit vs. implicit arithmetic processing.

PubMed

Avancini, Chiara; Galfano, Giovanni; Szűcs, Dénes

2014-12-01

Event-related potential (ERP) studies have detected several characteristic consecutive amplitude modulations in both implicit and explicit mental arithmetic tasks. Implicit tasks typically focused on the arithmetic relatedness effect (in which performance is affected by semantic associations between numbers) while explicit tasks focused on the distance effect (in which performance is affected by the numerical difference of to-be-compared numbers). Both task types elicit morphologically similar ERP waves which were explained in functionally similar terms. However, to date, the relationship between these tasks has not been investigated explicitly and systematically. In order to fill this gap, here we examined whether ERP effects and their underlying cognitive processes in implicit and explicit mental arithmetic tasks differ from each other. The same group of participants performed both an implicit number-matching task (in which arithmetic knowledge is task-irrelevant) and an explicit arithmetic-verification task (in which arithmetic knowledge is task-relevant). 129-channel ERP data differed substantially between tasks. In the number-matching task, the arithmetic relatedness effect appeared as a negativity over left-frontal electrodes whereas the distance effect was more prominent over right centro-parietal electrodes. In the verification task, all probe types elicited similar N2b waves over right fronto-central electrodes and typical centro-parietal N400 effects over central electrodes. The distance effect appeared as an early-rising, long-lasting left parietal negativity. We suggest that ERP effects in the implicit task reflect access to semantic memory networks and to magnitude discrimination, respectively. In contrast, effects of expectation violation are more prominent in explicit tasks and may mask more delicate cognitive processes. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Dissociation between arithmetic relatedness and distance effects is modulated by task properties: An ERP study comparing explicit vs. implicit arithmetic processing

PubMed Central

Avancini, Chiara; Galfano, Giovanni; Szűcs, Dénes

2014-01-01

Event-related potential (ERP) studies have detected several characteristic consecutive amplitude modulations in both implicit and explicit mental arithmetic tasks. Implicit tasks typically focused on the arithmetic relatedness effect (in which performance is affected by semantic associations between numbers) while explicit tasks focused on the distance effect (in which performance is affected by the numerical difference of to-be-compared numbers). Both task types elicit morphologically similar ERP waves which were explained in functionally similar terms. However, to date, the relationship between these tasks has not been investigated explicitly and systematically. In order to fill this gap, here we examined whether ERP effects and their underlying cognitive processes in implicit and explicit mental arithmetic tasks differ from each other. The same group of participants performed both an implicit number-matching task (in which arithmetic knowledge is task-irrelevant) and an explicit arithmetic-verification task (in which arithmetic knowledge is task-relevant). 129-channel ERP data differed substantially between tasks. In the number-matching task, the arithmetic relatedness effect appeared as a negativity over left-frontal electrodes whereas the distance effect was more prominent over right centro-parietal electrodes. In the verification task, all probe types elicited similar N2b waves over right fronto-central electrodes and typical centro-parietal N400 effects over central electrodes. The distance effect appeared as an early-rising, long-lasting left parietal negativity. We suggest that ERP effects in the implicit task reflect access to semantic memory networks and to magnitude discrimination, respectively. In contrast, effects of expectation violation are more prominent in explicit tasks and may mask more delicate cognitive processes. PMID:25450162

Semantic Neighborhood Effects for Abstract versus Concrete Words

PubMed Central

Danguecan, Ashley N.; Buchanan, Lori

2016-01-01

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

Semantic Neighborhood Effects for Abstract versus Concrete Words.

PubMed

Danguecan, Ashley N; Buchanan, Lori

2016-01-01

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

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…

Multiple priming of lexically ambiguous and unambiguous targets in the cerebral hemispheres: the coarse coding hypothesis revisited

PubMed Central

Kandhadai, Padmapriya; Federmeier, Kara D.

2009-01-01

The coarse coding hypothesis (Jung-Beeman 2005) postulates that the cerebral hemispheres differ in their breadth of semantic activation, with the left hemisphere (LH) activating a narrow, focused semantic field and the right (RH) weakly activating a broader semantic field. In support of coarse coding, studies (e.g., Faust and Lavidor 2003) investigating priming for multiple senses of a lexically ambiguous word have reported a RH benefit. However, studies of mediated priming (Livesay and Burgess 2003; Richards and Chiarello 1995) have failed to find a RH advantage for processing distantly-linked, unambiguous words. To address this debate, the present study made use of a multiple priming paradigm (Balota and Paul, 1996) in which two primes either converged onto the single meaning of an unambiguous, lexically-associated target (LION-STRIPES-TIGER) or diverged onto different meanings of an ambiguous target (KIDNEY-PIANO-ORGAN). In two experiments, participants either made lexical decisions to targets (Experiment 1) or made a semantic relatedness judgment between primes and targets (Experiment 2). In both tasks, for both ambiguous and unambiguous triplets we found equivalent priming strengths and patterns across the two visual fields, counter to the predictions of the coarse coding hypothesis. Priming patterns further suggested that both hemispheres made use of lexical level representations in the lexical decision task and semantic representations in the semantic judgment task. PMID:17459344

Semantic relations differentially impact associative recognition memory: electrophysiological evidence.

PubMed

Kriukova, Olga; Bridger, Emma; Mecklinger, Axel

2013-10-01

Though associative recognition memory is thought to rely primarily on recollection, recent research indicates that familiarity might also make a substantial contribution when to-be-learned items are integrated into a coherent structure by means of an existing semantic relation. It remains unclear how different types of semantic relations, such as categorical (e.g., dancer-singer) and thematic (e.g., dancer-stage) relations might affect associative recognition, however. Using event-related potentials (ERPs), we addressed this question by manipulating the type of semantic link between paired words in an associative recognition memory experiment. An early midfrontal old/new effect, typically linked to familiarity, was observed across the relation types. In contrast, a robust left parietal old/new effect was found in the categorical condition only, suggesting a clear contribution of recollection to associative recognition for this kind of pairs. One interpretation of this pattern is that familiarity was sufficiently diagnostic for associative recognition of thematic relations, which could result from the integrative nature of the thematic relatedness compared to the similarity-based nature of categorical pairs. The present study suggests that the extent to which recollection and familiarity are involved in associative recognition is at least in part determined by the properties of semantic relations between the paired associates. Copyright © 2013 Elsevier Inc. All rights reserved.

Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.

PubMed

Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan; Korhonen, Anna

2018-02-05

Word representations support a variety of Natural Language Processing (NLP) tasks. The quality of these representations is typically assessed by comparing the distances in the induced vector spaces against human similarity judgements. Whereas comprehensive evaluation resources have recently been developed for the general domain, similar resources for biomedicine currently suffer from the lack of coverage, both in terms of word types included and with respect to the semantic distinctions. Notably, verbs have been excluded, although they are essential for the interpretation of biomedical language. Further, current resources do not discern between semantic similarity and semantic relatedness, although this has been proven as an important predictor of the usefulness of word representations and their performance in downstream applications. We present two novel comprehensive resources targeting the evaluation of word representations in biomedicine. These resources, Bio-SimVerb and Bio-SimLex, address the previously mentioned problems, and can be used for evaluations of verb and noun representations respectively. In our experiments, we have computed the Pearson's correlation between performances on intrinsic and extrinsic tasks using twelve popular state-of-the-art representation models (e.g. word2vec models). The intrinsic-extrinsic correlations using our datasets are notably higher than with previous intrinsic evaluation benchmarks such as UMNSRS and MayoSRS. In addition, when evaluating representation models for their abilities to capture verb and noun semantics individually, we show a considerable variation between performances across all models. Bio-SimVerb and Bio-SimLex enable intrinsic evaluation of word representations. This evaluation can serve as a predictor of performance on various downstream tasks in the biomedical domain. The results on Bio-SimVerb and Bio-SimLex using standard word representation models highlight the importance of developing dedicated evaluation resources for NLP in biomedicine for particular word classes (e.g. verbs). These are needed to identify the most accurate methods for learning class-specific representations. Bio-SimVerb and Bio-SimLex are publicly available.

Rapid L2 Word Learning through High Constraint Sentence Context: An Event-Related Potential Study

PubMed Central

Chen, Baoguo; Ma, Tengfei; Liang, Lijuan; Liu, Huanhuan

2017-01-01

Previous studies have found quantity of exposure, i.e., frequency of exposure (Horst et al., 1998; Webb, 2008; Pellicer-Sánchez and Schmitt, 2010), is important for second language (L2) contextual word learning. Besides this factor, context constraint and L2 proficiency level have also been found to affect contextual word learning (Pulido, 2003; Tekmen and Daloglu, 2006; Elgort et al., 2015; Ma et al., 2015). In the present study, we adopted the event-related potential (ERP) technique and chose high constraint sentences as reading materials to further explore the effects of quantity of exposure and proficiency on L2 contextual word learning. Participants were Chinese learners of English with different English proficiency levels. For each novel word, there were four high constraint sentences with the critical word at the end of the sentence. Learners read sentences and made semantic relatedness judgment afterwards, with ERPs recorded. Results showed that in the high constraint condition where each pseudoword was embedded in four sentences with consistent meaning, N400 amplitude upon this pseudoword decreased significantly as learners read the first two sentences. High proficiency learners responded faster in the semantic relatedness judgment task. These results suggest that in high quality sentence contexts, L2 learners could rapidly acquire word meaning without multiple exposures, and L2 proficiency facilitated this learning process. PMID:29375420

Rapid L2 Word Learning through High Constraint Sentence Context: An Event-Related Potential Study.

PubMed

Chen, Baoguo; Ma, Tengfei; Liang, Lijuan; Liu, Huanhuan

2017-01-01

Previous studies have found quantity of exposure, i.e., frequency of exposure (Horst et al., 1998; Webb, 2008; Pellicer-Sánchez and Schmitt, 2010), is important for second language (L2) contextual word learning. Besides this factor, context constraint and L2 proficiency level have also been found to affect contextual word learning (Pulido, 2003; Tekmen and Daloglu, 2006; Elgort et al., 2015; Ma et al., 2015). In the present study, we adopted the event-related potential (ERP) technique and chose high constraint sentences as reading materials to further explore the effects of quantity of exposure and proficiency on L2 contextual word learning. Participants were Chinese learners of English with different English proficiency levels. For each novel word, there were four high constraint sentences with the critical word at the end of the sentence. Learners read sentences and made semantic relatedness judgment afterwards, with ERPs recorded. Results showed that in the high constraint condition where each pseudoword was embedded in four sentences with consistent meaning, N400 amplitude upon this pseudoword decreased significantly as learners read the first two sentences. High proficiency learners responded faster in the semantic relatedness judgment task. These results suggest that in high quality sentence contexts, L2 learners could rapidly acquire word meaning without multiple exposures, and L2 proficiency facilitated this learning process.

Enhancing clinical concept extraction with distributional semantics

PubMed Central

Cohen, Trevor; Wu, Stephen; Gonzalez, Graciela

2011-01-01

Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged f-measure for exact match increased from 80.3% to 82.3% and the micro-averaged f-measure based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data. PMID:22085698

Contrasting Semantic versus Inhibitory Processing in the Angular Gyrus: An fMRI Study.

PubMed

Lewis, Gwyneth A; Poeppel, David; Murphy, Gregory L

2018-06-06

Recent studies of semantic memory have focused on dissociating the neural bases of two foundational components of human thought: taxonomic categories, which group similar objects like dogs and seals based on features, and thematic categories, which group dissimilar objects like dogs and leashes based on events. While there is emerging consensus that taxonomic concepts are represented in the anterior temporal lobe, there is disagreement over whether thematic concepts are represented in the angular gyrus (AG). We previously found AG sensitivity to both kinds of concepts; however, some accounts suggest that such activity reflects inhibition of irrelevant information rather than thematic activation. To test these possibilities, an fMRI experiment investigated both types of conceptual relations in the AG during two semantic judgment tasks. Each task trained participants to give negative responses (inhibition) or positive responses (activation) to word pairs based on taxonomic and thematic criteria of relatedness. Results showed AG engagement during both negative judgments and thematic judgments, but not during positive judgments about taxonomic pairs. Together, the results suggest that activity in the AG reflects functions that include both thematic (but not taxonomic) processing and inhibiting irrelevant semantic information.

Establishing causal coherence across sentences: an ERP study

PubMed Central

Kuperberg, Gina R.; Paczynski, Martin; Ditman, Tali

2011-01-01

This study examined neural activity associated with establishing causal relationships across sentences during online comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the Late Positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched. PMID:20175676

Qualitative features of semantic fluency performance in mesial and lateral frontal patients.

PubMed

Reverberi, Carlo; Laiacona, Marcella; Capitani, Erminio

2006-01-01

Semantic verbal fluency is widely used in clinical and experimental studies. This task is highly sensitive to the presence of brain pathology and is frequently impaired after frontal lesions. Besides the total number of words generated, a qualitative analysis of their sequence can add valuable information about the impaired cognitive components. Thirty-four frontal patients and a group of matched controls were examined. Besides the number of words and subcategories retrieved by each group, we analysed two distinct aspects of the word sequence: the search strategy through a semantically organised store and the ability to switch from one subcategory to another. We checked whether the pattern of impairment changed according to the lesion site within the frontal lobe. Overall, patients produced fewer words than controls. However, only lateral frontal patients presented a reduced semantic relatedness between contiguously produced words and a specifically increased proportion of switches to different subcategories. The performance of lateral frontal patients was in line with the hypothesis of a search strategy impairment and cannot be attributed to a switching deficit. The performance of mesial frontal patients could be ascribed to a general deficit of activation.

Iconic gestures prime related concepts: an ERP study.

PubMed

Wu, Ying Croon; Coulson, Seana

2007-02-01

To assess priming by iconic gestures, we recorded EEG (at 29 scalp sites) in two experiments while adults watched short, soundless videos of spontaneously produced, cospeech iconic gestures followed by related or unrelated probe words. In Experiment 1, participants classified the relatedness between gestures and words. In Experiment 2, they attended to stimuli, and performed an incidental recognition memory test on words presented during the EEG recording session. Event-related potentials (ERPs) time-locked to the onset of probe words were measured, along with response latencies and word recognition rates. Although word relatedness did not affect reaction times or recognition rates, contextually related probe words elicited less-negative ERPs than did unrelated ones between 300 and 500 msec after stimulus onset (N400) in both experiments. These findings demonstrate sensitivity to semantic relations between iconic gestures and words in brain activity engendered during word comprehension.

The architecture of human kin detection

PubMed Central

Lieberman, Debra; Tooby, John; Cosmides, Leda

2012-01-01

Evolved mechanisms for assessing genetic relatedness have been found in many species, but their existence in humans has been a matter of controversy. Here we report three converging lines of evidence, drawn from siblings, that support the hypothesis that kin detection mechanisms exist in humans. These operate by computing, for each familiar individual, a unitary regulatory variable (the kinship index) that corresponds to a pairwise estimate of genetic relatedness between self and other. The cues that the system uses were identified by quantitatively matching individual exposure to potential cues of relatedness to variation in three outputs relevant to the system’s evolved functions: sibling altruism, aversion to personally engaging in sibling incest, and moral opposition to third party sibling incest. As predicted, the kin detection system uses two distinct, ancestrally valid cues to compute relatedness: the familiar other’s perinatal association with the individual’s biological mother, and duration of sibling coresidence. PMID:17301784

The role of action representations in thematic object relations

PubMed Central

Tsagkaridis, Konstantinos; Watson, Christine E.; Jax, Steven A.; Buxbaum, Laurel J.

2014-01-01

A number of studies have explored the role of associative/event-based (thematic) and categorical (taxonomic) relations in the organization of object representations. Recent evidence suggests that thematic information may be particularly important in determining relationships between manipulable artifacts. However, although sensorimotor information is on many accounts an important component of manipulable artifact representations, little is known about the role that action may play during the processing of semantic relationships (particularly thematic relationships) between multiple objects. In this study, we assessed healthy and left hemisphere stroke participants to explore three questions relevant to object relationship processing. First, we assessed whether participants tended to favor thematic relations including action (Th+A, e.g., wine bottle—corkscrew), thematic relationships without action (Th-A, e.g., wine bottle—cheese), or taxonomic relationships (Tax, e.g., wine bottle—water bottle) when choosing between them in an association judgment task with manipulable artifacts. Second, we assessed whether the underlying constructs of event relatedness, action relatedness, and categorical relatedness determined the choices that participants made. Third, we assessed the hypothesis that degraded action knowledge and/or damage to temporo-parietal cortex, a region of the brain associated with the representation of action knowledge, would reduce the influence of action on the choice task. Experiment 1 showed that explicit ratings of event, action, and categorical relatedness were differentially predictive of healthy participants' choices, with action relatedness determining choices between Th+A and Th-A associations above and beyond event and categorical ratings. Experiment 2 focused more specifically on these Th+A vs. Th-A choices and demonstrated that participants with left temporo-parietal lesions, a brain region known to be involved in sensorimotor processing, were less likely than controls and tended to be less likely than patients with lesions sparing that region to use action relatedness in determining their choices. These data indicate that action knowledge plays a critical role in processing of thematic relations for manipulable artifacts. PMID:24672461

The role of action representations in thematic object relations.

PubMed

Tsagkaridis, Konstantinos; Watson, Christine E; Jax, Steven A; Buxbaum, Laurel J

2014-01-01

A number of studies have explored the role of associative/event-based (thematic) and categorical (taxonomic) relations in the organization of object representations. Recent evidence suggests that thematic information may be particularly important in determining relationships between manipulable artifacts. However, although sensorimotor information is on many accounts an important component of manipulable artifact representations, little is known about the role that action may play during the processing of semantic relationships (particularly thematic relationships) between multiple objects. In this study, we assessed healthy and left hemisphere stroke participants to explore three questions relevant to object relationship processing. First, we assessed whether participants tended to favor thematic relations including action (Th+A, e.g., wine bottle-corkscrew), thematic relationships without action (Th-A, e.g., wine bottle-cheese), or taxonomic relationships (Tax, e.g., wine bottle-water bottle) when choosing between them in an association judgment task with manipulable artifacts. Second, we assessed whether the underlying constructs of event relatedness, action relatedness, and categorical relatedness determined the choices that participants made. Third, we assessed the hypothesis that degraded action knowledge and/or damage to temporo-parietal cortex, a region of the brain associated with the representation of action knowledge, would reduce the influence of action on the choice task. Experiment 1 showed that explicit ratings of event, action, and categorical relatedness were differentially predictive of healthy participants' choices, with action relatedness determining choices between Th+A and Th-A associations above and beyond event and categorical ratings. Experiment 2 focused more specifically on these Th+A vs. Th-A choices and demonstrated that participants with left temporo-parietal lesions, a brain region known to be involved in sensorimotor processing, were less likely than controls and tended to be less likely than patients with lesions sparing that region to use action relatedness in determining their choices. These data indicate that action knowledge plays a critical role in processing of thematic relations for manipulable artifacts.

Semantic Similarity in Biomedical Ontologies

PubMed Central

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

2009-01-01

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

Towards an Approach of Semantic Access Control for Cloud Computing

NASA Astrophysics Data System (ADS)

Hu, Luokai; Ying, Shi; Jia, Xiangyang; Zhao, Kai

With the development of cloud computing, the mutual understandability among distributed Access Control Policies (ACPs) has become an important issue in the security field of cloud computing. Semantic Web technology provides the solution to semantic interoperability of heterogeneous applications. In this paper, we analysis existing access control methods and present a new Semantic Access Control Policy Language (SACPL) for describing ACPs in cloud computing environment. Access Control Oriented Ontology System (ACOOS) is designed as the semantic basis of SACPL. Ontology-based SACPL language can effectively solve the interoperability issue of distributed ACPs. This study enriches the research that the semantic web technology is applied in the field of security, and provides a new way of thinking of access control in cloud computing.

The Separability of Morphological Processes from Semantic Meaning and Syntactic Class in Production of Single Words: Evidence from the Hebrew Root Morpheme.

PubMed

Deutsch, Avital

2016-02-01

In the present study we investigated to what extent the morphological facilitation effect induced by the derivational root morpheme in Hebrew is independent of semantic meaning and grammatical information of the part of speech involved. Using the picture-word interference paradigm with auditorily presented distractors, Experiment 1 compared the facilitation effect induced by semantically transparent versus semantically opaque morphologically related distractor words (i.e., a shared root) on the production latency of bare nouns. The results revealed almost the same amount of facilitation for both relatedness conditions. These findings accord with the results of the few studies that have addressed this issue in production in Indo-European languages, as well as previous studies in written word perception in Hebrew. Experiment 2 compared the root's facilitation effect, induced by morphologically related nominal versus verbal distractors, on the production latency of bare nouns. The results revealed a facilitation effect of similar size induced by the shared root, regardless of the distractor's part of speech. It is suggested that the principle that governs lexical organization at the level of morphology, at least for Hebrew roots, is form-driven and independent of semantic meaning. This principle of organization crosses the linguistic domains of production and written word perception, as well as grammatical organization according to part of speech.

The role of orthography in the semantic activation of neighbors.

PubMed

Hino, Yasushi; Lupker, Stephen J; Taylor, Tamsen E

2012-09-01

There is now considerable evidence that a letter string can activate semantic information appropriate to its orthographic neighbors (e.g., Forster & Hector's, 2002, TURPLE effect). This phenomenon is the focus of the present research. Using Japanese words, we examined whether semantic activation of neighbors is driven directly by orthographic similarity alone or whether there is also a role for phonological similarity. In Experiment 1, using a relatedness judgment task in which a Kanji word-Katakana word pair was presented on each trial, an inhibitory effect was observed when the initial Kanji word was related to an orthographic and phonological neighbor of the Katakana word target but not when the initial Kanji word was related to a phonological but not orthographic neighbor of the Katakana word target. This result suggests that phonology plays little, if any, role in the activation of neighbors' semantics when reading familiar words. In Experiment 2, the targets were transcribed into Hiragana, a script they are typically not written in, requiring readers to engage in phonological coding. In that experiment, inhibitory effects were observed in both conditions. This result indicates that phonologically mediated semantic activation of neighbors will emerge when phonological processing is necessary in order to understand a written word (e.g., when that word is transcribed into an unfamiliar script). PsycINFO Database Record (c) 2012 APA, all rights reserved.

Semantic and Syntactic Associations During Word Search Modulate the Relationship Between Attention and Subsequent Memory.

PubMed

Zhou, Wei; Mo, Fei; Zhang, Yunhong; Ding, Jinhong

2017-01-01

Two experiments were conducted to investigate how linguistic information influences attention allocation in visual search and memory for words. In Experiment 1, participants searched for the synonym of a cue word among five words. The distractors included one antonym and three unrelated words. In Experiment 2, participants were asked to judge whether the five words presented on the screen comprise a valid sentence. The relationships among words were sentential, semantically related or unrelated. A memory recognition task followed. Results in both experiments showed that linguistically related words produced better memory performance. We also found that there were significant interactions between linguistic relation conditions and memorization on eye-movement measures, indicating that good memory for words relied on frequent and long fixations during search in the unrelated condition but to a much lesser extent in linguistically related conditions. We conclude that semantic and syntactic associations attenuate the link between overt attention allocation and subsequent memory performance, suggesting that linguistic relatedness can somewhat compensate for a relative lack of attention during word search.

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

DOEpatents

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

2015-02-03

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

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

PubMed

Yeari, Menahem; van den Broek, Paul

2016-09-01

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

Objects Mediate Goal Integration in Ventrolateral Prefrontal Cortex during Action Observation

PubMed Central

Hrkać, Mari; Wurm, Moritz F.; Kühn, Anne B.; Schubotz, Ricarda I.

2015-01-01

Actions performed by others are mostly not observed in isolation, but embedded in sequences of actions tied together by an overarching goal. Therefore, preceding actions can modulate the observer's expectations in relation to the currently perceived action. Ventrolateral prefrontal cortex (vlPFC), and inferior frontal gyrus (IFG) in particular, is suggested to subserve the integration of episodic as well as semantic information and memory, including action scripts. The present fMRI study investigated if activation in IFG varies with the effort to integrate expected and observed action, even when not required by the task. During an fMRI session, participants were instructed to attend to short videos of single actions and to deliver a judgment about the actor’s current goal. We manipulated the strength of goal expectation induced by the preceding action, implementing the parameter "goal-relatedness" between the preceding and the currently observed action. Moreover, since objects point to the probability of certain actions, we also manipulated whether the current and the preceding action shared at least one object or not. We found an interaction between the two factors goal-relatedness and shared object: IFG activation increased the weaker the goal-relatedness between the preceding and the current action was, but only when they shared at least one object. Here, integration of successive action steps was triggered by the re-appearing (shared) object but hampered by a weak goal-relatedness between the actually observed manipulation. These findings foster the recently emerging view that IFG is enhanced by goal-related conflicts during action observation. PMID:26218102

Evaluative Priming in the Pronunciation Task.

PubMed

Klauer, Karl Christoph; Becker, Manuel; Spruyt, Adriaan

2016-01-01

We replicated and extended a study by Spruyt and Hermans (2008) in which picture primes engendered an evaluative-priming effect on the pronunciation of target words. As preliminary steps, we assessed data reproducibility of the original study, conducted Pilot Study I to identify highly semantically related prime-target pairs, reanalyzed the original data excluding such pairs, conducted Pilot Study II to demonstrate that we can replicate traditional associative priming effects in the pronunciation task, and conducted Pilot Study III to generate relatively unrelated sets of prime pictures and target words. The main study comprised three between-participants conditions: (1) a close replication of the original study, (2) the same condition excluding highly related prime-target pairs, and (3) a condition based on the relatively unrelated sets of prime pictures and target words developed in Pilot Study III. There was little evidence for an evaluative priming effect independent of semantic relatedness.

An enquiry into the process of categorization of pictures and words.

PubMed

Viswanathan, Madhubalan; Childers, Terry L

2003-02-01

This paper reports a series of experiments conducted to study the categorization of pictures and words. Whereas some studies reported in the past have found a picture advantage in categorization, other studies have yielded no differences between pictures and words. This paper used an experimental paradigm designed to overcome some methodological problems to examine picture-word categorization. The results of one experiment were consistent with an advantage for pictures in categorization. To identify the source of the picture advantage in categorization, two more experiments were conducted. Findings suggest that semantic relatedness may play an important role in the categorization of both pictures and words. We explain these findings by suggesting that pictures simultaneously access both their concept and visually salient features whereas words may initially access their concept and may subsequently activate features. Therefore, pictures have an advantage in categorization by offering multiple routes to semantic processing.

Proficiency and sentence constraint effects on second language word learning.

PubMed

Ma, Tengfei; Chen, Baoguo; Lu, Chunming; Dunlap, Susan

2015-07-01

This paper presents an experiment that investigated the effects of L2 proficiency and sentence constraint on semantic processing of unknown L2 words (pseudowords). All participants were Chinese native speakers who learned English as a second language. In the experiment, we used a whole sentence presentation paradigm with a delayed semantic relatedness judgment task. Both higher and lower-proficiency L2 learners could make use of the high-constraint sentence context to judge the meaning of novel pseudowords, and higher-proficiency L2 learners outperformed lower-proficiency L2 learners in all conditions. These results demonstrate that both L2 proficiency and sentence constraint affect subsequent word learning among second language learners. We extended L2 word learning into a sentence context, replicated the sentence constraint effects previously found among native speakers, and found proficiency effects in L2 word learning. Copyright © 2015 Elsevier B.V. All rights reserved.

Semantic computing and language knowledge bases

NASA Astrophysics Data System (ADS)

Wang, Lei; Wang, Houfeng; Yu, Shiwen

2017-09-01

As the proposition of the next-generation Web - semantic Web, semantic computing has been drawing more and more attention within the circle and the industries. A lot of research has been conducted on the theory and methodology of the subject, and potential applications have also been investigated and proposed in many fields. The progress of semantic computing made so far cannot be detached from its supporting pivot - language resources, for instance, language knowledge bases. This paper proposes three perspectives of semantic computing from a macro view and describes the current status of affairs about the construction of language knowledge bases and the related research and applications that have been carried out on the basis of these resources via a case study in the Institute of Computational Linguistics at Peking University.

Benchmarking Relatedness Inference Methods with Genome-Wide Data from Thousands of Relatives.

PubMed

Ramstetter, Monica D; Dyer, Thomas D; Lehman, Donna M; Curran, Joanne E; Duggirala, Ravindranath; Blangero, John; Mezey, Jason G; Williams, Amy L

2017-09-01

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92-99%) when detecting first- and second-degree relationships, but their accuracy dwindles to <43% for seventh-degree relationships. However, most identical by descent (IBD) segment-based methods inferred seventh-degree relatives correct to within one relatedness degree for >76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry (ERSA) and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches, such as new methods that leverage relatedness signals from multiple samples, are needed to achieve a sizeable jump in performance. Copyright © 2017 Ramstetter et al.

Publication and Retrieval of Computational Chemical-Physical Data Via the Semantic Web. Final Technical Report

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

Ostlund, Neil

This research showed the feasibility of applying the concepts of the Semantic Web to Computation Chemistry. We have created the first web portal (www.chemsem.com) that allows data created in the calculations of quantum chemistry, and other such chemistry calculations to be placed on the web in a way that makes the data accessible to scientists in a semantic form never before possible. The semantic web nature of the portal allows data to be searched, found, and used as an advance over the usual approach of a relational database. The semantic data on our portal has the nature of a Giantmore » Global Graph (GGG) that can be easily merged with related data and searched globally via a SPARQL Protocol and RDF Query Language (SPARQL) that makes global searches for data easier than with traditional methods. Our Semantic Web Portal requires that the data be understood by a computer and hence defined by an ontology (vocabulary). This ontology is used by the computer in understanding the data. We have created such an ontology for computational chemistry (purl.org/gc) that encapsulates a broad knowledge of the field of computational chemistry. We refer to this ontology as the Gainesville Core. While it is perhaps the first ontology for computational chemistry and is used by our portal, it is only a start of what must be a long multi-partner effort to define computational chemistry. In conjunction with the above efforts we have defined a new potential file standard (Common Standard for eXchange – CSX for computational chemistry data). This CSX file is the precursor of data in the Resource Description Framework (RDF) form that the semantic web requires. Our portal translates CSX files (as well as other computational chemistry data files) into RDF files that are part of the graph database that the semantic web employs. We propose a CSX file as a convenient way to encapsulate computational chemistry data.« less

The role of left and right dorsolateral prefrontal cortex in semantic processing: A transcranial direct current stimulation study.

PubMed

Mitchell, Rachel L C; Vidaki, Kleio; Lavidor, Michal

2016-10-01

For complex linguistic strings such as idioms, making a decision as to the correct meaning may require complex top-down cognitive control such as the suppression of incorrect alternative meanings. In the study presented here, we used transcranial direct current stimulation to test the hypothesis that a domain general dorsolateral prefrontal cognitive control network is involved in constraining the complex processing involved. Specifically, we sought to test prominent theoretical stances on the division of labour across dorsolateral prefrontal cortex in the left- and right-hemispheres of the brain, including the role of salience and fine vs. coarse semantic coding. 32 healthy young adult participants were randomly allocated to one of two stimulation montage groups (LH anodal/RH cathodal or RH anodal/LH cathodal). Participants were tested twice, completing a semantic decision task after either receiving active or sham stimulation. The semantic decision task required participants to judge the relatedness of an idiom and a target word. The target word was figuratively related, literally related, or unrelated to the idiom. Control non-literal non-idiomatic sentences were also included that only had a literal meaning. The results showed that left-hemisphere dorsolateral prefrontal cortex is highly involved in processing figurative language, whereas both left- and right- dorsolateral prefrontal cortex contributed to literal language processing. In comparison, semantic processing for the non-idiomatic control sentences did not require domain general cognitive control as it relates to suppression of the rejected alternative meaning. The results are discussed in terms of the interplay between need for domain general cognitive control in understanding the meaning of complex sentences, hemispheric differences in semantic processing, and salience detection. Copyright © 2016 Elsevier Ltd. All rights reserved.

Acquiring concepts and features of novel words by two types of learning: direct mapping and inference.

PubMed

Chen, Shuang; Wang, Lin; Yang, Yufang

2014-04-01

This study examined the semantic representation of novel words learnt in two conditions: directly mapping a novel word to a concept (Direct mapping: DM) and inferring the concept from provided features (Inferred learning: IF). A condition where no definite concept could be inferred (No basic-level meaning: NM) served as a baseline. The semantic representation of the novel word was assessed via a semantic-relatedness judgment task. In this task, the learned novel word served as a prime, while the corresponding concept, an unlearned feature of the concept, and an unrelated word served as targets. ERP responses to the targets, primed by the novel words in the three learning conditions, were compared. For the corresponding concept, smaller N400s were elicited in the DM and IF conditions than in the NM condition, indicating that the concept could be obtained in both learning conditions. However, for the unlearned feature, the targets in the IF condition produced an N400 effect while in the DM condition elicited an LPC effect relative to the NM learning condition. No ERP difference was observed among the three learning conditions for the unrelated words. The results indicate that conditions of learning affect the semantic representation of novel word, and that the unlearned feature was only activated by the novel word in the IF learning condition. Copyright © 2014 Elsevier Ltd. All rights reserved.

Shindigs, brunches, and rodeos: the neural basis of event words.

PubMed

Bedny, Marina; Dravida, Swethasri; Saxe, Rebecca

2014-09-01

Events (e.g., "running" or "eating") constitute a basic type within human cognition and human language. We asked whether thinking about events, as compared to other conceptual categories, depends on partially independent neural circuits. Indirect evidence for this hypothesis comes from previous studies showing elevated posterior temporal responses to verbs, which typically label events. Neural responses to verbs could, however, be driven either by their grammatical or by their semantic properties. In the present experiment, we separated the effects of grammatical class (verb vs. noun) and semantic category (event vs. object) by measuring neural responses to event nouns (e.g., "the hurricane"). Participants rated the semantic relatedness of event nouns, as well as of two categories of object nouns-animals (e.g., "the alligator") and plants (e.g., "the acorn")-and three categories of verbs-manner of motion (e.g., "to roll"), emission (e.g., "to sparkle"), and perception (e.g., "to gaze"). As has previously been observed, we found larger responses to verbs than to object nouns in the left posterior middle (LMTG) and superior (LSTG) temporal gyri. Crucially, we also found that the LMTG responds more to event than to object nouns. These data suggest that part of the posterior lateral temporal response to verbs is driven by their semantic properties. By contrast, a more superior region, at the junction of the temporal and parietal cortices, responded more to verbs than to all nouns, irrespective of their semantic category. We concluded that the neural mechanisms engaged when thinking about event and object categories are partially dissociable.

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.

Computation of Semantic Number from Morphological Information

ERIC Educational Resources Information Center

Berent, Iris; Pinker, Steven; Tzelgov, Joseph; Bibi, Uri; Goldfarb, Liat

2005-01-01

The distinction between singular and plural enters into linguistic phenomena such as morphology, lexical semantics, and agreement and also must interface with perceptual and conceptual systems that assess numerosity in the world. Three experiments examine the computation of semantic number for singulars and plurals from the morphological…

A funny thing happened on the way to articulation: N400 attenuation despite behavioral interference in picture naming

PubMed Central

Blackford, Trevor; Holcomb, Phillip J.; Grainger, Jonathan; Kuperberg, Gina R.

2013-01-01

We measured Event-Related Potentials (ERPs) and naming times to picture targets preceded by masked words (stimulus onset asynchrony: 80 ms) that shared one of three different types of relationship with the names of the pictures: (1) Identity related, in which the prime was the name of the picture (“socks” – ), (2) Phonemic Onset related, in which the initial segment of the prime was the same as the name of the picture (“log” – ), and (3) Semantically related in which the prime was a co–category exemplar and associated with the name of the picture (“cake” – ). Each type of related picture target was contrasted with an Unrelated picture target, resulting in a 3 × 2 design that crossed Relationship Type between the word and the target picture (Identity, Phonemic Onset and Semantic) with Relatedness (Related and Unrelated). Modulation of the N400 component to related (versus unrelated) pictures was taken to reflect semantic processing at the interface between the picture's conceptual features and its lemma, while naming times reflected the end product of all stages of processing. Both attenuation of the N400 and shorter naming times were observed to pictures preceded by Identity related (versus Unrelated) words. No ERP effects within 600 ms, but shorter naming times, were observed to pictures preceded by Phonemic Onset related (versus Unrelated) words. An attenuated N400 (electrophysiological semantic priming) but longer naming times (behavioral semantic interference) were observed to pictures preceded by Semantically related (versus Unrelated) words. These dissociations between ERP modulation and naming times suggest that (a) phonemic onset priming occurred late, during encoding of the articulatory response, and (b) semantic behavioral interference was not driven by competition at the lemma level of representation, but rather occurred at a later stage of production. PMID:22245030

Perseveration induces dissociative uncertainty in obsessive-compulsive disorder.

PubMed

Giele, Catharina L; van den Hout, Marcel A; Engelhard, Iris M; Dek, Eliane C P; Toffolo, Marieke B J; Cath, Danielle C

2016-09-01

Obsessive compulsive (OC)-like perseveration paradoxically increases feelings of uncertainty. We studied whether the underlying mechanism between perseveration and uncertainty is a reduced accessibility of meaning ('semantic satiation'). OCD patients (n = 24) and matched non-clinical controls (n = 24) repeated words 2 (non-perseveration) or 20 times (perseveration). They decided whether this word was related to another target word. Speed of relatedness judgments and feelings of dissociative uncertainty were measured. The effects of real-life perseveration on dissociative uncertainty were tested in a smaller subsample of the OCD group (n = 9). Speed of relatedness judgments was not affected by perseveration. However, both groups reported more dissociative uncertainty after perseveration compared to non-perseveration, which was higher in OCD patients. Patients reported more dissociative uncertainty after 'clinical' perseveration compared to non-perseveration.. Both parts of this study are limited by some methodological issues and a small sample size. Although the mechanism behind 'perseveration → uncertainty' is still unclear, results suggest that the effects of perseveration are counterproductive. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exploring the additive effects of stimulus quality and word frequency: the influence of local and list-wide prime relatedness.

PubMed

Scaltritti, Michele; Balota, David A; Peressotti, Francesca

2013-01-01

Stimulus quality and word frequency produce additive effects in lexical decision performance, whereas the semantic priming effect interacts with both stimulus quality and word frequency effects. This pattern places important constraints on models of visual word recognition. In Experiment 1, all three variables were investigated within a single speeded pronunciation study. The results indicated that the joint effects of stimulus quality and word frequency were dependent upon prime relatedness. In particular, an additive effect of stimulus quality and word frequency was found after related primes, and an interactive effect was found after unrelated primes. It was hypothesized that this pattern reflects an adaptive reliance on related prime information within the experimental context. In Experiment 2, related primes were eliminated from the list, and the interactive effects of stimulus quality and word frequency found following unrelated primes in Experiment 1 reverted to additive effects for the same unrelated prime conditions. The results are supportive of a flexible lexical processor that adapts to both local prime information and global list-wide context.

Integrating Conceptual Knowledge Within and Across Representational Modalities

PubMed Central

McNorgan, Chris; Reid, Jackie; McRae, Ken

2011-01-01

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants’ knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual feature verification task. The pattern of decision latencies across Experiments 1 to 4 is consistent with a deep integration hierarchy. PMID:21093853

Implicit co-activation of American Sign Language in deaf readers: An ERP study.

PubMed

Meade, Gabriela; Midgley, Katherine J; Sevcikova Sehyr, Zed; Holcomb, Phillip J; Emmorey, Karen

2017-07-01

In an implicit phonological priming paradigm, deaf bimodal bilinguals made semantic relatedness decisions for pairs of English words. Half of the semantically unrelated pairs had phonologically related translations in American Sign Language (ASL). As in previous studies with unimodal bilinguals, targets in pairs with phonologically related translations elicited smaller negativities than targets in pairs with phonologically unrelated translations within the N400 window. This suggests that the same lexicosemantic mechanism underlies implicit co-activation of a non-target language, irrespective of language modality. In contrast to unimodal bilingual studies that find no behavioral effects, we observed phonological interference, indicating that bimodal bilinguals may not suppress the non-target language as robustly. Further, there was a subset of bilinguals who were aware of the ASL manipulation (determined by debrief), and they exhibited an effect of ASL phonology in a later time window (700-900ms). Overall, these results indicate modality-independent language co-activation that persists longer for bimodal bilinguals. Copyright © 2017 Elsevier Inc. All rights reserved.

Semantic Web Compatible Names and Descriptions for Organisms

NASA Astrophysics Data System (ADS)

Wang, H.; Wilson, N.; McGuinness, D. L.

2012-12-01

Modern scientific names are critical for understanding the biological literature and provide a valuable way to understand evolutionary relationships. To validly publish a name, a description is required to separate the described group of organisms from those described by other names at the same level of the taxonomic hierarchy. The frequent revision of descriptions due to new evolutionary evidence has lead to situations where a single given scientific name may over time have multiple descriptions associated with it and a given published description may apply to multiple scientific names. Because of these many-to-many relationships between scientific names and descriptions, the usage of scientific names as a proxy for descriptions is inevitably ambiguous. Another issue lies in the fact that the precise application of scientific names often requires careful microscopic work, or increasingly, genetic sequencing, as scientific names are focused on the evolutionary relatedness between and within named groups such as species, genera, families, etc. This is problematic to many audiences, especially field biologists, who often do not have access to the instruments and tools required to make identifications on a microscopic or genetic basis. To better connect scientific names to descriptions and find a more convenient way to support computer assisted identification, we proposed the Semantic Vernacular System, a novel naming system that creates named, machine-interpretable descriptions for groups of organisms, and is compatible with the Semantic Web. Unlike the evolutionary relationship based scientific naming system, it emphasizes the observable features of organisms. By independently naming the descriptions composed of sets of observational features, as well as maintaining connections to scientific names, it preserves the observational data used to identify organisms. The system is designed to support a peer-review mechanism for creating new names, and uses a controlled vocabulary encoded in the Web Ontology Language to represent the observational features. A prototype of the system is currently under development in collaboration with the Mushroom Observer website. It allows users to propose new names and descriptions for fungi, provide feedback on those proposals, and ultimately have them formally approved. It relies on SPARQL queries and semantic reasoning for data management. This effort will offer the mycology community a knowledge base of fungal observational features and a tool for identifying fungal observations. It will also serve as an operational specification of how the Semantic Vernacular System can be used in practice in one scientific community (in this case mycology).

Coherent concepts are computed in the anterior temporal lobes.

PubMed

Lambon Ralph, Matthew A; Sage, Karen; Jones, Roy W; Mayberry, Emily J

2010-02-09

In his Philosophical Investigations, Wittgenstein famously noted that the formation of semantic representations requires more than a simple combination of verbal and nonverbal features to generate conceptually based similarities and differences. Classical and contemporary neuroscience has tended to focus upon how different neocortical regions contribute to conceptualization through the summation of modality-specific information. The additional yet critical step of computing coherent concepts has received little attention. Some computational models of semantic memory are able to generate such concepts by the addition of modality-invariant information coded in a multidimensional semantic space. By studying patients with semantic dementia, we demonstrate that this aspect of semantic memory becomes compromised following atrophy of the anterior temporal lobes and, as a result, the patients become increasingly influenced by superficial rather than conceptual similarities.

Semantic text relatedness on Al-Qur’an translation using modified path based method

NASA Astrophysics Data System (ADS)

Irwanto, Yudi; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Abdul Baquee Muhammad [1] have built Corpus that contained AlQur’an domain, WordNet and dictionary. He has did initialisation in the development of knowledges about AlQur’an and the knowledges about relatedness between texts in AlQur’an. The Path based measurement method that proposed by Liu, Zhou and Zheng [3] has never been used in the AlQur’an domain. By using AlQur’an translation dataset in this research, the path based measurement method proposed by Liu, Zhou and Zheng [3] will be used to test this method in AlQur’an domain to obtain similarity values and to measure its correlation value. In this study the degree value is proposed to be used in modifying the path based method that proposed in previous research. Degree Value is the number of links that owned by a lcs (lowest common subsumer) node on a taxonomy. The links owned by a node on the taxonomy represent the semantic relationship that a node has in the taxonomy. By using degree value to modify the path-based method that proposed in previous research is expected that the correlation value obtained will increase. After running some experiment by using proposed method, the correlation measurement value can obtain fairly good correlation ties with 200 Word Pairs derive from Noun POS SimLex-999. The correlation value that be obtained is 93.3% which means their bonds are strong and they have very strong correlation. Whereas for the POS other than Noun POS vocabulary that owned by WordNet is incomplete therefore many pairs of words that the value of its similarity is zero so the correlation value is low.

Investigating the flow of information during speaking: the impact of morpho-phonological, associative, and categorical picture distractors on picture naming

PubMed Central

Bölte, Jens; Böhl, Andrea; Dobel, Christian; Zwitserlood, Pienie

2015-01-01

In three experiments, participants named target pictures by means of German compound words (e.g., Gartenstuhl–garden chair), each accompanied by two different distractor pictures (e.g., lawn mower and swimming pool). Targets and distractor pictures were semantically related either associatively (garden chair and lawn mower) or by a shared semantic category (garden chair and wardrobe). Within each type of semantic relation, target and distractor pictures either shared morpho-phonological (word-form) information (Gartenstuhl with Gartenzwerg, garden gnome, and Gartenschlauch, garden hose) or not. A condition with two completely unrelated pictures served as baseline. Target naming was facilitated when distractor and target pictures were morpho-phonologically related. This is clear evidence for the activation of word-form information of distractor pictures. Effects were larger for associatively than for categorically related distractors and targets, which constitute evidence for lexical competition. Mere categorical relatedness, in the absence of morpho-phonological overlap, resulted in null effects (Experiments 1 and 2), and only speeded target naming when effects reflect only conceptual, but not lexical processing (Experiment 3). Given that distractor pictures activate their word forms, the data cannot be easily reconciled with discrete serial models. The results fit well with models that allow information to cascade forward from conceptual to word-form levels. PMID:26528209

NELasso: Group-Sparse Modeling for Characterizing Relations Among Named Entities in News Articles.

PubMed

Tariq, Amara; Karim, Asim; Foroosh, Hassan

2017-10-01

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities' network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time.

Investigating the flow of information during speaking: the impact of morpho-phonological, associative, and categorical picture distractors on picture naming.

PubMed

Bölte, Jens; Böhl, Andrea; Dobel, Christian; Zwitserlood, Pienie

2015-01-01

In three experiments, participants named target pictures by means of German compound words (e.g., Gartenstuhl-garden chair), each accompanied by two different distractor pictures (e.g., lawn mower and swimming pool). Targets and distractor pictures were semantically related either associatively (garden chair and lawn mower) or by a shared semantic category (garden chair and wardrobe). Within each type of semantic relation, target and distractor pictures either shared morpho-phonological (word-form) information (Gartenstuhl with Gartenzwerg, garden gnome, and Gartenschlauch, garden hose) or not. A condition with two completely unrelated pictures served as baseline. Target naming was facilitated when distractor and target pictures were morpho-phonologically related. This is clear evidence for the activation of word-form information of distractor pictures. Effects were larger for associatively than for categorically related distractors and targets, which constitute evidence for lexical competition. Mere categorical relatedness, in the absence of morpho-phonological overlap, resulted in null effects (Experiments 1 and 2), and only speeded target naming when effects reflect only conceptual, but not lexical processing (Experiment 3). Given that distractor pictures activate their word forms, the data cannot be easily reconciled with discrete serial models. The results fit well with models that allow information to cascade forward from conceptual to word-form levels.

Bridging Social and Semantic Computing - Design and Evaluation of User Interfaces for Hybrid Systems

ERIC Educational Resources Information Center

Bostandjiev, Svetlin Alex I.

2012-01-01

The evolution of the Web brought new interesting problems to computer scientists that we loosely classify in the fields of social and semantic computing. Social computing is related to two major paradigms: computations carried out by a large amount of people in a collective intelligence fashion (i.e. wikis), and performing computations on social…

Relatedness in spatially structured populations with empty sites: An approach based on spatial moment equations.

PubMed

Lion, Sébastien

2009-09-07

Taking into account the interplay between spatial ecological dynamics and selection is a major challenge in evolutionary ecology. Although inclusive fitness theory has proven to be a very useful tool to unravel the interactions between spatial genetic structuring and selection, applications of the theory usually rely on simplifying demographic assumptions. In this paper, I attempt to bridge the gap between spatial demographic models and kin selection models by providing a method to compute approximations for relatedness coefficients in a spatial model with empty sites. Using spatial moment equations, I provide an approximation of nearest-neighbour relatedness on random regular networks, and show that this approximation performs much better than the ordinary pair approximation. I discuss the connection between the relatedness coefficients I define and those used in population genetics, and sketch some potential extensions of the theory.

High Performance Semantic Factoring of Giga-Scale Semantic Graph Databases

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

Joslyn, Cliff A.; Adolf, Robert D.; Al-Saffar, Sinan

2010-10-04

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to bring high performance computational resources to bear on their analysis, interpretation, and visualization, especially with respect to their innate semantic structure. Our research group built a novel high performance hybrid system comprising computational capability for semantic graph database processing utilizing the large multithreaded architecture of the Cray XMT platform, conventional clusters, and large data stores. In this paper we describe that architecture, and present the results of our deployingmore » that for the analysis of the Billion Triple dataset with respect to its semantic factors.« less

The semantic system is involved in mathematical problem solving.

PubMed

Zhou, Xinlin; Li, Mengyi; Li, Leinian; Zhang, Yiyun; Cui, Jiaxin; Liu, Jie; Chen, Chuansheng

2018-02-01

Numerous studies have shown that the brain regions around bilateral intraparietal cortex are critical for number processing and arithmetical computation. However, the neural circuits for more advanced mathematics such as mathematical problem solving (with little routine arithmetical computation) remain unclear. Using functional magnetic resonance imaging (fMRI), this study (N = 24 undergraduate students) compared neural bases of mathematical problem solving (i.e., number series completion, mathematical word problem solving, and geometric problem solving) and arithmetical computation. Direct subject- and item-wise comparisons revealed that mathematical problem solving typically had greater activation than arithmetical computation in all 7 regions of the semantic system (which was based on a meta-analysis of 120 functional neuroimaging studies on semantic processing). Arithmetical computation typically had greater activation in the supplementary motor area and left precentral gyrus. The results suggest that the semantic system in the brain supports mathematical problem solving. Copyright © 2017 Elsevier Inc. All rights reserved.

High Performance Descriptive Semantic Analysis of Semantic Graph Databases

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

Joslyn, Cliff A.; Adolf, Robert D.; al-Saffar, Sinan

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to understand their inherent semantic structure, whether codified in explicit ontologies or not. Our group is researching novel methods for what we call descriptive semantic analysis of RDF triplestores, to serve purposes of analysis, interpretation, visualization, and optimization. But data size and computational complexity makes it increasingly necessary to bring high performance computational resources to bear on this task. Our research group built a novel high performance hybrid system comprisingmore » computational capability for semantic graph database processing utilizing the large multi-threaded architecture of the Cray XMT platform, conventional servers, and large data stores. In this paper we describe that architecture and our methods, and present the results of our analyses of basic properties, connected components, namespace interaction, and typed paths such for the Billion Triple Challenge 2010 dataset.« less

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…

Abstract conceptual feature ratings predict gaze within written word arrays: evidence from a Visual Wor(l)d paradigm

PubMed Central

Primativo, Silvia; Reilly, Jamie; Crutch, Sebastian J

2016-01-01

The Abstract Conceptual Feature (ACF) framework predicts that word meaning is represented within a high-dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eye tracking via an adaptation of the classical ‘visual word paradigm’. Healthy adults (N=20) selected the lexical item most related to a probe word in a 4-item written word array comprising the target and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye-movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were none the less significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe-target similarity at least as well as latent semantic analysis ratings which are based on word co-occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information (not restricted to sensorimotor and emotion information) shape the organization of abstract concepts. Whilst the adapted ‘visual word paradigm’ is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. PMID:26901571

Analysis of a mammography teaching program based on an affordance design model.

PubMed

Luo, Ping; Eikman, Edward A; Kealy, William; Qian, Wei

2006-12-01

The wide use of computer technology in education, particularly in mammogram reading, asks for e-learning evaluation. The existing media comparative studies, learner attitude evaluations, and performance tests are problematic. Based on an affordance design model, this study examined an existing e-learning program on mammogram reading. The selection criteria include content relatedness, representativeness, e-learning orientation, image quality, program completeness, and accessibility. A case study was conducted to examine the affordance features, functions, and presentations of the selected software. Data collection and analysis methods include interviews, protocol-based document analysis, and usability tests and inspection. Also some statistics were calculated. The examination of PBE identified that this educational software designed and programmed some tools. The learner can use these tools in the process of optimizing displays, scanning images, comparing different projections, marking the region of interests, constructing a descriptive report, assessing one's learning outcomes, and comparing one's decisions with the experts' decisions. Further, PBE provides some resources for the learner to construct one's knowledge and skills, including a categorized image library, a term-searching function, and some teaching links. Besides, users found it easy to navigate and carry out tasks. The users also reacted positively toward PBE's navigation system, instructional aids, layout, pace and flow of information, graphics, and other presentation design. The software provides learners with some cognitive tools, supporting their perceptual problem-solving processes and extending their capabilities. Learners can internalize the mental models in mammogram reading through multiple perceptual triangulations, sensitization of related features, semantic description of mammogram findings, and expert-guided semantic report construction. The design of these cognitive tools and the software interface matches the findings and principles in human learning and instructional design. Working with PBE's case-based simulations and categorized gallery, learners can enrich and transfer their experience to their jobs.

The neural and computational bases of semantic cognition.

PubMed

Ralph, Matthew A Lambon; Jefferies, Elizabeth; Patterson, Karalyn; Rogers, Timothy T

2017-01-01

Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. This Review summarizes key findings and issues arising from a decade of research into the neurocognitive and neurocomputational underpinnings of this ability, leading to a new framework that we term controlled semantic cognition (CSC). CSC offers solutions to long-standing queries in philosophy and cognitive science, and yields a convergent framework for understanding the neural and computational bases of healthy semantic cognition and its dysfunction in brain disorders.

High performance semantic factoring of giga-scale semantic graph databases.

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

al-Saffar, Sinan; Adolf, Bob; Haglin, David

2010-10-01

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to bring high performance computational resources to bear on their analysis, interpretation, and visualization, especially with respect to their innate semantic structure. Our research group built a novel high performance hybrid system comprising computational capability for semantic graph database processing utilizing the large multithreaded architecture of the Cray XMT platform, conventional clusters, and large data stores. In this paper we describe that architecture, and present the results of our deployingmore » that for the analysis of the Billion Triple dataset with respect to its semantic factors, including basic properties, connected components, namespace interaction, and typed paths.« less

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.

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.

Auto-Generated Semantic Processing Services

NASA Technical Reports Server (NTRS)

Davis, Rodney; Hupf, Greg

2009-01-01

Auto-Generated Semantic Processing (AGSP) Services is a suite of software tools for automated generation of other computer programs, denoted cross-platform semantic adapters, that support interoperability of computer-based communication systems that utilize a variety of both new and legacy communication software running in a variety of operating- system/computer-hardware combinations. AGSP has numerous potential uses in military, space-exploration, and other government applications as well as in commercial telecommunications. The cross-platform semantic adapters take advantage of common features of computer- based communication systems to enforce semantics, messaging protocols, and standards of processing of streams of binary data to ensure integrity of data and consistency of meaning among interoperating systems. The auto-generation aspect of AGSP Services reduces development time and effort by emphasizing specification and minimizing implementation: In effect, the design, building, and debugging of software for effecting conversions among complex communication protocols, custom device mappings, and unique data-manipulation algorithms is replaced with metadata specifications that map to an abstract platform-independent communications model. AGSP Services is modular and has been shown to be easily integrable into new and legacy NASA flight and ground communication systems.

The Inhibitory Mechanism in Learning Ambiguous Words in a Second Language

PubMed Central

Lu, Yao; Wu, Junjie; Dunlap, Susan; Chen, Baoguo

2017-01-01

Ambiguous words are hard to learn, yet little is known about what causes this difficulty. The current study aimed to investigate the relationship between the representations of new and prior meanings of ambiguous words in second language (L2) learning, and to explore the function of inhibitory control on L2 ambiguous word learning at the initial stage of learning. During a 4-day learning phase, Chinese–English bilinguals learned 30 novel English words for 30 min per day using bilingual flashcards. Half of the words to be learned were unambiguous (had one meaning) and half were ambiguous (had two semantically unrelated meanings learned in sequence). Inhibitory control was introduced as a subject variable measured by a Stroop task. The semantic representations established for the studied items were probed using a cross-language semantic relatedness judgment task, in which the learned English words served as the prime, and the targets were either semantically related or unrelated to the prime. Results showed that response latencies for the second meaning of ambiguous words were slower than for the first meaning and for unambiguous words, and that performance on only the second meaning of ambiguous words was predicted by inhibitory control ability. These results suggest that, at the initial stage of L2 ambiguous word learning, the representation of the second meaning is weak, probably interfered with by the representation of the prior learned meaning. Moreover, inhibitory control may modulate learning of the new meanings, such that individuals with better inhibitory control may more effectively suppress interference from the first meaning, and thus learn the new meaning more quickly. PMID:28496423

The Inhibitory Mechanism in Learning Ambiguous Words in a Second Language.

PubMed

Lu, Yao; Wu, Junjie; Dunlap, Susan; Chen, Baoguo

2017-01-01

Ambiguous words are hard to learn, yet little is known about what causes this difficulty. The current study aimed to investigate the relationship between the representations of new and prior meanings of ambiguous words in second language (L2) learning, and to explore the function of inhibitory control on L2 ambiguous word learning at the initial stage of learning. During a 4-day learning phase, Chinese-English bilinguals learned 30 novel English words for 30 min per day using bilingual flashcards. Half of the words to be learned were unambiguous (had one meaning) and half were ambiguous (had two semantically unrelated meanings learned in sequence). Inhibitory control was introduced as a subject variable measured by a Stroop task. The semantic representations established for the studied items were probed using a cross-language semantic relatedness judgment task, in which the learned English words served as the prime, and the targets were either semantically related or unrelated to the prime. Results showed that response latencies for the second meaning of ambiguous words were slower than for the first meaning and for unambiguous words, and that performance on only the second meaning of ambiguous words was predicted by inhibitory control ability. These results suggest that, at the initial stage of L2 ambiguous word learning, the representation of the second meaning is weak, probably interfered with by the representation of the prior learned meaning. Moreover, inhibitory control may modulate learning of the new meanings, such that individuals with better inhibitory control may more effectively suppress interference from the first meaning, and thus learn the new meaning more quickly.

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.

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

NASA Astrophysics Data System (ADS)

Wang, Hongbin; Feng, Yinhan; Cheng, Liang

2018-03-01

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

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

ERIC Educational Resources Information Center

Silberman, Yaron; Bentin, Shlomo; Miikkulainen, Risto

2007-01-01

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

An Intelligent Semantic E-Learning Framework Using Context-Aware Semantic Web Technologies

ERIC Educational Resources Information Center

Huang, Weihong; Webster, David; Wood, Dawn; Ishaya, Tanko

2006-01-01

Recent developments of e-learning specifications such as Learning Object Metadata (LOM), Sharable Content Object Reference Model (SCORM), Learning Design and other pedagogy research in semantic e-learning have shown a trend of applying innovative computational techniques, especially Semantic Web technologies, to promote existing content-focused…

Computer-Based Semantic Network in Molecular Biology: A Demonstration.

ERIC Educational Resources Information Center

Callman, Joshua L.; And Others

This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

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

PubMed Central

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 opposite effects on word processing. Experiment 2 confirmed these results: Word processing was slower for dense near neighborhoods and faster for dense distant neighborhoods. Analysis of a computational model showed that attractor dynamics can produce this pattern of neighborhood effects. The authors argue for reconsideration of traditional models of neighborhood effects in terms of attractor dynamics, which allow both inhibitory and facilitative effects to emerge. PMID:18194055

Integrating conceptual knowledge within and across representational modalities.

PubMed

McNorgan, Chris; Reid, Jackie; McRae, Ken

2011-02-01

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants' knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual-feature verification task. The pattern of decision latencies across Experiments 1-4 is consistent with a deep integration hierarchy. Copyright © 2010 Elsevier B.V. All rights reserved.

Dispositional mindfulness and semantic integration of emotional words: Evidence from event-related brain potentials.

PubMed

Dorjee, Dusana; Lally, Níall; Darrall-Rew, Jonathan; Thierry, Guillaume

2015-08-01

Initial research shows that mindfulness training can enhance attention and modulate the affective response. However, links between mindfulness and language processing remain virtually unexplored despite the prominent role of overt and silent negative ruminative speech in depressive and anxiety-related symptomatology. Here, we measured dispositional mindfulness and recorded participants' event-related brain potential responses to positive and negative target words preceded by words congruent or incongruent with the targets in terms of semantic relatedness and emotional valence. While the low mindfulness group showed similar N400 effect pattern for positive and negative targets, high dispositional mindfulness was associated with larger N400 effect to negative targets. This result suggests that negative meanings are less readily accessible in people with high dispositional mindfulness. Furthermore, high dispositional mindfulness was associated with reduced P600 amplitudes to emotional words, suggesting less post-analysis and attentional effort which possibly relates to a lower inclination to ruminate. Overall, these findings provide initial evidence on associations between modifications in language systems and mindfulness. Copyright © 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

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.

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

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.

Semantic Coherence Facilitates Distributional Learning.

PubMed

Ouyang, Long; Boroditsky, Lera; Frank, Michael C

2017-04-01

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. Copyright © 2016 Cognitive Science Society, Inc.

Designing learning management system interoperability in semantic web

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Incrementally Dissociating Syntax and Semantics

ERIC Educational Resources Information Center

Brennan, Jonathan R.

2010-01-01

A basic challenge for research into the neurobiology of language is understanding how the brain combines words to make complex representations. Linguistic theory divides this task into several computations including syntactic structure building and semantic composition. The close relationship between these computations, however, poses a strong…

Predicting Word Maturity from Frequency and Semantic Diversity: A Computational Study

ERIC Educational Resources Information Center

Jorge-Botana, Guillermo; Olmos, Ricardo; Sanjosé, Vicente

2017-01-01

Semantic word representation changes over different ages of childhood until it reaches its adult form. One method to formally model this change is the word maturity paradigm. This method uses a text sample for each age, including adult age, and transforms the samples into a semantic space by means of Latent Semantic Analysis. The representation of…

The Development of a Social Networking–Based Relatedness Intervention Among Young, First-Time Blood Donors: Pilot Study

PubMed Central

Frye, Victoria; Duffy, Louisa; France, Janis L; Kessler, Debra A; Rebosa, Mark; Shaz, Beth H; Carlson, Bruce W

2018-01-01

Background Increasing repeat blood donation behavior is a critical public health goal. According to self-determination theory, the process of developing internal motivation to give blood and an associated self-identity as a blood donor may be promoted by feelings of “relatedness” or a connection to other donors, which may be enhanced through social relations and interactions. Objective The purpose of this report it to describe the development and pilot testing of a social networking-based (Facebook) intervention condition designed to increase feelings of relatedness via virtual social interaction and support. Methods To develop the intervention condition content, images, text, polls, and video content were assembled. Ohio University college students (N=127) rated the content (82 images/text) presented by computer in random order using a scale of one to five on various dimensions of relatedness. Mean ratings were calculated and analyses of variance were conducted to assess associations among the dimensions. Based on these results, the relatedness intervention was adapted and evaluated for feasibility, acceptability, and preliminary efficacy among 24 first-time donors, aged 18 to 24 years, in a 30-day pilot trial. Paired t-tests were conducted to examine change over time in relatedness and connectedness. Results The intervention condition that was developed was acceptable and feasible. Results of the uncontrolled, preintervention, and postintervention evaluation revealed that feelings of individual-level relatedness increased significantly after the intervention. Conclusions By promoting first-time blood donor relatedness, our goal is to enhance internal motivation for donating and the integration of the blood donor identity, thus increasing the likelihood of future repeat donation. Trial Registration ClinicalTrials.gov NCT02717338; https://clinicaltrials.gov/ct2/show/NCT02717338 (Archived by WebCite at http://www.webcitation.org/6ymHRBCwu) PMID:29699961

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.

Visual naming deficits in dyslexia: An ERP investigation of different processing domains.

PubMed

Araújo, Susana; Faísca, Luís; Reis, Alexandra; Marques, J Frederico; Petersson, Karl Magnus

2016-10-01

Naming speed deficits are well documented in developmental dyslexia, expressed by slower naming times and more errors in response to familiar items. Here we used event-related potentials (ERPs) to examine at what processing level the deficits in dyslexia emerge during a discrete-naming task. Dyslexic and skilled adult control readers performed a primed object-naming task, in which the relationship between the prime and the target was manipulated along perceptual, semantic and phonological dimensions. A 3×2 design that crossed Relationship Type (Visual, Phonemic Onset, and Semantic) with Relatedness (Related and Unrelated) was used. An attenuated N/P190 - indexing early visual processing - and N300 - which index late visual processing - was observed to pictures preceded by perceptually related (vs. unrelated) primes in the control but not in the dyslexic group. These findings suggest suboptimal processing in early stages of object processing in dyslexia, when integration and mapping of perceptual information to a more form-specific percept in memory take place. On the other hand, both groups showed an N400 effect associated with semantically related pictures (vs. unrelated), taken to reflect intact integration of semantic similarities in both dyslexic and control readers. We also found an electrophysiological effect of phonological priming in the N400 range - that is, an attenuated N400 to objects preceded by phonemic related primes vs. unrelated - while it showed a more widespread distributed and more pronounced over the right hemisphere in the dyslexics. Topographic differences between groups might have originated from a word form encoding process with different characteristics in dyslexics compared to control readers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Adult and Child Semantic Neighbors of the Kroll and Potter (1984) Nonobjects

PubMed Central

Storkel, Holly L.; Adlof, Suzanne M.

2008-01-01

Purpose The purpose was to determine the number of semantic neighbors, namely semantic set size, for 88 nonobjects (Kroll & Potter, 1984) and determine how semantic set size related to other measures and age. Method Data were collected from 82 adults and 92 preschool children in a discrete association task. The nonobjects were presented via computer, and participants reported the first word that came to mind that was meaningfully related to the nonobject. Words reported by two or more participants were considered semantic neighbors. The strength of each neighbor was computed as the proportion of participants who reported the neighbor. Results Results showed that semantic set size was not significantly correlated with objectlikeness ratings or object decision reaction times from Kroll and Potter (1984). However, semantic set size was significantly negatively correlated with the strength of the strongest neighbor(s). In terms of age effects, adult and child semantic set sizes were significantly positively correlated and the majority of numeric differences were on the order of 0–3 neighbors. Comparison of actual neighbors showed greater discrepancies; however, this varied by neighbor strength. Conclusions Semantic set size can be determined for nonobjects. Specific guidelines are suggested for using these nonobjects in future research. PMID:19252127

Disturbance and density-dependent processes (competition and facilitation) influence the fine-scale genetic structure of a tree species' population.

PubMed

Fajardo, Alex; Torres-Díaz, Cristian; Till-Bottraud, Irène

2016-01-01

Disturbances, dispersal and biotic interactions are three major drivers of the spatial distribution of genotypes within populations, the last of which has been less studied than the other two. This study aimed to determine the role of competition and facilitation in the degree of conspecific genetic relatedness of nearby individuals of tree populations. It was expected that competition among conspecifics will lead to low relatedness, while facilitation will lead to high relatedness (selection for high relatedness within clusters). The stand structure and spatial genetic structure (SGS) of trees were examined within old-growth and second-growth forests (including multi-stemmed trees at the edge of forests) of Nothofagus pumilio following large-scale fires in Patagonia, Chile. Genetic spatial autocorrelations were computed on a spatially explicit sampling of the forests using five microsatellite loci. As biotic plant interactions occur among immediate neighbours, mean nearest neighbour distance (MNND) among trees was computed as a threshold for distinguishing the effects of disturbances and biotic interactions. All forests exhibited a significant SGS for distances greater than the MNND. The old-growth forest genetic and stand structure indicated gap recolonization from nearby trees (significantly related trees at distances between 4 and 10 m). At distances smaller than the MNND, trees of the second-growth interior forest showed significantly lower relatedness, suggesting a fading of the recolonization structure by competition, whereas the second-growth edge forest showed a positive and highly significant relatedness among trees (higher among stems of a cluster than among stems of different clusters), resulting from facilitation. Biotic interactions are shown to influence the genetic composition of a tree population. However, facilitation can only persist if individuals are related. Thus, the genetic composition in turn influences what type of biotic interactions will take place among immediate neighbours in post-disturbance forests. © The Author 2015. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A Semantic Grid Oriented to E-Tourism

NASA Astrophysics Data System (ADS)

Zhang, Xiao Ming

With increasing complexity of tourism business models and tasks, there is a clear need of the next generation e-Tourism infrastructure to support flexible automation, integration, computation, storage, and collaboration. Currently several enabling technologies such as semantic Web, Web service, agent and grid computing have been applied in the different e-Tourism applications, however there is no a unified framework to be able to integrate all of them. So this paper presents a promising e-Tourism framework based on emerging semantic grid, in which a number of key design issues are discussed including architecture, ontologies structure, semantic reconciliation, service and resource discovery, role based authorization and intelligent agent. The paper finally provides the implementation of the framework.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

PubMed Central

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

2014-01-01

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification. PMID:24632078

An ’Active Vision’ Computational Model of Visual Search for Human-Computer Interaction

DTIC Science & Technology

2009-01-01

semantically related (e.g. cashew , peanut, almond) or randomly grouped (e.g. elm, eraser, potato). Groups were either labeled or not. In some...colored region were further semantically related (e.g. nuts with candy, and clothing with cosmetics). Layouts always contained 28 eight groups with

Grounding Collaborative Learning in Semantics-Based Critiquing

ERIC Educational Resources Information Center

Cheung, William K.; Mørch, Anders I.; Wong, Kelvin C.; Lee, Cynthia; Liu, Jiming; Lam, Mason H.

2007-01-01

In this article we investigate the use of latent semantic analysis (LSA), critiquing systems, and knowledge building to support computer-based teaching of English composition. We have built and tested an English composition critiquing system that makes use of LSA to analyze student essays and compute feedback by comparing their essays with…

In a Concurrent Memory and Auditory Perception Task, the Pupil Dilation Response Is More Sensitive to Memory Load Than to Auditory Stimulus Characteristics.

PubMed

Zekveld, Adriana A; Kramer, Sophia E; Rönnberg, Jerker; Rudner, Mary

2018-06-19

Speech understanding may be cognitively demanding, but it can be enhanced when semantically related text cues precede auditory sentences. The present study aimed to determine whether (a) providing text cues reduces pupil dilation, a measure of cognitive load, during listening to sentences, (b) repeating the sentences aloud affects recall accuracy and pupil dilation during recall of cue words, and (c) semantic relatedness between cues and sentences affects recall accuracy and pupil dilation during recall of cue words. Sentence repetition following text cues and recall of the text cues were tested. Twenty-six participants (mean age, 22 years) with normal hearing listened to masked sentences. On each trial, a set of four-word cues was presented visually as text preceding the auditory presentation of a sentence whose meaning was either related or unrelated to the cues. On each trial, participants first read the cue words, then listened to a sentence. Following this they spoke aloud either the cue words or the sentence, according to instruction, and finally on all trials orally recalled the cues. Peak pupil dilation was measured throughout listening and recall on each trial. Additionally, participants completed a test measuring the ability to perceive degraded verbal text information and three working memory tests (a reading span test, a size-comparison span test, and a test of memory updating). Cue words that were semantically related to the sentence facilitated sentence repetition but did not reduce pupil dilation. Recall was poorer and there were more intrusion errors when the cue words were related to the sentences. Recall was also poorer when sentences were repeated aloud. Both behavioral effects were associated with greater pupil dilation. Larger reading span capacity and smaller size-comparison span were associated with larger peak pupil dilation during listening. Furthermore, larger reading span and greater memory updating ability were both associated with better cue recall overall. Although sentence-related word cues facilitate sentence repetition, our results indicate that they do not reduce cognitive load during listening in noise with a concurrent memory load. As expected, higher working memory capacity was associated with better recall of the cues. Unexpectedly, however, semantic relatedness with the sentence reduced word cue recall accuracy and increased intrusion errors, suggesting an effect of semantic confusion. Further, speaking the sentence aloud also reduced word cue recall accuracy, probably due to articulatory suppression. Importantly, imposing a memory load during listening to sentences resulted in the absence of formerly established strong effects of speech intelligibility on the pupil dilation response. This nullified intelligibility effect demonstrates that the pupil dilation response to a cognitive (memory) task can completely overshadow the effect of perceptual factors on the pupil dilation response. This highlights the importance of taking cognitive task load into account during auditory testing.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Extracting Useful Semantic Information from Large Scale Corpora of Text

ERIC Educational Resources Information Center

Mendoza, Ray Padilla, Jr.

2012-01-01

Extracting and representing semantic information from large scale corpora is at the crux of computer-assisted knowledge generation. Semantic information depends on collocation extraction methods, mathematical models used to represent distributional information, and weighting functions which transform the space. This dissertation provides a…

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

NASA Astrophysics Data System (ADS)

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

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

"Truth be told" - Semantic memory as the scaffold for veridical communication.

PubMed

Hayes, Brett K; Ramanan, Siddharth; Irish, Muireann

2018-01-01

Theoretical accounts placing episodic memory as central to constructive and communicative functions neglect the role of semantic memory. We argue that the decontextualized nature of semantic schemas largely supersedes the computational bottleneck and error-prone nature of episodic memory. Rather, neuroimaging and neuropsychological evidence of episodic-semantic interactions suggest that an integrative framework more accurately captures the mechanisms underpinning social communication.

Ubiquitous Computing Services Discovery and Execution Using a Novel Intelligent Web Services Algorithm

PubMed Central

Choi, Okkyung; Han, SangYong

2007-01-01

Ubiquitous Computing makes it possible to determine in real time the location and situations of service requesters in a web service environment as it enables access to computers at any time and in any place. Though research on various aspects of ubiquitous commerce is progressing at enterprises and research centers, both domestically and overseas, analysis of a customer's personal preferences based on semantic web and rule based services using semantics is not currently being conducted. This paper proposes a Ubiquitous Computing Services System that enables a rule based search as well as semantics based search to support the fact that the electronic space and the physical space can be combined into one and the real time search for web services and the construction of efficient web services thus become possible.

Putting semantics into the semantic web: how well can it capture biology?

PubMed

Kazic, Toni

2006-01-01

Could the Semantic Web work for computations of biological interest in the way it's intended to work for movie reviews and commercial transactions? It would be wonderful if it could, so it's worth looking to see if its infrastructure is adequate to the job. The technologies of the Semantic Web make several crucial assumptions. I examine those assumptions; argue that they create significant problems; and suggest some alternative ways of achieving the Semantic Web's goals for biology.

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

PubMed

Sogancioglu, Gizem; Öztürk, Hakime; Özgür, Arzucan

2017-07-15

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

Quest for a Computerised Semantics.

ERIC Educational Resources Information Center

Leslie, Adrian R.

The objective of this thesis was to colligate the various strands of research in the literature of computational linguistics that have to do with the computational treatment of semantic content so as to encode it into a computerized dictionary. In chapter 1 the course of mechanical translation (1947-1960) and quantitative linguistics is traced to…

Philosophy of Language. Course Notes for a Tutorial on Computational Semantics.

ERIC Educational Resources Information Center

Wilks, Yorick

This course was part of a tutorial focusing on the state of computational semantics, i.e., the state of work on natural language within the artificial intelligence (AI) paradigm. The discussion in the course centered on the philosophers Richard Montague and Ludwig Wittgenstein. The course was divided into three sections: (1)…

The Semantic Web in Teacher Education

ERIC Educational Resources Information Center

Czerkawski, Betül Özkan

2014-01-01

The Semantic Web enables increased collaboration among computers and people by organizing unstructured data on the World Wide Web. Rather than a separate body, the Semantic Web is a functional extension of the current Web made possible by defining relationships among websites and other online content. When explicitly defined, these relationships…

Structure and Deterioration of Semantic Memory: A Neuropsychological and Computational Investigation

ERIC Educational Resources Information Center

Rogers, Timothy T.; Lambon Ralph, Matthew A.; Garrard, Peter; Bozeat, Sasha; McClelland, James L.; Hodges, John R.; Patterson, Karalyn

2004-01-01

Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual…

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

From data to analysis: linking NWChem and Avogadro with the syntax and semantics of Chemical Markup Language

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

De Jong, Wibe A.; Walker, Andrew M.; Hanwell, Marcus D.

Background Multidisciplinary integrated research requires the ability to couple the diverse sets of data obtained from a range of complex experiments and computer simulations. Integrating data requires semantically rich information. In this paper the generation of semantically rich data from the NWChem computational chemistry software is discussed within the Chemical Markup Language (CML) framework. Results The NWChem computational chemistry software has been modified and coupled to the FoX library to write CML compliant XML data files. The FoX library was expanded to represent the lexical input files used by the computational chemistry software. Conclusions The production of CML compliant XMLmore » files for the computational chemistry software NWChem can be relatively easily accomplished using the FoX library. A unified computational chemistry or CompChem convention and dictionary needs to be developed through a community-based effort. The long-term goal is to enable a researcher to do Google-style chemistry and physics searches.« less

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…

Distinctive Features Hold a Privileged Status in the Computation of Word Meaning: Implications for Theories of Semantic Memory

ERIC Educational Resources Information Center

Cree, George S.; McNorgan, Chris; McRae, Ken

2006-01-01

The authors present data from 2 feature verification experiments designed to determine whether distinctive features have a privileged status in the computation of word meaning. They use an attractor-based connectionist model of semantic memory to derive predictions for the experiments. Contrary to central predictions of the conceptual structure…

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

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

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

Studies of Human Memory and Language Processing.

ERIC Educational Resources Information Center

Collins, Allan M.

The purposes of this study were to determine the nature of human semantic memory and to obtain knowledge usable in the future development of computer systems that can converse with people. The work was based on a computer model which is designed to comprehend English text, relating the text to information stored in a semantic data base that is…

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

ERIC Educational Resources Information Center

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

2012-01-01

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

High-Dimensional Semantic Space Accounts of Priming

ERIC Educational Resources Information Center

Jones, Michael N.; Kintsch, Walter; Mewhort, Douglas J. K.

2006-01-01

A broad range of priming data has been used to explore the structure of semantic memory and to test between models of word representation. In this paper, we examine the computational mechanisms required to learn distributed semantic representations for words directly from unsupervised experience with language. To best account for the variety of…

Research on Extension of Sparql Ontology Query Language Considering the Computation of Indoor Spatial Relations

NASA Astrophysics Data System (ADS)

Li, C.; Zhu, X.; Guo, W.; Liu, Y.; Huang, H.

2015-05-01

A method suitable for indoor complex semantic query considering the computation of indoor spatial relations is provided According to the characteristics of indoor space. This paper designs ontology model describing the space related information of humans, events and Indoor space objects (e.g. Storey and Room) as well as their relations to meet the indoor semantic query. The ontology concepts are used in IndoorSPARQL query language which extends SPARQL syntax for representing and querying indoor space. And four types specific primitives for indoor query, "Adjacent", "Opposite", "Vertical" and "Contain", are defined as query functions in IndoorSPARQL used to support quantitative spatial computations. Also a method is proposed to analysis the query language. Finally this paper adopts this method to realize indoor semantic query on the study area through constructing the ontology model for the study building. The experimental results show that the method proposed in this paper can effectively support complex indoor space semantic query.

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.

The Development of a Social Networking-Based Relatedness Intervention Among Young, First-Time Blood Donors: Pilot Study.

PubMed

Frye, Victoria; Duffy, Louisa; France, Janis L; Kessler, Debra A; Rebosa, Mark; Shaz, Beth H; Carlson, Bruce W; France, Christopher R

2018-04-26

Increasing repeat blood donation behavior is a critical public health goal. According to self-determination theory, the process of developing internal motivation to give blood and an associated self-identity as a blood donor may be promoted by feelings of “relatedness” or a connection to other donors, which may be enhanced through social relations and interactions. The purpose of this report it to describe the development and pilot testing of a social networking-based (Facebook) intervention condition designed to increase feelings of relatedness via virtual social interaction and support. To develop the intervention condition content, images, text, polls, and video content were assembled. Ohio University college students (N=127) rated the content (82 images/text) presented by computer in random order using a scale of one to five on various dimensions of relatedness. Mean ratings were calculated and analyses of variance were conducted to assess associations among the dimensions. Based on these results, the relatedness intervention was adapted and evaluated for feasibility, acceptability, and preliminary efficacy among 24 first-time donors, aged 18 to 24 years, in a 30-day pilot trial. Paired t-tests were conducted to examine change over time in relatedness and connectedness. The intervention condition that was developed was acceptable and feasible. Results of the uncontrolled, preintervention, and postintervention evaluation revealed that feelings of individual-level relatedness increased significantly after the intervention. By promoting first-time blood donor relatedness, our goal is to enhance internal motivation for donating and the integration of the blood donor identity, thus increasing the likelihood of future repeat donation. ClinicalTrials.gov NCT02717338; https://clinicaltrials.gov/ct2/show/NCT02717338 (Archived by WebCite at http://www.webcitation.org/6ymHRBCwu) ©Victoria Frye, Louisa Duffy, Janis L France, Debra A Kessler, Mark Rebosa, Beth H Shaz, Bruce W Carlson, Christopher R. France. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 26.04.2018.

Problem Solving with General Semantics.

ERIC Educational Resources Information Center

Hewson, David

1996-01-01

Discusses how to use general semantics formulations to improve problem solving at home or at work--methods come from the areas of artificial intelligence/computer science, engineering, operations research, and psychology. (PA)

DOORS to the semantic web and grid with a PORTAL for biomedical computing.

PubMed

Taswell, Carl

2008-03-01

The semantic web remains in the early stages of development. It has not yet achieved the goals envisioned by its founders as a pervasive web of distributed knowledge and intelligence. Success will be attained when a dynamic synergism can be created between people and a sufficient number of infrastructure systems and tools for the semantic web in analogy with those for the original web. The domain name system (DNS), web browsers, and the benefits of publishing web pages motivated many people to register domain names and publish web sites on the original web. An analogous resource label system, semantic search applications, and the benefits of collaborative semantic networks will motivate people to register resource labels and publish resource descriptions on the semantic web. The Domain Ontology Oriented Resource System (DOORS) and Problem Oriented Registry of Tags and Labels (PORTAL) are proposed as infrastructure systems for resource metadata within a paradigm that can serve as a bridge between the original web and the semantic web. The Internet Registry Information Service (IRIS) registers [corrected] domain names while DNS publishes domain addresses with mapping of names to addresses for the original web. Analogously, PORTAL registers resource labels and tags while DOORS publishes resource locations and descriptions with mapping of labels to locations for the semantic web. BioPORT is proposed as a prototype PORTAL registry specific for the problem domain of biomedical computing.

Methods and apparatus for multi-resolution replication of files in a parallel computing system using semantic information

DOEpatents

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

2015-10-20

Techniques are provided for storing files in a parallel computing system using different resolutions. A method is provided for storing at least one file generated by a distributed application in a parallel computing system. The file comprises one or more of a complete file and a sub-file. The method comprises the steps of obtaining semantic information related to the file; generating a plurality of replicas of the file with different resolutions based on the semantic information; and storing the file and the plurality of replicas of the file in one or more storage nodes of the parallel computing system. The different resolutions comprise, for example, a variable number of bits and/or a different sub-set of data elements from the file. A plurality of the sub-files can be merged to reproduce the file.

Knowledge-Base Semantic Gap Analysis for the Vulnerability Detection

NASA Astrophysics Data System (ADS)

Wu, Raymond; Seki, Keisuke; Sakamoto, Ryusuke; Hisada, Masayuki

Web security became an alert in internet computing. To cope with ever-rising security complexity, semantic analysis is proposed to fill-in the gap that the current approaches fail to commit. Conventional methods limit their focus to the physical source codes instead of the abstraction of semantics. It bypasses new types of vulnerability and causes tremendous business loss.

Semantic Metrics for Analysis of Software

NASA Technical Reports Server (NTRS)

Etzkorn, Letha H.; Cox, Glenn W.; Farrington, Phil; Utley, Dawn R.; Ghalston, Sampson; Stein, Cara

2005-01-01

A recently conceived suite of object-oriented software metrics focus is on semantic aspects of software, in contradistinction to traditional software metrics, which focus on syntactic aspects of software. Semantic metrics represent a more human-oriented view of software than do syntactic metrics. The semantic metrics of a given computer program are calculated by use of the output of a knowledge-based analysis of the program, and are substantially more representative of software quality and more readily comprehensible from a human perspective than are the syntactic metrics.

Fusing literature and full network data improves disease similarity computation.

PubMed

Li, Ping; Nie, Yaling; Yu, Jingkai

2016-08-30

Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

Bibliography on the Semantics of Human Language. Linguistic Bibliography Series, No. 1.

ERIC Educational Resources Information Center

Hofmann, Thomas R.

This bibliography, prepared and stored on a computer, is intended to aid in locating works on the semantics of human language. As a reference bibliography, it presents as many different places and modes of publication as possible, and includes as wide a range of subjects as possible. It is intended to cover all of linguistic semantics, with…

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.

[Artificial intelligence meeting neuropsychology. Semantic memory in normal and pathological aging].

PubMed

Aimé, Xavier; Charlet, Jean; Maillet, Didier; Belin, Catherine

2015-03-01

Artificial intelligence (IA) is the subject of much research, but also many fantasies. It aims to reproduce human intelligence in its learning capacity, knowledge storage and computation. In 2014, the Defense Advanced Research Projects Agency (DARPA) started the restoring active memory (RAM) program that attempt to develop implantable technology to bridge gaps in the injured brain and restore normal memory function to people with memory loss caused by injury or disease. In another IA's field, computational ontologies (a formal and shared conceptualization) try to model knowledge in order to represent a structured and unambiguous meaning of the concepts of a target domain. The aim of these structures is to ensure a consensual understanding of their meaning and a univariant use (the same concept is used by all to categorize the same individuals). The first representations of knowledge in the AI's domain are largely based on model tests of semantic memory. This one, as a component of long-term memory is the memory of words, ideas, concepts. It is the only declarative memory system that resists so remarkably to the effects of age. In contrast, non-specific cognitive changes may decrease the performance of elderly in various events and instead report difficulties of access to semantic representations that affect the semantics stock itself. Some dementias, like semantic dementia and Alzheimer's disease, are linked to alteration of semantic memory. We propose in this paper, using the computational ontologies model, a formal and relatively thin modeling, in the service of neuropsychology: 1) for the practitioner with decision support systems, 2) for the patient as cognitive prosthesis outsourced, and 3) for the researcher to study semantic memory.

Building a Semantic Framework for eScience

NASA Astrophysics Data System (ADS)

Movva, S.; Ramachandran, R.; Maskey, M.; Li, X.

2009-12-01

The e-Science vision focuses on the use of advanced computing technologies to support scientists. Recent research efforts in this area have focused primarily on “enabling” use of infrastructure resources for both data and computational access especially in Geosciences. One of the existing gaps in the existing e-Science efforts has been the failure to incorporate stable semantic technologies within the design process itself. In this presentation, we describe our effort in designing a framework for e-Science built using Service Oriented Architecture. Our framework provides users capabilities to create science workflows and mine distributed data. Our e-Science framework is being designed around a mass market tool to promote reusability across many projects. Semantics is an integral part of this framework and our design goal is to leverage the latest stable semantic technologies. The use of these stable semantic technologies will provide the users of our framework the useful features such as: allow search engines to find their content with RDFa tags; create RDF triple data store for their content; create RDF end points to share with others; and semantically mash their content with other online content available as RDF end point.

Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.

PubMed

Cleary, Anne M; Ryals, Anthony J; Wagner, Samantha R

2016-01-01

Research suggests that a feature-matching process underlies cue familiarity-detection when cued recall with graphemic cues fails. When a test cue (e.g., potchbork) overlaps in graphemic features with multiple unrecalled studied items (e.g., patchwork, pitchfork, pocketbook, pullcork), higher cue familiarity ratings are given during recall failure of all of the targets than when the cue overlaps in graphemic features with only one studied target and that target fails to be recalled (e.g., patchwork). The present study used semantic feature production norms (McRae et al., Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005) to examine whether the same holds true when the cues are semantic in nature (e.g., jaguar is used to cue cheetah). Indeed, test cues (e.g., cedar) that overlapped in semantic features (e.g., a_tree, has_bark, etc.) with four unretrieved studied items (e.g., birch, oak, pine, willow) received higher cue familiarity ratings during recall failure than test cues that overlapped in semantic features with only two (also unretrieved) studied items (e.g., birch, oak), which in turn received higher familiarity ratings during recall failure than cues that did not overlap in semantic features with any studied items. These findings suggest that the feature-matching theory of recognition during recall failure can accommodate recognition of semantic cues during recall failure, providing a potential mechanism for conceptually-based forms of cue recognition during target retrieval failure. They also provide converging evidence for the existence of the semantic features envisaged in feature-based models of semantic knowledge representation and for those more concretely specified by the production norms of McRae et al. (Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005).

Interleaving Semantic Web Reasoning and Service Discovery to Enforce Context-Sensitive Security and Privacy Policies

DTIC Science & Technology

2005-07-01

policies in pervasive computing environments. In this context, the owner of information sources (e.g. user, sensor, application, or organization...work in decentralized trust management and semantic web technologies . Section 3 introduces an Information Disclosure Agent architecture for...Norman Sadeh July 2005 CMU-ISRI-05-113 School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA, 15213

Phonological ambiguity modulates resolution of semantic ambiguity during reading: An fMRI study of Hebrew.

PubMed

Bitan, Tali; Kaftory, Asaf; Meiri-Leib, Adi; Eviatar, Zohar; Peleg, Orna

2017-10-01

The current fMRI study examined the role of phonology in the extraction of meaning from print in each hemisphere by comparing homophonic and heterophonic homographs (ambiguous words in which both meanings have the same or different sounds respectively, e.g., bank or tear). The analysis distinguished between the first phase, in which participants read ambiguous words without context, and the second phase in which the context resolves the ambiguity. Native Hebrew readers were scanned during semantic relatedness judgments on pairs of words in which the first word was either a homophone or a heterophone and the second word was related to its dominant or subordinate meaning. In Phase 1 there was greater activation for heterophones in left inferior frontal gyrus (IFG), pars opercularis, and more activation for homophones in bilateral IFG pars orbitalis, suggesting that resolution of the conflict at the phonological level has abolished the semantic ambiguity for heterophones. Reduced activation for all ambiguous words in temporo-parietal regions suggests that although ambiguity enhances controlled lexical selection processes in frontal regions it reduces reliance on bottom-up mapping processes. After presentation of the context, a larger difference between the dominant and subordinate meaning was found for heterophones in all reading-related regions, suggesting a greater engagement for heterophones with the dominant meaning. Altogether these results are consistent with the prominent role of phonological processing in visual word recognition. Finally, despite differences in hemispheric asymmetry between homophones and heterophones, ambiguity resolution, even toward the subordinate meaning, is largely left lateralized. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Developing Visualization Techniques for Semantics-based Information Networks

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Hall, David R.

2003-01-01

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

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).

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

PubMed

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

2017-09-01

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

Mediator infrastructure for information integration and semantic data integration environment for biomedical research.

PubMed

Grethe, Jeffrey S; Ross, Edward; Little, David; Sanders, Brian; Gupta, Amarnath; Astakhov, Vadim

2009-01-01

This paper presents current progress in the development of semantic data integration environment which is a part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A goal is the development of a cyberinfrastructure for biomedical research that supports advance data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services over the Internet. Each participating institution maintains storage of their experimental or computationally derived data. Mediator-based data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institution's data. This paper describes recent revision of the system architecture, implementation, and capabilities of the semantically based data integration environment for BIRN.

The semantic web and computer vision: old AI meets new AI

NASA Astrophysics Data System (ADS)

Mundy, J. L.; Dong, Y.; Gilliam, A.; Wagner, R.

2018-04-01

There has been vast process in linking semantic information across the billions of web pages through the use of ontologies encoded in the Web Ontology Language (OWL) based on the Resource Description Framework (RDF). A prime example is the Wikipedia where the knowledge contained in its more than four million pages is encoded in an ontological database called DBPedia http://wiki.dbpedia.org/. Web-based query tools can retrieve semantic information from DBPedia encoded in interlinked ontologies that can be accessed using natural language. This paper will show how this vast context can be used to automate the process of querying images and other geospatial data in support of report changes in structures and activities. Computer vision algorithms are selected and provided with context based on natural language requests for monitoring and analysis. The resulting reports provide semantically linked observations from images and 3D surface models.

Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

PubMed

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

2014-08-01

We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models

PubMed Central

Chen, Han; Wang, Chaolong; Conomos, Matthew P.; Stilp, Adrienne M.; Li, Zilin; Sofer, Tamar; Szpiro, Adam A.; Chen, Wei; Brehm, John M.; Celedón, Juan C.; Redline, Susan; Papanicolaou, George J.; Thornton, Timothy A.; Laurie, Cathy C.; Rice, Kenneth; Lin, Xihong

2016-01-01

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM’s constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs. PMID:27018471

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.

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda

PubMed Central

Priedhorsky, Reid; Osthus, Dave; Daughton, Ashlynn R.; Moran, Kelly R.; Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.

2017-01-01

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease. PMID:28782059

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda.

PubMed

Priedhorsky, Reid; Osthus, Dave; Daughton, Ashlynn R; Moran, Kelly R; Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y

2017-01-01

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

Harmony Search Algorithm for Word Sense Disambiguation.

PubMed

Abed, Saad Adnan; Tiun, Sabrina; Omar, Nazlia

2015-01-01

Word Sense Disambiguation (WSD) is the task of determining which sense of an ambiguous word (word with multiple meanings) is chosen in a particular use of that word, by considering its context. A sentence is considered ambiguous if it contains ambiguous word(s). Practically, any sentence that has been classified as ambiguous usually has multiple interpretations, but just one of them presents the correct interpretation. We propose an unsupervised method that exploits knowledge based approaches for word sense disambiguation using Harmony Search Algorithm (HSA) based on a Stanford dependencies generator (HSDG). The role of the dependency generator is to parse sentences to obtain their dependency relations. Whereas, the goal of using the HSA is to maximize the overall semantic similarity of the set of parsed words. HSA invokes a combination of semantic similarity and relatedness measurements, i.e., Jiang and Conrath (jcn) and an adapted Lesk algorithm, to perform the HSA fitness function. Our proposed method was experimented on benchmark datasets, which yielded results comparable to the state-of-the-art WSD methods. In order to evaluate the effectiveness of the dependency generator, we perform the same methodology without the parser, but with a window of words. The empirical results demonstrate that the proposed method is able to produce effective solutions for most instances of the datasets used.

Harmony Search Algorithm for Word Sense Disambiguation

PubMed Central

Abed, Saad Adnan; Tiun, Sabrina; Omar, Nazlia

2015-01-01

Word Sense Disambiguation (WSD) is the task of determining which sense of an ambiguous word (word with multiple meanings) is chosen in a particular use of that word, by considering its context. A sentence is considered ambiguous if it contains ambiguous word(s). Practically, any sentence that has been classified as ambiguous usually has multiple interpretations, but just one of them presents the correct interpretation. We propose an unsupervised method that exploits knowledge based approaches for word sense disambiguation using Harmony Search Algorithm (HSA) based on a Stanford dependencies generator (HSDG). The role of the dependency generator is to parse sentences to obtain their dependency relations. Whereas, the goal of using the HSA is to maximize the overall semantic similarity of the set of parsed words. HSA invokes a combination of semantic similarity and relatedness measurements, i.e., Jiang and Conrath (jcn) and an adapted Lesk algorithm, to perform the HSA fitness function. Our proposed method was experimented on benchmark datasets, which yielded results comparable to the state-of-the-art WSD methods. In order to evaluate the effectiveness of the dependency generator, we perform the same methodology without the parser, but with a window of words. The empirical results demonstrate that the proposed method is able to produce effective solutions for most instances of the datasets used. PMID:26422368

Guidance of visual attention by semantic information in real-world scenes

PubMed Central

Wu, Chia-Chien; Wick, Farahnaz Ahmed; Pomplun, Marc

2014-01-01

Recent research on attentional guidance in real-world scenes has focused on object recognition within the context of a scene. This approach has been valuable for determining some factors that drive the allocation of visual attention and determine visual selection. This article provides a review of experimental work on how different components of context, especially semantic information, affect attentional deployment. We review work from the areas of object recognition, scene perception, and visual search, highlighting recent studies examining semantic structure in real-world scenes. A better understanding on how humans parse scene representations will not only improve current models of visual attention but also advance next-generation computer vision systems and human-computer interfaces. PMID:24567724

A computational language approach to modeling prose recall in schizophrenia

PubMed Central

Rosenstein, Mark; Diaz-Asper, Catherine; Foltz, Peter W.; Elvevåg, Brita

2014-01-01

Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes. We employ computational language approaches to assess time-varying semantic and sequential properties of prose recall at various retrieval intervals (immediate, 30 min and 24 h later) in patients with schizophrenia, unaffected siblings and healthy unrelated control participants. First, we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis (i.e., group membership) on performance. Next we model the human scoring of recall performance using an n-gram language sequence technique, and then with a semantic feature based on Latent Semantic Analysis. These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring. The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features. Taken individually, the semantic feature is most predictive, while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach. We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall. PMID:24709122

Encoding of low-quality DNA profiles as genotype probability matrices for improved profile comparisons, relatedness evaluation and database searches.

PubMed

Ryan, K; Williams, D Gareth; Balding, David J

2016-11-01

Many DNA profiles recovered from crime scene samples are of a quality that does not allow them to be searched against, nor entered into, databases. We propose a method for the comparison of profiles arising from two DNA samples, one or both of which can have multiple donors and be affected by low DNA template or degraded DNA. We compute likelihood ratios to evaluate the hypothesis that the two samples have a common DNA donor, and hypotheses specifying the relatedness of two donors. Our method uses a probability distribution for the genotype of the donor of interest in each sample. This distribution can be obtained from a statistical model, or we can exploit the ability of trained human experts to assess genotype probabilities, thus extracting much information that would be discarded by standard interpretation rules. Our method is compatible with established methods in simple settings, but is more widely applicable and can make better use of information than many current methods for the analysis of mixed-source, low-template DNA profiles. It can accommodate uncertainty arising from relatedness instead of or in addition to uncertainty arising from noisy genotyping. We describe a computer program GPMDNA, available under an open source licence, to calculate LRs using the method presented in this paper. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Research on Interactive Acquisition and Use of Knowledge.

DTIC Science & Technology

1983-11-01

complex and challenging endeavor. Computer scientists faced with the problem of managing software complexity have de - veloped strict design disciplines...handle most-indeed, probably all-- phenomena in the syntax and semantics of natural language. It has also turned out to be well suited for the classes of...Semantics The previous grammar performs a de facto coordination of syntax and semantics by requiring that the (syntactically) preverbal NP play the

Architecture for WSN Nodes Integration in Context Aware Systems Using Semantic Messages

NASA Astrophysics Data System (ADS)

Larizgoitia, Iker; Muguira, Leire; Vazquez, Juan Ignacio

Wireless sensor networks (WSN) are becoming extremely popular in the development of context aware systems. Traditionally WSN have been focused on capturing data, which was later analyzed and interpreted in a server with more computational power. In this kind of scenario the problem of representing the sensor information needs to be addressed. Every node in the network might have different sensors attached; therefore their correspondent packet structures will be different. The server has to be aware of the meaning of every single structure and data in order to be able to interpret them. Multiple sensors, multiple nodes, multiple packet structures (and not following a standard format) is neither scalable nor interoperable. Context aware systems have solved this problem with the use of semantic technologies. They provide a common framework to achieve a standard definition of any domain. Nevertheless, these representations are computationally expensive, so a WSN cannot afford them. The work presented in this paper tries to bridge the gap between the sensor information and its semantic representation, by defining a simple architecture that enables the definition of this information natively in a semantic way, achieving the integration of the semantic information in the network packets. This will have several benefits, the most important being the possibility of promoting every WSN node to a real semantic information source.

A Semantics of Synchronization.

DTIC Science & Technology

1980-09-01

suggestion of having very hungry philosophers. One can easily imagine the complexity of the equivalent implementation using semaphores . Synchronization types...Edinburgh, July 1978. [STAR79] Stark, E.W., " Semaphore Primitives and Fair Mutual Exclusion," TM-158, Laboratory for Computer Science, M.I.T., Cambridge...AD-AQ91 015 MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR COMPUTE--ETC F/S 9/2 A SEMANTICS OF SYNCHRONIZATION .(U) .C SEP 80 C A SEAQUIST N00015-75

The Formal Semantics of PVS

NASA Technical Reports Server (NTRS)

Owre, Sam; Shankar, Natarajan

1999-01-01

A specification language is a medium for expressing what is computed rather than how it is computed. Specification languages share some features with programming languages but are also different in several important ways. For our purpose, a specification language is a logic within which the behavior of computational systems can be formalized. Although a specification can be used to simulate the behavior of such systems, we mainly use specifications to state and prove system properties with mechanical assistance. We present the formal semantics of the specification language of SRI's Prototype Verification System (PVS). This specification language is based on the simply typed lambda calculus. The novelty in PVS is that it contains very expressive language features whose static analysis (e.g., typechecking) requires the assistance of a theorem prover. The formal semantics illuminates several of the design considerations underlying PVS, the interaction between theorem proving and typechecking.

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).

Neural correlates of strategic processes underlying episodic memory in women with major depression: A 15O-PET study.

PubMed

Ottowitz, William E; Deckersbach, Thilo; Savage, Cary R; Lindquist, Martin A; Dougherty, Darin D

2010-01-01

To evaluate the functional integrity of brain regions underlying strategic mnemonic processing in patients with major depressive disorder, the authors administered a modified version of the California Verbal Learning Test to depressed patients during presentation of lists of unrelated words and, conversely, during presentation of lists of related words with and without orientation regarding the relatedness of the words (eight healthy females, IQ=122, and eight depressed females, IQ=107). Brain function evaluated across all three conditions showed that patients with major depressive disorder revealed activation of the right anterior cingulate cortex, left ventrolateral prefrontal cortex, both hippocampi, and the left orbitofrontal cortex. Further analysis showed that patients with major depressive disorder had greater activation of the right anterior cingulate cortex during semantic organization and the right ventrolateral prefrontal cortex during strategy initiation.

Learning semantic and visual similarity for endomicroscopy video retrieval.

PubMed

Andre, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2012-06-01

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.

Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics.

PubMed

Marelli, Marco; Baroni, Marco

2015-07-01

The present work proposes a computational model of morpheme combination at the meaning level. The model moves from the tenets of distributional semantics, and assumes that word meanings can be effectively represented by vectors recording their co-occurrence with other words in a large text corpus. Given this assumption, affixes are modeled as functions (matrices) mapping stems onto derived forms. Derived-form meanings can be thought of as the result of a combinatorial procedure that transforms the stem vector on the basis of the affix matrix (e.g., the meaning of nameless is obtained by multiplying the vector of name with the matrix of -less). We show that this architecture accounts for the remarkable human capacity of generating new words that denote novel meanings, correctly predicting semantic intuitions about novel derived forms. Moreover, the proposed compositional approach, once paired with a whole-word route, provides a new interpretative framework for semantic transparency, which is here partially explained in terms of ease of the combinatorial procedure and strength of the transformation brought about by the affix. Model-based predictions are in line with the modulation of semantic transparency on explicit intuitions about existing words, response times in lexical decision, and morphological priming. In conclusion, we introduce a computational model to account for morpheme combination at the meaning level. The model is data-driven, theoretically sound, and empirically supported, and it makes predictions that open new research avenues in the domain of semantic processing. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Semantic ambiguity effects on traditional Chinese character naming: A corpus-based approach.

PubMed

Chang, Ya-Ning; Lee, Chia-Ying

2017-11-09

Words are considered semantically ambiguous if they have more than one meaning and can be used in multiple contexts. A number of recent studies have provided objective ambiguity measures by using a corpus-based approach and have demonstrated ambiguity advantages in both naming and lexical decision tasks. Although the predictive power of objective ambiguity measures has been examined in several alphabetic language systems, the effects in logographic languages remain unclear. Moreover, most ambiguity measures do not explicitly address how the various contexts associated with a given word relate to each other. To explore these issues, we computed the contextual diversity (Adelman, Brown, & Quesada, Psychological Science, 17; 814-823, 2006) and semantic ambiguity (Hoffman, Lambon Ralph, & Rogers, Behavior Research Methods, 45; 718-730, 2013) of traditional Chinese single-character words based on the Academia Sinica Balanced Corpus, where contextual diversity was used to evaluate the present semantic space. We then derived a novel ambiguity measure, namely semantic variability, by computing the distance properties of the distinct clusters grouped by the contexts that contained a given word. We demonstrated that semantic variability was superior to semantic diversity in accounting for the variance in naming response times, suggesting that considering the substructure of the various contexts associated with a given word can provide a relatively fine scale of ambiguity information for a word. All of the context and ambiguity measures for 2,418 Chinese single-character words are provided as supplementary materials.

Executive Semantic Processing Is Underpinned by a Large-scale Neural Network: Revealing the Contribution of Left Prefrontal, Posterior Temporal, and Parietal Cortex to Controlled Retrieval and Selection Using TMS

ERIC Educational Resources Information Center

Whitney, Carin; Kirk, Marie; O'Sullivan, Jamie; Ralph, Matthew A. Lambon; Jefferies, Elizabeth

2012-01-01

To understand the meanings of words and objects, we need to have knowledge about these items themselves plus executive mechanisms that compute and manipulate semantic information in a task-appropriate way. The neural basis for semantic control remains controversial. Neuroimaging studies have focused on the role of the left inferior frontal gyrus…

Learning for Semantic Parsing with Kernels under Various Forms of Supervision

DTIC Science & Technology

2007-08-01

natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing...sentences are semantically tractable. This indi- cates that Geoquery is more challenging domain for semantic parsing than ATIS. In the past, there have been a...Combining parsers. In Proceedings of the Conference on Em- pirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/ VLC -99), pp. 187–194

Approaching semantic interoperability in Health Level Seven

PubMed Central

Alschuler, Liora

2010-01-01

‘Semantic Interoperability’ is a driving objective behind many of Health Level Seven's standards. The objective in this paper is to take a step back, and consider what semantic interoperability means, assess whether or not it has been achieved, and, if not, determine what concrete next steps can be taken to get closer. A framework for measuring semantic interoperability is proposed, using a technique called the ‘Single Logical Information Model’ framework, which relies on an operational definition of semantic interoperability and an understanding that interoperability improves incrementally. Whether semantic interoperability tomorrow will enable one computer to talk to another, much as one person can talk to another person, is a matter for speculation. It is assumed, however, that what gets measured gets improved, and in that spirit this framework is offered as a means to improvement. PMID:21106995

Structure, function, and behaviour of computational models in systems biology

PubMed Central

2013-01-01

Background Systems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such “bio-models” necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. Results We present a conceptual framework – the meaning facets – which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model’s components (structure), the meaning of the model’s intended use (function), and the meaning of the model’s dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces. Conclusions The meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research. PMID:23721297

What is in a contour map? A region-based logical formalization of contour semantics

USGS Publications Warehouse

Usery, E. Lynn; Hahmann, Torsten

2015-01-01

This paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours.

SemVisM: semantic visualizer for medical image

NASA Astrophysics Data System (ADS)

Landaeta, Luis; La Cruz, Alexandra; Baranya, Alexander; Vidal, María.-Esther

2015-01-01

SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically, combining rendering techniques, and segmentation algorithms. SemVisM comprises two main components: i) AMORE (A Modest vOlume REgister) to handle input data (RAW, DAT or DICOM) and to initially annotate the images using terms defined on medical ontologies (e.g., MesH, FMA or RadLex), and ii) VOLPROB (VOlume PRObability Builder) for generating the annotated volumetric data containing the classified voxels that belong to a particular tissue. SemVisM is built on top of the semantic visualizer ANISE.1

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.

SLEEC: Semantics-Rich Libraries for Effective Exascale Computation. Final Technical Report

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

Milind, Kulkarni

SLEEC (Semantics-rich Libraries for Effective Exascale Computation) was a project funded by the Department of Energy X-Stack Program, award number DE-SC0008629. The initial project period was September 2012–August 2015. The project was renewed for an additional year, expiring August 2016. Finally, the project received a no-cost extension, leading to a final expiry date of August 2017. Modern applications, especially those intended to run at exascale, are not written from scratch. Instead, they are built by stitching together various carefully-written, hand-tuned libraries. Correctly composing these libraries is difficult, but traditional compilers are unable to effectively analyze and transform across abstraction layers.more » Domain specific compilers integrate semantic knowledge into compilers, allowing them to transform applications that use particular domain-specific languages, or domain libraries. But they do not help when new domains are developed, or applications span multiple domains. SLEEC aims to fix these problems. To do so, we are building generic compiler and runtime infrastructures that are semantics-aware but not domain-specific. By performing optimizations related to the semantics of a domain library, the same infrastructure can be made generic and apply across multiple domains.« less

Semantic Similarity between Web Documents Using Ontology

NASA Astrophysics Data System (ADS)

Chahal, Poonam; Singh Tomer, Manjeet; Kumar, Suresh

2018-06-01

The World Wide Web is the source of information available in the structure of interlinked web pages. However, the procedure of extracting significant information with the assistance of search engine is incredibly critical. This is for the reason that web information is written mainly by using natural language, and further available to individual human. Several efforts have been made in semantic similarity computation between documents using words, concepts and concepts relationship but still the outcome available are not as per the user requirements. This paper proposes a novel technique for computation of semantic similarity between documents that not only takes concepts available in documents but also relationships that are available between the concepts. In our approach documents are being processed by making ontology of the documents using base ontology and a dictionary containing concepts records. Each such record is made up of the probable words which represents a given concept. Finally, document ontology's are compared to find their semantic similarity by taking the relationships among concepts. Relevant concepts and relations between the concepts have been explored by capturing author and user intention. The proposed semantic analysis technique provides improved results as compared to the existing techniques.

Building a biomedical semantic network in Wikipedia with Semantic Wiki Links

PubMed Central

Good, Benjamin M.; Clarke, Erik L.; Loguercio, Salvatore; Su, Andrew I.

2012-01-01

Wikipedia is increasingly used as a platform for collaborative data curation, but its current technical implementation has significant limitations that hinder its use in biocuration applications. Specifically, while editors can easily link between two articles in Wikipedia to indicate a relationship, there is no way to indicate the nature of that relationship in a way that is computationally accessible to the system or to external developers. For example, in addition to noting a relationship between a gene and a disease, it would be useful to differentiate the cases where genetic mutation or altered expression causes the disease. Here, we introduce a straightforward method that allows Wikipedia editors to embed computable semantic relations directly in the context of current Wikipedia articles. In addition, we demonstrate two novel applications enabled by the presence of these new relationships. The first is a dynamically generated information box that can be rendered on all semantically enhanced Wikipedia articles. The second is a prototype gene annotation system that draws its content from the gene-centric articles on Wikipedia and exposes the new semantic relationships to enable previously impossible, user-defined queries. Database URL: http://en.wikipedia.org/wiki/Portal:Gene_Wiki PMID:22434829

Building a biomedical semantic network in Wikipedia with Semantic Wiki Links.

PubMed

Good, Benjamin M; Clarke, Erik L; Loguercio, Salvatore; Su, Andrew I

2012-01-01

Wikipedia is increasingly used as a platform for collaborative data curation, but its current technical implementation has significant limitations that hinder its use in biocuration applications. Specifically, while editors can easily link between two articles in Wikipedia to indicate a relationship, there is no way to indicate the nature of that relationship in a way that is computationally accessible to the system or to external developers. For example, in addition to noting a relationship between a gene and a disease, it would be useful to differentiate the cases where genetic mutation or altered expression causes the disease. Here, we introduce a straightforward method that allows Wikipedia editors to embed computable semantic relations directly in the context of current Wikipedia articles. In addition, we demonstrate two novel applications enabled by the presence of these new relationships. The first is a dynamically generated information box that can be rendered on all semantically enhanced Wikipedia articles. The second is a prototype gene annotation system that draws its content from the gene-centric articles on Wikipedia and exposes the new semantic relationships to enable previously impossible, user-defined queries. DATABASE URL: http://en.wikipedia.org/wiki/Portal:Gene_Wiki.

Interests diffusion on a semantic multiplex. Comparing Computer Science and American Physical Society communities

NASA Astrophysics Data System (ADS)

D'Agostino, Gregorio; De Nicola, Antonio

2016-10-01

Exploiting the information about members of a Social Network (SN) represents one of the most attractive and dwelling subjects for both academic and applied scientists. The community of Complexity Science and especially those researchers working on multiplex social systems are devoting increasing efforts to outline general laws, models, and theories, to the purpose of predicting emergent phenomena in SN's (e.g. success of a product). On the other side the semantic web community aims at engineering a new generation of advanced services tailored to specific people needs. This implies defining constructs, models and methods for handling the semantic layer of SNs. We combined models and techniques from both the former fields to provide a hybrid approach to understand a basic (yet complex) phenomenon: the propagation of individual interests along the social networks. Since information may move along different social networks, one should take into account a multiplex structure. Therefore we introduced the notion of "Semantic Multiplex". In this paper we analyse two different semantic social networks represented by authors publishing in the Computer Science and those in the American Physical Society Journals. The comparison allows to outline common and specific features.

Semantic Similarity between Web Documents Using Ontology

NASA Astrophysics Data System (ADS)

Chahal, Poonam; Singh Tomer, Manjeet; Kumar, Suresh

2018-03-01

The World Wide Web is the source of information available in the structure of interlinked web pages. However, the procedure of extracting significant information with the assistance of search engine is incredibly critical. This is for the reason that web information is written mainly by using natural language, and further available to individual human. Several efforts have been made in semantic similarity computation between documents using words, concepts and concepts relationship but still the outcome available are not as per the user requirements. This paper proposes a novel technique for computation of semantic similarity between documents that not only takes concepts available in documents but also relationships that are available between the concepts. In our approach documents are being processed by making ontology of the documents using base ontology and a dictionary containing concepts records. Each such record is made up of the probable words which represents a given concept. Finally, document ontology's are compared to find their semantic similarity by taking the relationships among concepts. Relevant concepts and relations between the concepts have been explored by capturing author and user intention. The proposed semantic analysis technique provides improved results as compared to the existing techniques.

A Computational Analysis of Complex Noun Phrases in Navy Messages

DTIC Science & Technology

1984-07-01

Hirschman. Automated Determination of Suhlanguage Syntactic Usage. Proc. COLING 84) (current volume). [Hirschman 1082 ] Hirsehman, L. Constraints on...Restricted Semantic Domains. de Grnyter New York, 1082 . [Levi 1078] Levi, J.N. The Syntaz and Semantics of Com- plez Nominals, Academic Press, New York

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.

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

PubMed

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

2016-05-15

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

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.

Semantic encoding of relational databases in wireless networks

NASA Astrophysics Data System (ADS)

Benjamin, David P.; Walker, Adrian

2005-03-01

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

Interaction between Phonological and Semantic Representations: Time Matters

ERIC Educational Resources Information Center

Chen, Qi; Mirman, Daniel

2015-01-01

Computational modeling and eye-tracking were used to investigate how phonological and semantic information interact to influence the time course of spoken word recognition. We extended our recent models (Chen & Mirman, 2012; Mirman, Britt, & Chen, 2013) to account for new evidence that competition among phonological neighbors influences…

Semantic Processing for Communicative Exercises in Foreign-Language Learning.

ERIC Educational Resources Information Center

Mulford, George W.

1989-01-01

Outlines the history of semantically based programs that have influenced the design of computer assisted language instruction (CALI) programs. Describes early attempts to make intelligent CALI as well as current projects, including the Foreign Language Adventure Game, developed at the University of Delaware. Describes some important…

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.

Enhancing prospective memory in mild cognitive impairment: The role of enactment.

PubMed

Pereira, Antonina; de Mendonça, Alexandre; Silva, Dina; Guerreiro, Manuela; Freeman, Jayne; Ellis, Judi

2015-01-01

Prospective memory (PM) is a fundamental requirement for independent living which might be prematurely compromised in the neurodegenerative process, namely in mild cognitive impairment (MCI), a typical prodromal Alzheimer's disease (AD) phase. Most encoding manipulations that typically enhance learning in healthy adults are of minimal benefit to AD patients. However, there is some indication that these can display a recall advantage when encoding is accompanied by the physical enactment of the material. The aim of this study was to explore the potential benefits of enactment at encoding and cue-action relatedness on memory for intentions in MCI patients and healthy controls using a behavioral PM experimental paradigm. We report findings examining the influence of enactment at encoding for PM performance in MCI patients and age- and education-matched controls using a laboratory-based PM task with a factorial independent design. PM performance was consistently superior when physical enactment was used at encoding and when target-action pairs were strongly associated. Importantly, these beneficial effects were cumulative and observable across both a healthy and a cognitively impaired lifespan as well as evident in the perceived subjective difficulty in performing the task. The identified beneficial effects of enacted encoding and semantic relatedness have unveiled the potential contribution of this encoding technique to optimize attentional demands through an adaptive allocation of strategic resources. We discuss our findings with respect to their potential impact on developing strategies to improve PM in AD sufferers.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models.

PubMed

Chen, Han; Wang, Chaolong; Conomos, Matthew P; Stilp, Adrienne M; Li, Zilin; Sofer, Tamar; Szpiro, Adam A; Chen, Wei; Brehm, John M; Celedón, Juan C; Redline, Susan; Papanicolaou, George J; Thornton, Timothy A; Laurie, Cathy C; Rice, Kenneth; Lin, Xihong

2016-04-07

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs. Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Semantic similarity measure in biomedical domain leverage web search engine.

PubMed

Chen, Chi-Huang; Hsieh, Sheau-Ling; Weng, Yung-Ching; Chang, Wen-Yung; Lai, Feipei

2010-01-01

Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.

F-OWL: An Inference Engine for Semantic Web

NASA Technical Reports Server (NTRS)

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

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.

Quantifying Narrative Ability in Autism Spectrum Disorder: A Computational Linguistic Analysis of Narrative Coherence

PubMed Central

Losh, Molly; Gordon, Peter C.

2014-01-01

Autism Spectrum Disorder (ASD) is characterized by difficulties with social communication and functioning, and ritualistic/repetitive behaviors (American Psychiatric Association, 2013). While substantial heterogeneity exists in symptom expression, impairments in language discourse skills, including narrative, are universally observed (Tager-Flusberg, Paul, & Lord, 2005). This study applied a computational linguistic tool, Latent Semantic Analysis (LSA), to objectively characterize narrative performance in ASD across two narrative contexts differing in interpersonal and cognitive demands. Results indicated that individuals with ASD produced narratives comparable in semantic content to those from controls when narrating from a picture book, but produced narratives diminished in semantic quality in a more demanding narrative recall task. Results are discussed in terms of the utility of LSA as a quantitative, objective, and efficient measure of narrative ability. PMID:24915929

Individual differences in white matter microstructure predict semantic control.

PubMed

Nugiel, Tehila; Alm, Kylie H; Olson, Ingrid R

2016-12-01

In everyday conversation, we make many rapid choices between competing concepts and words in order to convey our intent. This process is termed semantic control, and it is thought to rely on information transmission between a distributed semantic store in the temporal lobes and a more discrete region, optimized for retrieval and selection, in the left inferior frontal gyrus. Here, we used diffusion tensor imaging in a group of neurologically normal young adults to investigate the relationship between semantic control and white matter tracts that have been implicated in semantic memory retrieval. Participants completed a verb generation task that taps semantic control (Snyder & Munakata, 2008; Snyder et al., 2010) and underwent a diffusion imaging scan. Deterministic tractography was performed to compute indices representing the microstructural properties of the inferior fronto-occipital fasciculus (IFOF), the uncinate fasciculus (UF), and the inferior longitudinal fasciculus (ILF). Microstructural measures of the UF failed to predict semantic control performance. However, there was a significant relationship between microstructure of the left IFOF and ILF and individual differences in semantic control. Our findings support the view put forth by Duffau (2013) that the IFOF is a key structural pathway in semantic retrieval.

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.

Latent semantic analysis.

PubMed

Evangelopoulos, Nicholas E

2013-11-01

This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2013 John Wiley & Sons, Ltd.

The influence of multiple trials and computer-mediated communication on collaborative and individual semantic recall.

PubMed

Hinds, Joanne M; Payne, Stephen J

2018-04-01

Collaborative inhibition is a phenomenon where collaborating groups experience a decrement in recall when interacting with others. Despite this, collaboration has been found to improve subsequent individual recall. We explore these effects in semantic recall, which is seldom studied in collaborative retrieval. We also examine "parallel CMC", a synchronous form of computer-mediated communication that has previously been found to improve collaborative recall [Hinds, J. M., & Payne, S. J. (2016). Collaborative inhibition and semantic recall: Improving collaboration through computer-mediated communication. Applied Cognitive Psychology, 30(4), 554-565]. Sixty three triads completed a semantic recall task, which involved generating words beginning with "PO" or "HE" across three recall trials, in one of three retrieval conditions: Individual-Individual-Individual (III), Face-to-face-Face-to-Face-Individual (FFI) and Parallel-Parallel-Individual (PPI). Collaborative inhibition was present across both collaborative conditions. Individual recall in Recall 3 was higher when participants had previously collaborated in comparison to recalling three times individually. There was no difference between face-to-face and parallel CMC recall, however subsidiary analyses of instance repetitions and subjective organisation highlighted differences in group members' approaches to recall in terms of organisation and attention to others' contributions. We discuss the implications of these findings in relation to retrieval strategy disruption.

Development of Category-based Induction and Semantic Knowledge

ERIC Educational Resources Information Center

Fisher, Anna V.; Godwin, Karrie E.; Matlen, Bryan J.; Unger, Layla

2015-01-01

Category-based induction is a hallmark of mature cognition; however, little is known about its origins. This study evaluated the hypothesis that category-based induction is related to semantic development. Computational studies suggest that early on there is little differentiation among concepts, but learning and development lead to increased…

Towards Text Copyright Detection Using Metadata in Web Applications

ERIC Educational Resources Information Center

Poulos, Marios; Korfiatis, Nikolaos; Bokos, George

2011-01-01

Purpose: This paper aims to present the semantic content identifier (SCI), a permanent identifier, computed through a linear-time onion-peeling algorithm that enables the extraction of semantic features from a text, and the integration of this information within the permanent identifier. Design/methodology/approach: The authors employ SCI to…

Computer Programs for the Semantic Differential: Further Modifications.

ERIC Educational Resources Information Center

Lawson, Edwin D.; And Others

The original nine programs for semantic differential analysis have been condensed into three programs which have been further refined and augmented. They yield: (1) means, standard deviations, and standard errors for each subscale on each concept; (2) Evaluation, Potency, and Activity (EPA) means, standard deviations, and standard errors; (3)…

Parametric Effects of Syntactic-Semantic Conflict in Broca's Area during Sentence Processing

ERIC Educational Resources Information Center

Thothathiri, Malathi; Kim, Albert; Trueswell, John C.; Thompson-Schill, Sharon L.

2012-01-01

The hypothesized role of Broca's area in sentence processing ranges from domain-general executive function to domain-specific computation that is specific to certain syntactic structures. We examined this issue by manipulating syntactic structure and conflict between syntactic and semantic cues in a sentence processing task. Functional…

The semantics of Chemical Markup Language (CML) for computational chemistry : CompChem.

PubMed

Phadungsukanan, Weerapong; Kraft, Markus; Townsend, Joe A; Murray-Rust, Peter

2012-08-07

: This paper introduces a subdomain chemistry format for storing computational chemistry data called CompChem. It has been developed based on the design, concepts and methodologies of Chemical Markup Language (CML) by adding computational chemistry semantics on top of the CML Schema. The format allows a wide range of ab initio quantum chemistry calculations of individual molecules to be stored. These calculations include, for example, single point energy calculation, molecular geometry optimization, and vibrational frequency analysis. The paper also describes the supporting infrastructure, such as processing software, dictionaries, validation tools and database repositories. In addition, some of the challenges and difficulties in developing common computational chemistry dictionaries are discussed. The uses of CompChem are illustrated by two practical applications.

The semantics of Chemical Markup Language (CML) for computational chemistry : CompChem

PubMed Central

2012-01-01

This paper introduces a subdomain chemistry format for storing computational chemistry data called CompChem. It has been developed based on the design, concepts and methodologies of Chemical Markup Language (CML) by adding computational chemistry semantics on top of the CML Schema. The format allows a wide range of ab initio quantum chemistry calculations of individual molecules to be stored. These calculations include, for example, single point energy calculation, molecular geometry optimization, and vibrational frequency analysis. The paper also describes the supporting infrastructure, such as processing software, dictionaries, validation tools and database repositories. In addition, some of the challenges and difficulties in developing common computational chemistry dictionaries are discussed. The uses of CompChem are illustrated by two practical applications. PMID:22870956

Impact of ontology evolution on functional analyses.

PubMed

Groß, Anika; Hartung, Michael; Prüfer, Kay; Kelso, Janet; Rahm, Erhard

2012-10-15

Ontologies are used in the annotation and analysis of biological data. As knowledge accumulates, ontologies and annotation undergo constant modifications to reflect this new knowledge. These modifications may influence the results of statistical applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. Here, we investigate to what degree modifications of the Gene Ontology (GO) impact these statistical analyses for both experimental and simulated data. The analysis is based on new measures for the stability of result sets and considers different ontology and annotation changes. Our results show that past changes in the GO are non-uniformly distributed over different branches of the ontology. Considering the semantic relatedness of significant categories in analysis results allows a more realistic stability assessment for functional enrichment studies. We observe that the results of term-enrichment analyses tend to be surprisingly stable despite changes in ontology and annotation.

Graded effects of regularity in language revealed by N400 indices of morphological priming.

PubMed

Kielar, Aneta; Joanisse, Marc F

2010-07-01

Differential electrophysiological effects for regular and irregular linguistic forms have been used to support the theory that grammatical rules are encoded using a dedicated cognitive mechanism. The alternative hypothesis is that language systematicities are encoded probabilistically in a way that does not categorically distinguish rule-like and irregular forms. In the present study, this matter was investigated more closely by focusing specifically on whether the regular-irregular distinction in English past tenses is categorical or graded. We compared the ERP priming effects of regulars (baked-bake), vowel-change irregulars (sang-sing), and "suffixed" irregulars that display a partial regularity (suffixed irregular verbs, e.g., slept-sleep), as well as forms that are related strictly along formal or semantic dimensions. Participants performed a visual lexical decision task with either visual (Experiment 1) or auditory prime (Experiment 2). Stronger N400 priming effects were observed for regular than vowel-change irregular verbs, whereas suffixed irregulars tended to group with regular verbs. Subsequent analyses decomposed early versus late-going N400 priming, and suggested that differences among forms can be attributed to the orthographic similarity of prime and target. Effects of morphological relatedness were observed in the later-going time period, however, we failed to observe true regular-irregular dissociations in either experiment. The results indicate that morphological effects emerge from the interaction of orthographic, phonological, and semantic overlap between words.

Computer Technology, Large-Scale Social Integration, and the Local Community.

ERIC Educational Resources Information Center

Calhoun, Craig

1986-01-01

A conceptual framework is proposed for studying variations in kind and extent of social integration and relatedness, such as those new communication technology may foster. Emphasis is on the contrast between direct and indirect social relationships. The framework is illustrated by consideration of potential social impacts of widespread…

Assessing Knowledge Change in Computer Science

ERIC Educational Resources Information Center

Nash, Jane Gradwohl; Bravaco, Ralph J.; Simonson, Shai

2006-01-01

The purpose of this study was to assess structural knowledge change across a two-week workshop designed to provide high-school teachers with training in Java and Object Oriented Programming. Both before and after the workshop, teachers assigned relatedness ratings to pairs of key concepts regarding Java and Object Oriented Programming. Their…

Rule-based support system for multiple UMLS semantic type assignments

PubMed Central

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

2012-01-01

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

Accelerating Cancer Systems Biology Research through Semantic Web Technology

PubMed Central

Wang, Zhihui; Sagotsky, Jonathan; Taylor, Thomas; Shironoshita, Patrick; Deisboeck, Thomas S.

2012-01-01

Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute’s caBIG®, so users can not only interact with the DMR through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers’ intellectual property. PMID:23188758

Accelerating cancer systems biology research through Semantic Web technology.

PubMed

Wang, Zhihui; Sagotsky, Jonathan; Taylor, Thomas; Shironoshita, Patrick; Deisboeck, Thomas S

2013-01-01

Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute's caBIG, so users can interact with the DMR not only through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers' intellectual property. Copyright © 2012 Wiley Periodicals, Inc.

From data to analysis: linking NWChem and Avogadro with the syntax and semantics of Chemical Markup Language.

PubMed

de Jong, Wibe A; Walker, Andrew M; Hanwell, Marcus D

2013-05-24

Multidisciplinary integrated research requires the ability to couple the diverse sets of data obtained from a range of complex experiments and computer simulations. Integrating data requires semantically rich information. In this paper an end-to-end use of semantically rich data in computational chemistry is demonstrated utilizing the Chemical Markup Language (CML) framework. Semantically rich data is generated by the NWChem computational chemistry software with the FoX library and utilized by the Avogadro molecular editor for analysis and visualization. The NWChem computational chemistry software has been modified and coupled to the FoX library to write CML compliant XML data files. The FoX library was expanded to represent the lexical input files and molecular orbitals used by the computational chemistry software. Draft dictionary entries and a format for molecular orbitals within CML CompChem were developed. The Avogadro application was extended to read in CML data, and display molecular geometry and electronic structure in the GUI allowing for an end-to-end solution where Avogadro can create input structures, generate input files, NWChem can run the calculation and Avogadro can then read in and analyse the CML output produced. The developments outlined in this paper will be made available in future releases of NWChem, FoX, and Avogadro. The production of CML compliant XML files for computational chemistry software such as NWChem can be accomplished relatively easily using the FoX library. The CML data can be read in by a newly developed reader in Avogadro and analysed or visualized in various ways. A community-based effort is needed to further develop the CML CompChem convention and dictionary. This will enable the long-term goal of allowing a researcher to run simple "Google-style" searches of chemistry and physics and have the results of computational calculations returned in a comprehensible form alongside articles from the published literature.

From data to analysis: linking NWChem and Avogadro with the syntax and semantics of Chemical Markup Language

PubMed Central

2013-01-01

Background Multidisciplinary integrated research requires the ability to couple the diverse sets of data obtained from a range of complex experiments and computer simulations. Integrating data requires semantically rich information. In this paper an end-to-end use of semantically rich data in computational chemistry is demonstrated utilizing the Chemical Markup Language (CML) framework. Semantically rich data is generated by the NWChem computational chemistry software with the FoX library and utilized by the Avogadro molecular editor for analysis and visualization. Results The NWChem computational chemistry software has been modified and coupled to the FoX library to write CML compliant XML data files. The FoX library was expanded to represent the lexical input files and molecular orbitals used by the computational chemistry software. Draft dictionary entries and a format for molecular orbitals within CML CompChem were developed. The Avogadro application was extended to read in CML data, and display molecular geometry and electronic structure in the GUI allowing for an end-to-end solution where Avogadro can create input structures, generate input files, NWChem can run the calculation and Avogadro can then read in and analyse the CML output produced. The developments outlined in this paper will be made available in future releases of NWChem, FoX, and Avogadro. Conclusions The production of CML compliant XML files for computational chemistry software such as NWChem can be accomplished relatively easily using the FoX library. The CML data can be read in by a newly developed reader in Avogadro and analysed or visualized in various ways. A community-based effort is needed to further develop the CML CompChem convention and dictionary. This will enable the long-term goal of allowing a researcher to run simple “Google-style” searches of chemistry and physics and have the results of computational calculations returned in a comprehensible form alongside articles from the published literature. PMID:23705910

tDCS over the motor cortex improves lexical retrieval of action words in poststroke aphasia.

PubMed

Branscheidt, Meret; Hoppe, Julia; Zwitserlood, Pienie; Liuzzi, Gianpiero

2018-02-01

One-third of stroke survivors worldwide suffer from aphasia. Speech and language therapy (SLT) is considered effective in treating aphasia, but because of time constraints, improvements are often limited. Noninvasive brain stimulation is a promising adjuvant strategy to facilitate SLT. However, stroke might render "classical" language regions ineffective as stimulation sites. Recent work showed the effectiveness of motor cortex stimulation together with intensive naming therapy to improve outcomes in aphasia (Meinzer et al. 2016). Although that study highlights the involvement of the motor cortex, the functional aspects by which it influences language remain unclear. In the present study, we focus on the role of motor cortex in language, investigating its functional involvement in access to specific lexico-semantic (object vs. action relatedness) information in poststroke aphasia. To this end, we tested effects of anodal transcranial direct current stimulation (tDCS) to the left motor cortex on lexical retrieval in 16 patients with poststroke aphasia in a sham-controlled, double-blind study design. Critical stimuli were action and object words, and pseudowords. Participants performed a lexical decision task, deciding whether stimuli were words or pseudowords. Anodal tDCS improved accuracy in lexical decision, especially for words with action-related content and for pseudowords with an "action-like" ending ( t 15  = 2.65, P = 0.036), but not for words with object-related content and pseudowords with "object-like" characteristics. We show as a proof-of-principle that the motor cortex may play a specific role in access to lexico-semantic content. Thus motor-cortex stimulation may strengthen content-specific word-to-semantic concept associations during language treatment in poststroke aphasia. NEW & NOTEWORTHY The role of motor cortex (MC) in language processing has been debated in both health and disease. Recent work has suggested that MC stimulation together with speech and language therapy enhances outcomes in aphasia. We show that MC stimulation has a differential effect on object- and action-word processing in poststroke aphasia. We propose that MC stimulation may specifically strengthen word-to-semantic concept association in aphasia. Our results potentially provide a way to tailor therapies for language rehabilitation.

An Interactive Multimedia Learning Environment for VLSI Built with COSMOS

ERIC Educational Resources Information Center

Angelides, Marios C.; Agius, Harry W.

2002-01-01

This paper presents Bigger Bits, an interactive multimedia learning environment that teaches students about VLSI within the context of computer electronics. The system was built with COSMOS (Content Oriented semantic Modelling Overlay Scheme), which is a modelling scheme that we developed for enabling the semantic content of multimedia to be used…

A Schema Theory Account of Some Cognitive Processes in Complex Learning. Technical Report No. 81.

ERIC Educational Resources Information Center

Munro, Allen; Rigney, Joseph W.

Procedural semantics models have diminished the distinction between data structures and procedures in computer simulations of human intelligence. This development has theoretical consequences for models of cognition. One type of procedural semantics model, called schema theory, is presented, and a variety of cognitive processes are explained in…

Word maturity indices with latent semantic analysis: why, when, and where is Procrustes rotation applied?

PubMed

Jorge-Botana, Guillermo; Olmos, Ricardo; Luzón, José M

2018-01-01

The aim of this paper is to describe and explain one useful computational methodology to model the semantic development of word representation: Word maturity. In particular, the methodology is based on the longitudinal word monitoring created by Kirylev and Landauer using latent semantic analysis for the representation of lexical units. The paper is divided into two parts. First, the steps required to model the development of the meaning of words are explained in detail. We describe the technical and theoretical aspects of each step. Second, we provide a simple example of application of this methodology with some simple tools that can be used by applied researchers. This paper can serve as a user-friendly guide for researchers interested in modeling changes in the semantic representations of words. Some current aspects of the technique and future directions are also discussed. WIREs Cogn Sci 2018, 9:e1457. doi: 10.1002/wcs.1457 This article is categorized under: Computer Science > Natural Language Processing Linguistics > Language Acquisition Psychology > Development and Aging. © 2017 Wiley Periodicals, Inc.

Visual analytics for semantic queries of TerraSAR-X image content

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

UBioLab: a web-laboratory for ubiquitous in-silico experiments.

PubMed

Bartocci, Ezio; Cacciagrano, Diletta; Di Berardini, Maria Rita; Merelli, Emanuela; Vito, Leonardo

2012-07-09

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.

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

Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD.

PubMed

Bullinaria, John A; Levy, Joseph P

2012-09-01

In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.

Computerized Analysis of Verbal Fluency: Normative Data and the Effects of Repeated Testing, Simulated Malingering, and Traumatic Brain Injury

PubMed Central

Wyma, John M.; Herron, Timothy J.; Yund, E. William

2016-01-01

In verbal fluency (VF) tests, subjects articulate words in a specified category during a short test period (typically 60 s). Verbal fluency tests are widely used to study language development and to evaluate memory retrieval in neuropsychiatric disorders. Performance is usually measured as the total number of correct words retrieved. Here, we describe the properties of a computerized VF (C-VF) test that tallies correct words and repetitions while providing additional lexical measures of word frequency, syllable count, and typicality. In addition, the C-VF permits (1) the analysis of the rate of responding over time, and (2) the analysis of the semantic relationships between words using a new method, Explicit Semantic Analysis (ESA), as well as the established semantic clustering and switching measures developed by Troyer et al. (1997). In Experiment 1, we gathered normative data from 180 subjects ranging in age from 18 to 82 years in semantic (“animals”) and phonemic (letter “F”) conditions. The number of words retrieved in 90 s correlated with education and daily hours of computer-use. The rate of word production declined sharply over time during both tests. In semantic conditions, correct-word scores correlated strongly with the number of ESA and Troyer-defined semantic switches as well as with an ESA-defined semantic organization index (SOI). In phonemic conditions, ESA revealed significant semantic influences in the sequence of words retrieved. In Experiment 2, we examined the test-retest reliability of different measures across three weekly tests in 40 young subjects. Different categories were used for each semantic (“animals”, “parts of the body”, and “foods”) and phonemic (letters “F”, “A”, and “S”) condition. After regressing out the influences of education and computer-use, we found that correct-word z-scores in the first session did not differ from those of the subjects in Experiment 1. Word production was uniformly greater in semantic than phonemic conditions. Intraclass correlation coefficients (ICCs) of correct-word z-scores were higher for phonemic (0.91) than semantic (0.77) tests. In semantic conditions, good reliability was also seen for the SOI (ICC = 0.68) and ESA-defined switches in semantic categories (ICC = 0.62). In Experiment 3, we examined the performance of subjects from Experiment 2 when instructed to malinger: 38% showed abnormal (p< 0.05) performance in semantic conditions. Simulated malingerers with abnormal scores could be distinguished with 80% sensitivity and 89% specificity from subjects with abnormal scores in Experiment 1 using lexical, temporal, and semantic measures. In Experiment 4, we tested patients with mild and severe traumatic brain injury (mTBI and sTBI). Patients with mTBI performed within the normal range, while patients with sTBI showed significant impairments in correct-word z-scores and category shifts. The lexical, temporal, and semantic measures of the C-VF provide an automated and comprehensive description of verbal fluency performance. PMID:27936001

Facilitation and interference in naming: A consequence of the same learning process?

PubMed

Hughes, Julie W; Schnur, Tatiana T

2017-08-01

Our success with naming depends on what we have named previously, a phenomenon thought to reflect learning processes. Repeatedly producing the same name facilitates language production (i.e., repetition priming), whereas producing semantically related names hinders subsequent performance (i.e., semantic interference). Semantic interference is found whether naming categorically related items once (continuous naming) or multiple times (blocked cyclic naming). A computational model suggests that the same learning mechanism responsible for facilitation in repetition creates semantic interference in categorical naming (Oppenheim, Dell, & Schwartz, 2010). Accordingly, we tested the predictions that variability in semantic interference is correlated across categorical naming tasks and is caused by learning, as measured by two repetition priming tasks (picture-picture repetition priming, Exp. 1; definition-picture repetition priming, Exp. 2, e.g., Wheeldon & Monsell, 1992). In Experiment 1 (77 subjects) semantic interference and repetition priming effects were robust, but the results revealed no relationship between semantic interference effects across contexts. Critically, learning (picture-picture repetition priming) did not predict semantic interference effects in either task. We replicated these results in Experiment 2 (81 subjects), finding no relationship between semantic interference effects across tasks or between semantic interference effects and learning (definition-picture repetition priming). We conclude that the changes underlying facilitatory and interfering effects inherent to lexical access are the result of distinct learning processes where multiple mechanisms contribute to semantic interference in naming. Copyright © 2017 Elsevier B.V. All rights reserved.

Semantic memory is impaired in patients with unilateral anterior temporal lobe resection for temporal lobe epilepsy.

PubMed

Lambon Ralph, Matthew A; Ehsan, Sheeba; Baker, Gus A; Rogers, Timothy T

2012-01-01

Contemporary clinical and basic neuroscience studies have increasingly implicated the anterior temporal lobe regions, bilaterally, in the formation of coherent concepts. Mounting convergent evidence for the importance of the anterior temporal lobe in semantic memory is found in patients with bilateral anterior temporal lobe damage (e.g. semantic dementia), functional neuroimaging and repetitive transcranial magnetic stimulation studies. If this proposal is correct, then one might expect patients with anterior temporal lobe resection for long-standing temporal lobe epilepsy to be semantically impaired. Such patients, however, do not present clinically with striking comprehension deficits but with amnesia and variable anomia, leading some to conclude that semantic memory is intact in resection for temporal lobe epilepsy and thus casting doubt over the conclusions drawn from semantic dementia and linked basic neuroscience studies. Whilst there is a considerable neuropsychological literature on temporal lobe epilepsy, few studies have probed semantic memory directly, with mixed results, and none have undertaken the same type of systematic investigation of semantic processing that has been conducted with other patient groups. In this study, therefore, we investigated the semantic performance of 20 patients with resection for chronic temporal lobe epilepsy with a full battery of semantic assessments, including more sensitive measures of semantic processing. The results provide a bridge between the current clinical observations about resection for temporal lobe epilepsy and the expectations from semantic dementia and other neuroscience findings. Specifically, we found that on simple semantic tasks, the patients' accuracy fell in the normal range, with the exception that some patients with left resection for temporal lobe epilepsy had measurable anomia. Once the semantic assessments were made more challenging, by probing specific-level concepts, lower frequency/more abstract items or measuring reaction times on semantic tasks versus those on difficulty-matched non-semantic assessments, evidence of a semantic impairment was found in all individuals. We conclude by describing a unified, computationally inspired framework for capturing the variable degrees of semantic impairment found across different patient groups (semantic dementia, temporal lobe epilepsy, glioma and stroke) as well as semantic processing in neurologically intact participants.

Semantic memory is impaired in patients with unilateral anterior temporal lobe resection for temporal lobe epilepsy

PubMed Central

Ehsan, Sheeba; Baker, Gus A.; Rogers, Timothy T.

2012-01-01

Contemporary clinical and basic neuroscience studies have increasingly implicated the anterior temporal lobe regions, bilaterally, in the formation of coherent concepts. Mounting convergent evidence for the importance of the anterior temporal lobe in semantic memory is found in patients with bilateral anterior temporal lobe damage (e.g. semantic dementia), functional neuroimaging and repetitive transcranial magnetic stimulation studies. If this proposal is correct, then one might expect patients with anterior temporal lobe resection for long-standing temporal lobe epilepsy to be semantically impaired. Such patients, however, do not present clinically with striking comprehension deficits but with amnesia and variable anomia, leading some to conclude that semantic memory is intact in resection for temporal lobe epilepsy and thus casting doubt over the conclusions drawn from semantic dementia and linked basic neuroscience studies. Whilst there is a considerable neuropsychological literature on temporal lobe epilepsy, few studies have probed semantic memory directly, with mixed results, and none have undertaken the same type of systematic investigation of semantic processing that has been conducted with other patient groups. In this study, therefore, we investigated the semantic performance of 20 patients with resection for chronic temporal lobe epilepsy with a full battery of semantic assessments, including more sensitive measures of semantic processing. The results provide a bridge between the current clinical observations about resection for temporal lobe epilepsy and the expectations from semantic dementia and other neuroscience findings. Specifically, we found that on simple semantic tasks, the patients’ accuracy fell in the normal range, with the exception that some patients with left resection for temporal lobe epilepsy had measurable anomia. Once the semantic assessments were made more challenging, by probing specific-level concepts, lower frequency/more abstract items or measuring reaction times on semantic tasks versus those on difficulty-matched non-semantic assessments, evidence of a semantic impairment was found in all individuals. We conclude by describing a unified, computationally inspired framework for capturing the variable degrees of semantic impairment found across different patient groups (semantic dementia, temporal lobe epilepsy, glioma and stroke) as well as semantic processing in neurologically intact participants. PMID:22287382

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

PubMed

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

2018-04-16

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

Science-Relatedness and Gender-Appropriateness of Careers: Some Pupil Perceptions.

ERIC Educational Resources Information Center

Taber, Keith S.

1992-01-01

Presents findings that young secondary students have stereotyped ideas about the appropriateness of certain careers for men and women. Indicates that careers such as pilot, engine mechanic, electrician, and computer technician are viewed by all students as more suitable for males. Considers the consequences of these results relative to the…

Resistance and relatedness on an evolutionary graph

PubMed Central

Maciejewski, Wes

2012-01-01

When investigating evolution in structured populations, it is often convenient to consider the population as an evolutionary graph—individuals as nodes, and whom they may act with as edges. There has, in recent years, been a surge of interest in evolutionary graphs, especially in the study of the evolution of social behaviours. An inclusive fitness framework is best suited for this type of study. A central requirement for an inclusive fitness analysis is an expression for the genetic similarity between individuals residing on the graph. This has been a major hindrance for work in this area as highly technical mathematics are often required. Here, I derive a result that links genetic relatedness between haploid individuals on an evolutionary graph to the resistance between vertices on a corresponding electrical network. An example that demonstrates the potential computational advantage of this result over contemporary approaches is provided. This result offers more, however, to the study of population genetics than strictly computationally efficient methods. By establishing a link between gene transfer and electric circuit theory, conceptualizations of the latter can enhance understanding of the former. PMID:21849384

A Model for New Linkages for Prior Learning Assessment

ERIC Educational Resources Information Center

Kalz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

2008-01-01

Purpose: The purpose of this paper is twofold: first the paper aims to sketch the theoretical basis for the use of electronic portfolios for prior learning assessment; second it endeavours to introduce latent semantic analysis (LSA) as a powerful method for the computation of semantic similarity between texts and a basis for a new observation link…

How Colours Are Semantically Construed in the Arabic and English Culture: A Comparative Study

ERIC Educational Resources Information Center

Hasan, Amna A.; Al-Sammerai, Nabiha S. Mehdi; Kadir, Fakhrul Adabi Bin Abdul

2011-01-01

Most works in cognitive semantics have been focusing on the manner, in which an individual behaves--be it the mind, brain, or even computers, which process various kinds of information. Among humans, in particular, social life is richly cultured. Sociality and culture are made possible by cognitive studies; they provide specific inputs to…

Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis

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

Joslyn, Cliff A.; Weaver, Jesse R.

In web-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult. We require methods which provide an effective balance between the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexitymore » and human cognition. Here we build on J{\\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency. We demonstrate the efficacy of this approach in a web ingest experiment over the 2012 Billion Triple dataset from the Semantic Web Challenge.« less

Self-Organized Service Negotiation for Collaborative Decision Making

PubMed Central

Zhang, Bo; Zheng, Ziming

2014-01-01

This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM. PMID:25243228

Self-organized service negotiation for collaborative decision making.

PubMed

Zhang, Bo; Huang, Zhenhua; Zheng, Ziming

2014-01-01

This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM.

Utilizing the Structure and Content Information for XML Document Clustering

NASA Astrophysics Data System (ADS)

Tran, Tien; Kutty, Sangeetha; Nayak, Richi

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 document mining challenge. The clustering approach utilizes both the structure and content information of the Wikipedia XML document collection. A latent semantic kernel (LSK) is used to measure the semantic similarity between XML documents based on their content features. The construction of a latent semantic kernel involves the computing of singular vector decomposition (SVD). On a large feature space matrix, the computation of SVD is very expensive in terms of time and memory requirements. Thus in this clustering approach, the dimension of the document space of a term-document matrix is reduced before performing SVD. The document space reduction is based on the common structural information of the Wikipedia XML document collection. The proposed clustering approach has shown to be effective on the Wikipedia collection in the INEX 2008 document mining challenge.

Concepts, Control, and Context: A Connectionist Account of Normal and Disordered Semantic Cognition

PubMed Central

2018-01-01

Semantic cognition requires conceptual representations shaped by verbal and nonverbal experience and executive control processes that regulate activation of knowledge to meet current situational demands. A complete model must also account for the representation of concrete and abstract words, of taxonomic and associative relationships, and for the role of context in shaping meaning. We present the first major attempt to assimilate all of these elements within a unified, implemented computational framework. Our model combines a hub-and-spoke architecture with a buffer that allows its state to be influenced by prior context. This hybrid structure integrates the view, from cognitive neuroscience, that concepts are grounded in sensory-motor representation with the view, from computational linguistics, that knowledge is shaped by patterns of lexical co-occurrence. The model successfully codes knowledge for abstract and concrete words, associative and taxonomic relationships, and the multiple meanings of homonyms, within a single representational space. Knowledge of abstract words is acquired through (a) their patterns of co-occurrence with other words and (b) acquired embodiment, whereby they become indirectly associated with the perceptual features of co-occurring concrete words. The model accounts for executive influences on semantics by including a controlled retrieval mechanism that provides top-down input to amplify weak semantic relationships. The representational and control elements of the model can be damaged independently, and the consequences of such damage closely replicate effects seen in neuropsychological patients with loss of semantic representation versus control processes. Thus, the model provides a wide-ranging and neurally plausible account of normal and impaired semantic cognition. PMID:29733663

Semantic representation in the white matter pathway

PubMed Central

Fang, Yuxing; Wang, Xiaosha; Zhong, Suyu; Song, Luping; Han, Zaizhu; Gong, Gaolang

2018-01-01

Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients. This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items, models trained with one item should also predict naming accuracy of the other. Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space. Such associations were not attributable to modality-specific attributes (shape, manipulation, color, and motion), to peripheral picture-naming processes (picture visual similarity, phonological similarity), to broad semantic categories, or to the properties of the cortical regions that they connected, which tended to represent multiple modality-specific attributes. That is, the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes. PMID:29624578

A Computational Unification of Scientific Law:. Spelling out a Universal Semantics for Physical Reality

NASA Astrophysics Data System (ADS)

Marcer, Peter J.; Rowlands, Peter

2013-09-01

The principal criteria Cn (n = 1 to 23) and grammatical production rules are set out of a universal computational rewrite language spelling out a semantic description of an emergent, self-organizing architecture for the cosmos. These language productions already predicate: (1) Einstein's conservation law of energy, momentum and mass and, subsequently, (2) with respect to gauge invariant relativistic space time (both Lorentz special & Einstein general); (3) Standard Model elementary particle physics; (4) the periodic table of the elements & chemical valence; and (5) the molecular biological basis of the DNA / RNA genetic code; so enabling the Cybernetic Machine specialist Groups Mission Statement premise;** (6) that natural semantic language thinking at the higher level of the self-organized emergent chemical molecular complexity of the human brain (only surpassed by that of the cosmos itself!) would be realized (7) by this same universal semantic language via (8) an architecture of a conscious human brain/mind and self which, it predicates consists of its neural / glia and microtubule substrates respectively, so as to endow it with; (9) the intelligent semantic capability to be able to specify, symbolize, spell out and understand the cosmos that conceived it; and (10) provide a quantum physical explanation of consciousness and of how (11) the dichotomy between first person subjectivity and third person objectivity or `hard problem' is resolved.

On the Equivalence of Formal Grammars and Machines.

ERIC Educational Resources Information Center

Lund, Bruce

1991-01-01

Explores concepts of formal language and automata theory underlying computational linguistics. A computational formalism is described known as a "logic grammar," with which computational systems process linguistic data, with examples in declarative and procedural semantics and definite clause grammars. (13 references) (CB)

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

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

Hohimer, Ryan E.; Greitzer, Frank L.; Noonan, Christine F.

2011-11-15

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

Highlighting relatedness promotes prosocial motives and behavior.

PubMed

Pavey, Louisa; Greitemeyer, Tobias; Sparks, Paul

2011-07-01

According to self-determination theory, people have three basic psychological needs: relatedness, competence, and autonomy. Of these, the authors reasoned that relatedness need satisfaction is particularly important for promoting prosocial behavior because of the increased sense of connectedness to others that this engenders. In Experiment 1, the authors manipulated relatedness, autonomy, competence, or gave participants a neutral task, and found that highlighting relatedness led to higher interest in volunteering and intentions to volunteer relative to the other conditions. Experiment 2 found that writing about relatedness experiences promoted feelings of connectedness to others, which in turn predicted greater prosocial intentions. Experiment 3 found that relatedness manipulation participants donated significantly more money to charity than did participants given a neutral task. The results suggest that highlighting relatedness increases engagement in prosocial activities and are discussed in relation to the conflict and compatibility between individual and social outcomes. © 2011 by the Society for Personality and Social Psychology, Inc

The Cascading Development of Autonomy and Relatedness From Adolescence to Adulthood

PubMed Central

Oudekerk, Barbara A.; Allen, Joseph P.; Hessel, Elenda T.; Molloy, Lauren E.

2014-01-01

We tested a developmental cascade model of autonomy and relatedness in the progression from parent to friend to romantic relationships across ages 13, 18, and 21. Participants included 184 adolescents (53% female, 58% Caucasian, 29% African American) recruited from a public middle school in Virginia. Parental psychological control at age 13 undermined the development of autonomy and relatedness, predicting relative decreases in autonomy and relatedness with friends between ages 13 and 18 and lower levels of autonomy and relatedness with partners at age 18. These cascade effects extended into adult friendships and romantic relationships, with autonomy and relatedness with romantic partners at age 18 being a strong predictor of autonomy and relatedness with both friends and partners at age 21. PMID:25345623

Does sunshine prime loyal … or summer? Effects of associative relatedness on the evaluative priming effect in the valent/neutral categorisation task.

PubMed

Werner, Benedikt; von Ramin, Elisabeth; Spruyt, Adriaan; Rothermund, Klaus

2018-02-01

After 30 years of research, the mechanisms underlying the evaluative priming effect are still a topic of debate. In this study, we tested whether the evaluative priming effect can result from (uncontrolled) associative relatedness rather than evaluative congruency. Stimuli that share the same evaluative connotation are more likely to show some degree of non-evaluative associative relatedness than stimuli that have a different evaluative connotation. Therefore, unless associative relatedness is explicitly controlled for, evaluative priming effects reported in earlier research may be driven by associative relatedness instead of evaluative relatedness. To address this possibility, we performed an evaluative priming study in which evaluative congruency and associative relatedness were manipulated independently from each other. The valent/neutral categorisation task was used to ensure evaluative stimulus processing in the absence of response priming effects. Results showed an effect of associative relatedness but no (overall) effect of evaluative congruency. Our findings highlight the importance of controlling for associative relatedness when testing for evaluative priming effects.

Computational Modeling of Reading in Semantic Dementia: Comment on Woollams, Lambon Ralph, Plaut, and Patterson (2007)

ERIC Educational Resources Information Center

Coltheart, Max; Tree, Jeremy J.; Saunders, Steven J.

2010-01-01

Woollams, Lambon Ralph, Plaut, and Patterson (see record 2007-05396-004) reported detailed data on reading in 51 cases of semantic dementia. They simulated some aspects of these data using a connectionist parallel distributed processing (PDP) triangle model of reading. We argue here that a different model of reading, the dual route cascaded (DRC)…

The next generation of similarity measures that fully explore the semantics in biomedical ontologies.

PubMed

Couto, Francisco M; Pinto, H Sofia

2013-10-01

There is a prominent trend to augment and improve the formality of biomedical ontologies. For example, this is shown by the current effort on adding description logic axioms, such as disjointness. One of the key ontology applications that can take advantage of this effort is the conceptual (functional) similarity measurement. The presence of description logic axioms in biomedical ontologies make the current structural or extensional approaches weaker and further away from providing sound semantics-based similarity measures. Although beneficial in small ontologies, the exploration of description logic axioms by semantics-based similarity measures is computational expensive. This limitation is critical for biomedical ontologies that normally contain thousands of concepts. Thus in the process of gaining their rightful place, biomedical functional similarity measures have to take the journey of finding how this rich and powerful knowledge can be fully explored while keeping feasible computational costs. This manuscript aims at promoting and guiding the development of compelling tools that deliver what the biomedical community will require in a near future: a next-generation of biomedical similarity measures that efficiently and fully explore the semantics present in biomedical ontologies.

The MMI Semantic Framework: Rosetta Stones for Earth Sciences

NASA Astrophysics Data System (ADS)

Rueda, C.; Bermudez, L. E.; Graybeal, J.; Alexander, P.

2009-12-01

Semantic interoperability—the exchange of meaning among computer systems—is needed to successfully share data in Ocean Science and across all Earth sciences. The best approach toward semantic interoperability requires a designed framework, and operationally tested tools and infrastructure within that framework. Currently available technologies make a scientific semantic framework feasible, but its development requires sustainable architectural vision and development processes. This presentation outlines the MMI Semantic Framework, including recent progress on it and its client applications. The MMI Semantic Framework consists of tools, infrastructure, and operational and community procedures and best practices, to meet short-term and long-term semantic interoperability goals. The design and prioritization of the semantic framework capabilities are based on real-world scenarios in Earth observation systems. We describe some key uses cases, as well as the associated requirements for building the overall infrastructure, which is realized through the MMI Ontology Registry and Repository. This system includes support for community creation and sharing of semantic content, ontology registration, version management, and seamless integration of user-friendly tools and application programming interfaces. The presentation describes the architectural components for semantic mediation, registry and repository for vocabularies, ontology, and term mappings. We show how the technologies and approaches in the framework can address community needs for managing and exchanging semantic information. We will demonstrate how different types of users and client applications exploit the tools and services for data aggregation, visualization, archiving, and integration. Specific examples from OOSTethys (http://www.oostethys.org) and the Ocean Observatories Initiative Cyberinfrastructure (http://www.oceanobservatories.org) will be cited. Finally, we show how semantic augmentation of web services standards could be performed using framework tools.

Constructing a Geology Ontology Using a Relational Database

NASA Astrophysics Data System (ADS)

Hou, W.; Yang, L.; Yin, S.; Ye, J.; Clarke, K.

2013-12-01

In geology community, the creation of a common geology ontology has become a useful means to solve problems of data integration, knowledge transformation and the interoperation of multi-source, heterogeneous and multiple scale geological data. Currently, human-computer interaction methods and relational database-based methods are the primary ontology construction methods. Some human-computer interaction methods such as the Geo-rule based method, the ontology life cycle method and the module design method have been proposed for applied geological ontologies. Essentially, the relational database-based method is a reverse engineering of abstracted semantic information from an existing database. The key is to construct rules for the transformation of database entities into the ontology. Relative to the human-computer interaction method, relational database-based methods can use existing resources and the stated semantic relationships among geological entities. However, two problems challenge the development and application. One is the transformation of multiple inheritances and nested relationships and their representation in an ontology. The other is that most of these methods do not measure the semantic retention of the transformation process. In this study, we focused on constructing a rule set to convert the semantics in a geological database into a geological ontology. According to the relational schema of a geological database, a conversion approach is presented to convert a geological spatial database to an OWL-based geological ontology, which is based on identifying semantics such as entities, relationships, inheritance relationships, nested relationships and cluster relationships. The semantic integrity of the transformation was verified using an inverse mapping process. In a geological ontology, an inheritance and union operations between superclass and subclass were used to present the nested relationship in a geochronology and the multiple inheritances relationship. Based on a Quaternary database of downtown of Foshan city, Guangdong Province, in Southern China, a geological ontology was constructed using the proposed method. To measure the maintenance of semantics in the conversation process and the results, an inverse mapping from the ontology to a relational database was tested based on a proposed conversation rule. The comparison of schema and entities and the reduction of tables between the inverse database and the original database illustrated that the proposed method retains the semantic information well during the conversation process. An application for abstracting sandstone information showed that semantic relationships among concepts in the geological database were successfully reorganized in the constructed ontology. Key words: geological ontology; geological spatial database; multiple inheritance; OWL Acknowledgement: This research is jointly funded by the Specialized Research Fund for the Doctoral Program of Higher Education of China (RFDP) (20100171120001), NSFC (41102207) and the Fundamental Research Funds for the Central Universities (12lgpy19).

Cognitive search model and a new query paradigm

NASA Astrophysics Data System (ADS)

Xu, Zhonghui

2001-06-01

This paper proposes a cognitive model in which people begin to search pictures by using semantic content and find a right picture by judging whether its visual content is a proper visualization of the semantics desired. It is essential that human search is not just a process of matching computation on visual feature but rather a process of visualization of the semantic content known. For people to search electronic images in the way as they manually do in the model, we suggest that querying be a semantic-driven process like design. A query-by-design paradigm is prosed in the sense that what you design is what you find. Unlike query-by-example, query-by-design allows users to specify the semantic content through an iterative and incremental interaction process so that a retrieval can start with association and identification of the given semantic content and get refined while further visual cues are available. An experimental image retrieval system, Kuafu, has been under development using the query-by-design paradigm and an iconic language is adopted.

Decoding semantic information from human electrocorticographic (ECoG) signals.

PubMed

Wang, Wei; Degenhart, Alan D; Sudre, Gustavo P; Pomerleau, Dean A; Tyler-Kabara, Elizabeth C

2011-01-01

This study examined the feasibility of decoding semantic information from human cortical activity. Four human subjects undergoing presurgical brain mapping and seizure foci localization participated in this study. Electrocorticographic (ECoG) signals were recorded while the subjects performed simple language tasks involving semantic information processing, such as a picture naming task where subjects named pictures of objects belonging to different semantic categories. Robust high-gamma band (60-120 Hz) activation was observed at the left inferior frontal gyrus (LIFG) and the posterior portion of the superior temporal gyrus (pSTG) with a temporal sequence corresponding to speech production and perception. Furthermore, Gaussian Naïve Bayes and Support Vector Machine classifiers, two commonly used machine learning algorithms for pattern recognition, were able to predict the semantic category of an object using cortical activity captured by ECoG electrodes covering the frontal, temporal and parietal cortices. These findings have implications for both basic neuroscience research and development of semantic-based brain-computer interface systems (BCI) that can help individuals with severe motor or communication disorders to express their intention and thoughts.

Using RDF to Model the Structure and Process of Systems

NASA Astrophysics Data System (ADS)

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

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

A novel co-occurrence-based approach to predict pure associative and semantic priming.

PubMed

Roelke, Andre; Franke, Nicole; Biemann, Chris; Radach, Ralph; Jacobs, Arthur M; Hofmann, Markus J

2018-03-15

The theoretical "difficulty in separating association strength from [semantic] feature overlap" has resulted in inconsistent findings of either the presence or absence of "pure" associative priming in recent literature (Hutchison, 2003, Psychonomic Bulletin & Review, 10(4), p. 787). The present study used co-occurrence statistics of words in sentences to provide a full factorial manipulation of direct association (strong/no) and the number of common associates (many/no) of the prime and target words. These common associates were proposed to serve as semantic features for a recent interactive activation model of semantic processing (i.e., the associative read-out model; Hofmann & Jacobs, 2014). With stimulus onset asynchrony (SOA) as an additional factor, our findings indicate that associative and semantic priming are indeed dissociable. Moreover, the effect of direct association was strongest at a long SOA (1,000 ms), while many common associates facilitated lexical decisions primarily at a short SOA (200 ms). This response pattern is consistent with previous performance-based accounts and suggests that associative and semantic priming can be evoked by computationally determined direct and common associations.

Social Relatedness and Autism: Current Research, Issues, Directions.

ERIC Educational Resources Information Center

Walters, Anne S.; And Others

1990-01-01

This literature review focuses on recent studies devoted to hypothesized aspects of social relatedness in autism, discussing attachment and social interest, recognition of and communication of emotions, social cognition, social communication, symbolic play, neuroanatomy of social relatedness, and neurochemistry of social relatedness. (JDD)

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.

The Mental Representation of Polysemy across Word Classes

PubMed Central

Lopukhina, Anastasiya; Laurinavichyute, Anna; Lopukhin, Konstantin; Dragoy, Olga

2018-01-01

Experimental studies on polysemy have come to contradictory conclusions on whether words with multiple senses are stored as separate or shared mental representations. The present study examined the semantic relatedness and semantic similarity of literal and non-literal (metonymic and metaphorical) senses of three word classes: nouns, verbs, and adjectives. Two methods were used: a psycholinguistic experiment and a distributional analysis of corpus data. In the experiment, participants were presented with 6–12 short phrases containing a polysemous word in literal, metonymic, or metaphorical senses and were asked to classify them so that phrases with the same perceived sense were grouped together. To investigate the impact of professional background on their decisions, participants were controlled for linguistic vs. non-linguistic education. For nouns and verbs, all participants preferred to group together phrases with literal and metonymic senses, but not any other pairs of senses. For adjectives, two pairs of senses were often grouped together: literal with metonymic, and metonymic with metaphorical. Participants with a linguistic background were more accurate than participants with non-linguistic backgrounds, although both groups shared principal patterns of sense classification. For the distributional analysis of corpus data, we used a semantic vector approach to quantify the similarity of phrases with literal, metonymic, and metaphorical senses in the corpora. We found that phrases with literal and metonymic senses had the highest degree of similarity for the three word classes, and that metonymic and metaphorical senses of adjectives had the highest degree of similarity among all word classes. These findings are in line with the experimental results. Overall, the results suggest that the mental representation of a polysemous word depends on its word class. In nouns and verbs, literal and metonymic senses are stored together, while metaphorical senses are stored separately; in adjectives, metonymic senses significantly overlap with both literal and metaphorical senses. PMID:29515502

The processing of actions and action-words in amyotrophic lateral sclerosis patients.

PubMed

Papeo, Liuba; Cecchetto, Cinzia; Mazzon, Giulia; Granello, Giulia; Cattaruzza, Tatiana; Verriello, Lorenzo; Eleopra, Roberto; Rumiati, Raffaella I

2015-03-01

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with prime consequences on the motor function and concomitant cognitive changes, most frequently in the domain of executive functions. Moreover, poorer performance with action-verbs versus object-nouns has been reported in ALS patients, raising the hypothesis that the motor dysfunction deteriorates the semantic representation of actions. Using action-verbs and manipulable-object nouns sharing semantic relationship with the same motor representations, the verb-noun difference was assessed in a group of 21 ALS-patients with severely impaired motor behavior, and compared with a normal sample's performance. ALS-group performed better on nouns than verbs, both in production (action and object naming) and comprehension (word-picture matching). This observation implies that the interpretation of the verb-noun difference in ALS cannot be accounted by the relatedness of verbs to motor representations, but has to consider the role of other semantic and/or morpho-phonological dimensions that distinctively define the two grammatical classes. Moreover, this difference in the ALS-group was not greater than the noun-verb difference in the normal sample. The mental representation of actions also involves an executive-control component to organize, in logical/temporal order, the individual motor events (or sub-goals) that form a purposeful action. We assessed this ability with action sequencing tasks, requiring participants to re-construct a purposeful action from the scrambled presentation of its constitutive motor events, shown in the form of photographs or short sentences. In those tasks, ALS-group's performance was significantly poorer than controls'. Thus, the executive dysfunction manifested in the sequencing deficit -but not the selective verb deficit- appears as a consistent feature of the cognitive profile associated with ALS. We suggest that ALS can offer a valuable model to study the relationship between (frontal) motor centers and the executive-control machinery housed in the frontal brain, and the implications of executive dysfunctions in tasks such as action processing. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Mental Representation of Polysemy across Word Classes.

PubMed

Lopukhina, Anastasiya; Laurinavichyute, Anna; Lopukhin, Konstantin; Dragoy, Olga

2018-01-01

Experimental studies on polysemy have come to contradictory conclusions on whether words with multiple senses are stored as separate or shared mental representations. The present study examined the semantic relatedness and semantic similarity of literal and non-literal (metonymic and metaphorical) senses of three word classes: nouns, verbs, and adjectives. Two methods were used: a psycholinguistic experiment and a distributional analysis of corpus data. In the experiment, participants were presented with 6-12 short phrases containing a polysemous word in literal, metonymic, or metaphorical senses and were asked to classify them so that phrases with the same perceived sense were grouped together. To investigate the impact of professional background on their decisions, participants were controlled for linguistic vs. non-linguistic education. For nouns and verbs, all participants preferred to group together phrases with literal and metonymic senses, but not any other pairs of senses. For adjectives, two pairs of senses were often grouped together: literal with metonymic, and metonymic with metaphorical. Participants with a linguistic background were more accurate than participants with non-linguistic backgrounds, although both groups shared principal patterns of sense classification. For the distributional analysis of corpus data, we used a semantic vector approach to quantify the similarity of phrases with literal, metonymic, and metaphorical senses in the corpora. We found that phrases with literal and metonymic senses had the highest degree of similarity for the three word classes, and that metonymic and metaphorical senses of adjectives had the highest degree of similarity among all word classes. These findings are in line with the experimental results. Overall, the results suggest that the mental representation of a polysemous word depends on its word class. In nouns and verbs, literal and metonymic senses are stored together, while metaphorical senses are stored separately; in adjectives, metonymic senses significantly overlap with both literal and metaphorical senses.

Computer Processing of Esperanto Text.

ERIC Educational Resources Information Center

Sherwood, Bruce

1981-01-01

Basic aspects of computer processing of Esperanto are considered in relation to orthography and computer representation, phonetics, morphology, one-syllable and multisyllable words, lexicon, semantics, and syntax. There are 28 phonemes in Esperanto, each represented in orthography by a single letter. The PLATO system handles diacritics by using a…

Preferences of newborn mice for odours indicating closer genetic relatedness: is experience necessary?

PubMed

Todrank, Josephine; Busquet, Nicolas; Baudoin, Claude; Heth, Giora

2005-10-07

Evidence from studies with adult rodents indicates that individual recognition enables distinctions between familiar individuals irrespective of relatedness (but including close kin) and a separate mechanism enables discriminations based on genetic relatedness without prior familiarity. For example, adult mice could assess the extent of their genetic relatedness to unfamiliar individuals using perceptual similarities between their individual odours. The ontogeny of this genetic relatedness assessment mechanism, however, had not been investigated. Here, in two-choice tests, newborn mice differentially preferred odours of more genetically similar lactating females (paternal aunts to unrelated conspecific and conspecific to heterospecific) even without prior direct exposure to adults with the tested genotypes. The results provide a direct demonstration of genetic relatedness assessment abilities in newborns and show that experience with parental odours is not necessary for genetic relatedness distinctions. Future studies will be necessary to determine whether exposure to odours of other foetuses in the womb or littermates shortly after birth affects this genetic relatedness assessment process.

Preferences of newborn mice for odours indicating closer genetic relatedness: is experience necessary?

PubMed Central

Todrank, Josephine; Busquet, Nicolas; Baudoin, Claude; Heth, Giora

2005-01-01

Evidence from studies with adult rodents indicates that individual recognition enables distinctions between familiar individuals irrespective of relatedness (but including close kin) and a separate mechanism enables discriminations based on genetic relatedness without prior familiarity. For example, adult mice could assess the extent of their genetic relatedness to unfamiliar individuals using perceptual similarities between their individual odours. The ontogeny of this genetic relatedness assessment mechanism, however, had not been investigated. Here, in two-choice tests, newborn mice differentially preferred odours of more genetically similar lactating females (paternal aunts to unrelated conspecific and conspecific to heterospecific) even without prior direct exposure to adults with the tested genotypes. The results provide a direct demonstration of genetic relatedness assessment abilities in newborns and show that experience with parental odours is not necessary for genetic relatedness distinctions. Future studies will be necessary to determine whether exposure to odours of other foetuses in the womb or littermates shortly after birth affects this genetic relatedness assessment process. PMID:16191620

Predicting Visual Semantic Descriptive Terms from Radiological Image Data: Preliminary Results with Liver Lesions in CT

PubMed Central

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

2014-01-01

We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using high–order steerable Riesz wavelets and support vector machines (SVM). The organization of scales and directions that are specific to every VST are modeled as linear combinations of directional Riesz wavelets. The models obtained are steerable, which means that any orientation of the model can be synthesized from linear combinations of the basis filters. The latter property is leveraged to model VST independently from their local orientation. In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a non–hierarchical computationally–derived ontology of VST containing inter–term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave–one–patient–out cross–validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST when using SVMs in a feature space combining the magnitudes of the steered models with CT intensities. Likelihood maps are created for each VST, which enables high transparency of the information modeled. The computationally–derived ontology obtained from the VST models was found to be consistent with the underlying semantics of the visual terms. It was found to be complementary to the RadLex ontology, and constitutes a potential method to link the image content to visual semantics. The proposed framework is expected to foster human–computer synergies for the interpretation of radiological images while using rotation–covariant computational models of VSTs to (1) quantify their local likelihood and (2) explicitly link them with pixel–based image content in the context of a given imaging domain. PMID:24808406

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

The cascading development of autonomy and relatedness from adolescence to adulthood.

PubMed

Oudekerk, Barbara A; Allen, Joseph P; Hessel, Elenda T; Molloy, Lauren E

2015-01-01

A developmental cascade model of autonomy and relatedness in the progression from parent to friend to romantic relationships across ages 13, 18, and 21 was examined among 184 adolescents (53% female, 58% Caucasian, 29% African American) recruited from a public middle school in Virginia. Parental psychological control at age 13 undermined the development of autonomy and relatedness, predicting relative decreases in autonomy and relatedness with friends between ages 13 and 18 and lower levels of autonomy and relatedness with partners at age 18. These cascade effects extended into adult friendships and romantic relationships, with autonomy and relatedness with romantic partners at age 18 being a strong predictor of autonomy and relatedness with both friends and partners at age 21. © 2014 The Authors. Child Development © 2014 Society for Research in Child Development, Inc.

Understanding human activity patterns based on space-time-semantics

NASA Astrophysics Data System (ADS)

Huang, Wei; Li, Songnian

2016-11-01

Understanding human activity patterns plays a key role in various applications in an urban environment, such as transportation planning and traffic forecasting, urban planning, public health and safety, and emergency response. Most existing studies in modeling human activity patterns mainly focus on spatiotemporal dimensions, which lacks consideration of underlying semantic context. In fact, what people do and discuss at some places, inferring what is happening at the places, cannot be simple neglected because it is the root of human mobility patterns. We believe that the geo-tagged semantic context, representing what individuals do and discuss at a place and a specific time, drives a formation of specific human activity pattern. In this paper, we aim to model human activity patterns not only based on space and time but also with consideration of associated semantics, and attempt to prove a hypothesis that similar mobility patterns may have different motivations. We develop a spatiotemporal-semantic model to quantitatively express human activity patterns based on topic models, leading to an analysis of space, time and semantics. A case study is conducted using Twitter data in Toronto based on our model. Through computing the similarities between users in terms of spatiotemporal pattern, semantic pattern and spatiotemporal-semantic pattern, we find that only a small number of users (2.72%) have very similar activity patterns, while the majority (87.14%) show different activity patterns (i.e., similar spatiotemporal patterns and different semantic patterns, similar semantic patterns and different spatiotemporal patterns, or different in both). The population of users that has very similar activity patterns is decreased by 56.41% after incorporating semantic information in the corresponding spatiotemporal patterns, which can quantitatively prove the hypothesis.

Surface errors without semantic impairment in acquired dyslexia: a voxel-based lesion–symptom mapping study

PubMed Central

Pillay, Sara B.; Humphries, Colin J.; Gross, William L.; Graves, William W.; Book, Diane S.

2016-01-01

Patients with surface dyslexia have disproportionate difficulty pronouncing irregularly spelled words (e.g. pint), suggesting impaired use of lexical-semantic information to mediate phonological retrieval. Patients with this deficit also make characteristic ‘regularization’ errors, in which an irregularly spelled word is mispronounced by incorrect application of regular spelling-sound correspondences (e.g. reading plaid as ‘played’), indicating over-reliance on sublexical grapheme–phoneme correspondences. We examined the neuroanatomical correlates of this specific error type in 45 patients with left hemisphere chronic stroke. Voxel-based lesion–symptom mapping showed a strong positive relationship between the rate of regularization errors and damage to the posterior half of the left middle temporal gyrus. Semantic deficits on tests of single-word comprehension were generally mild, and these deficits were not correlated with the rate of regularization errors. Furthermore, the deep occipital-temporal white matter locus associated with these mild semantic deficits was distinct from the lesion site associated with regularization errors. Thus, in contrast to patients with surface dyslexia and semantic impairment from anterior temporal lobe degeneration, surface errors in our patients were not related to a semantic deficit. We propose that these patients have an inability to link intact semantic representations with phonological representations. The data provide novel evidence for a post-semantic mechanism mediating the production of surface errors, and suggest that the posterior middle temporal gyrus may compute an intermediate representation linking semantics with phonology. PMID:26966139

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text.

PubMed

Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro; Poesio, Massimo; Baroni, Marco

2015-10-15

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations. Copyright © 2015 Elsevier Inc. All rights reserved.

On a categorial aspect of knowledge representation

NASA Astrophysics Data System (ADS)

Tataj, Emanuel; Mulawka, Jan; Nieznański, Edward

Adequate representation of data is crucial for modeling any type of data. To faithfully present and describe the relevant section of the world it is necessary to select the method that can easily be implemented on a computer system which will help in further description allowing reasoning. The main objective of this contribution is to present methods of knowledge representation using categorial approach. Next to identify the main advantages for computer implementation. Categorical aspect of knowledge representation is considered in semantic networks realisation. Such method borrows already known metaphysics properties for data modeling process. The potential topics of further development of categorical semantic networks implementations are also underlined.

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.

Computational approaches for predicting biomedical research collaborations.

PubMed

Zhang, Qing; Yu, Hong

2014-01-01

Biomedical research is increasingly collaborative, and successful collaborations often produce high impact work. Computational approaches can be developed for automatically predicting biomedical research collaborations. Previous works of collaboration prediction mainly explored the topological structures of research collaboration networks, leaving out rich semantic information from the publications themselves. In this paper, we propose supervised machine learning approaches to predict research collaborations in the biomedical field. We explored both the semantic features extracted from author research interest profile and the author network topological features. We found that the most informative semantic features for author collaborations are related to research interest, including similarity of out-citing citations, similarity of abstracts. Of the four supervised machine learning models (naïve Bayes, naïve Bayes multinomial, SVMs, and logistic regression), the best performing model is logistic regression with an ROC ranging from 0.766 to 0.980 on different datasets. To our knowledge we are the first to study in depth how research interest and productivities can be used for collaboration prediction. Our approach is computationally efficient, scalable and yet simple to implement. The datasets of this study are available at https://github.com/qingzhanggithub/medline-collaboration-datasets.

Computer-Based Mapping for Curriculum Development.

ERIC Educational Resources Information Center

Allen, Brockenbrough S.; And Others

This article describes the results of a three-month experiment in the use of computer-based semantic networks for curriculum development. A team of doctoral and master's degree students developed a 1200-item computer database representing a tentative "domain of competency" for a proposed MA degree in Workforce Education and Lifelong…

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

PubMed Central

2013-01-01

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

Relative category-specific preservation in semantic dementia? Evidence from 35 cases.

PubMed

Merck, Catherine; Jonin, Pierre-Yves; Vichard, Hélène; Boursiquot, Sandrine Le Moal; Leblay, Virginie; Belliard, Serge

2013-03-01

Category-specific deficits have rarely been reported in semantic dementia (SD). To our knowledge, only four previous studies have documented category-specific deficits, and these have focused on the living versus non-living things contrast rather than on more fine-grained semantic categories. This study aimed to determine whether a category-specific effect could be highlighted by a semantic sorting task administered to 35 SD patients once at baseline and again after 2 years and to 10 Alzheimer's disease patients (AD). We found a relative preservation of fruit and vegetables only in SD. This relative preservation of fruit and vegetables could be considered with regard to the importance of color knowledge in their discrimination. Indeed, color knowledge retrieval is known to depend on the left posterior fusiform gyrus which is relatively spared in SD. Finally, according to predictions of semantic memory models, our findings best fitted the Devlin and Gonnerman's computational account. Copyright © 2013 Elsevier Inc. All rights reserved.

Taxonomic and Thematic Semantic Systems

PubMed Central

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

2017-01-01

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

An Ethnographic Study of the Computational Strategies of a Group of Young Street Vendors in Beirut.

ERIC Educational Resources Information Center

Jurdak, Murad; Shahin, Iman

1999-01-01

Examines the computational strategies of 10 young street vendors in Beirut by describing, comparing, and analyzing computational strategies used in solving three types of problems: (1) transactions in the workplace; (2) word problems; and (3) computation exercises in a school-like setting. Indicates that vendors' use of semantically-based mental…

Stimulus Value Signals in Ventromedial PFC Reflect the Integration of Attribute Value Signals Computed in Fusiform Gyrus and Posterior Superior Temporal Gyrus

PubMed Central

Lim, Seung-Lark; O'Doherty, John P.

2013-01-01

We often have to make choices among multiattribute stimuli (e.g., a food that differs on its taste and health). Behavioral data suggest that choices are made by computing the value of the different attributes and then integrating them into an overall stimulus value signal. However, it is not known whether this theory describes the way the brain computes the stimulus value signals, or how the underlying computations might be implemented. We investigated these questions using a human fMRI task in which individuals had to evaluate T-shirts that varied in their visual esthetic (e.g., color) and semantic (e.g., meaning of logo printed in T-shirt) components. We found that activity in the fusiform gyrus, an area associated with the processing of visual features, correlated with the value of the visual esthetic attributes, but not with the value of the semantic attributes. In contrast, activity in posterior superior temporal gyrus, an area associated with the processing of semantic meaning, exhibited the opposite pattern. Furthermore, both areas exhibited functional connectivity with an area of ventromedial prefrontal cortex that reflects the computation of overall stimulus values at the time of decision. The results provide supporting evidence for the hypothesis that some attribute values are computed in cortical areas specialized in the processing of such features, and that those attribute-specific values are then passed to the vmPFC to be integrated into an overall stimulus value signal to guide the decision. PMID:23678116

Stimulus value signals in ventromedial PFC reflect the integration of attribute value signals computed in fusiform gyrus and posterior superior temporal gyrus.

PubMed

Lim, Seung-Lark; O'Doherty, John P; Rangel, Antonio

2013-05-15

We often have to make choices among multiattribute stimuli (e.g., a food that differs on its taste and health). Behavioral data suggest that choices are made by computing the value of the different attributes and then integrating them into an overall stimulus value signal. However, it is not known whether this theory describes the way the brain computes the stimulus value signals, or how the underlying computations might be implemented. We investigated these questions using a human fMRI task in which individuals had to evaluate T-shirts that varied in their visual esthetic (e.g., color) and semantic (e.g., meaning of logo printed in T-shirt) components. We found that activity in the fusiform gyrus, an area associated with the processing of visual features, correlated with the value of the visual esthetic attributes, but not with the value of the semantic attributes. In contrast, activity in posterior superior temporal gyrus, an area associated with the processing of semantic meaning, exhibited the opposite pattern. Furthermore, both areas exhibited functional connectivity with an area of ventromedial prefrontal cortex that reflects the computation of overall stimulus values at the time of decision. The results provide supporting evidence for the hypothesis that some attribute values are computed in cortical areas specialized in the processing of such features, and that those attribute-specific values are then passed to the vmPFC to be integrated into an overall stimulus value signal to guide the decision.

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

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity.

PubMed

Murray, Kevin D; Webers, Christfried; Ong, Cheng Soon; Borevitz, Justin; Warthmann, Norman

2017-09-01

Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or "samples") in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.

Semantics-based distributed I/O with the ParaMEDIC framework.

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

Balaji, P.; Feng, W.; Lin, H.

2008-01-01

Many large-scale applications simultaneously rely on multiple resources for efficient execution. For example, such applications may require both large compute and storage resources; however, very few supercomputing centers can provide large quantities of both. Thus, data generated at the compute site oftentimes has to be moved to a remote storage site for either storage or visualization and analysis. Clearly, this is not an efficient model, especially when the two sites are distributed over a wide-area network. Thus, we present a framework called 'ParaMEDIC: Parallel Metadata Environment for Distributed I/O and Computing' which uses application-specific semantic information to convert the generatedmore » data to orders-of-magnitude smaller metadata at the compute site, transfer the metadata to the storage site, and re-process the metadata at the storage site to regenerate the output. Specifically, ParaMEDIC trades a small amount of additional computation (in the form of data post-processing) for a potentially significant reduction in data that needs to be transferred in distributed environments.« less

Pupillary Stroop effects

PubMed Central

Ørbo, Marte; Holmlund, Terje; Miozzo, Michele

2010-01-01

We recorded the pupil diameters of participants performing the words’ color-naming Stroop task (i.e., naming the color of a word that names a color). Non-color words were used as baseline to firmly establish the effects of semantic relatedness induced by color word distractors. We replicated the classic Stroop effects of color congruency and color incongruency with pupillary diameter recordings: relative to non-color words, pupil diameters increased for color distractors that differed from color responses, while they reduced for color distractors that were identical to color responses. Analyses of the time courses of pupil responses revealed further differences between color-congruent and color-incongruent distractors, with the latter inducing a steep increase of pupil size and the former a relatively lower increase. Consistent with previous findings that have demonstrated that pupil size increases as task demands rise, the present results indicate that pupillometry is a robust measure of Stroop interference, and it represents a valuable addition to the cognitive scientist’s toolbox. PMID:20865297

Enhancing lexical ambiguity resolution by brain polarization of the right posterior superior temporal sulcus.

PubMed

Peretz, Yael; Lavidor, Michal

2013-04-01

Previous studies have reported a hemispheric asymmetry in processing dominant (e.g., paper) and subordinate (e.g., farmer) associations of ambiguous words (pen). Here we applied sham and anodal Transcranial Direct Current Stimulation (tDCS) over Wernicke's area and its right homologue to test whether we can modulate the selective hemispheric expertise in processing lexical ambiguity. Ambiguous prime words were presented followed by target words that could be associated to the dominant or subordinate meaning of the prime in a semantic relatedness task. Anodal stimulation of the right Wernicke's area significantly decreased response time (RTs) to subordinate but not dominant associations compared to sham stimulation. There was also a complementary trend of faster responses to dominant associations following anodal stimulation of Wernicke's area. The results support brain asymmetry in processing lexical ambiguity and show that tDCS can enhance complex language processing even in a sample of highly literate individuals. Copyright © 2012 Elsevier Ltd. All rights reserved.

Practical Semantic Astronomy

NASA Astrophysics Data System (ADS)

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

2010-01-01

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

Neural bases of event knowledge and syntax integration in comprehension of complex sentences.

PubMed

Malaia, Evie; Newman, Sharlene

2015-01-01

Comprehension of complex sentences is necessarily supported by both syntactic and semantic knowledge, but what linguistic factors trigger a readers' reliance on a specific system? This functional neuroimaging study orthogonally manipulated argument plausibility and verb event type to investigate cortical bases of the semantic effect on argument comprehension during reading. The data suggest that telic verbs facilitate online processing by means of consolidating the event schemas in episodic memory and by easing the computation of syntactico-thematic hierarchies in the left inferior frontal gyrus. The results demonstrate that syntax-semantics integration relies on trade-offs among a distributed network of regions for maximum comprehension efficiency.

Social and Personal Factors in Semantic Infusion Projects

NASA Astrophysics Data System (ADS)

West, P.; Fox, P. A.; McGuinness, D. L.

2009-12-01

As part of our semantic data framework activities across multiple, diverse disciplines we required the involvement of domain scientists, computer scientists, software engineers, data managers, and often, social scientists. This involvement from a cross-section of disciplines turns out to be a social exercise as much as it is a technical and methodical activity. Each member of the team is used to different modes of working, expectations, vocabularies, levels of participation, and incentive and reward systems. We will examine how both roles and personal responsibilities play in the development of semantic infusion projects, and how an iterative development cycle can contribute to the successful completion of such a project.

The Interaction between Semantic Representation and Episodic Memory.

PubMed

Fang, Jing; Rüther, Naima; Bellebaum, Christian; Wiskott, Laurenz; Cheng, Sen

2018-02-01

The experimental evidence on the interrelation between episodic memory and semantic memory is inconclusive. Are they independent systems, different aspects of a single system, or separate but strongly interacting systems? Here, we propose a computational role for the interaction between the semantic and episodic systems that might help resolve this debate. We hypothesize that episodic memories are represented as sequences of activation patterns. These patterns are the output of a semantic representational network that compresses the high-dimensional sensory input. We show quantitatively that the accuracy of episodic memory crucially depends on the quality of the semantic representation. We compare two types of semantic representations: appropriate representations, which means that the representation is used to store input sequences that are of the same type as those that it was trained on, and inappropriate representations, which means that stored inputs differ from the training data. Retrieval accuracy is higher for appropriate representations because the encoded sequences are less divergent than those encoded with inappropriate representations. Consistent with our model prediction, we found that human subjects remember some aspects of episodes significantly more accurately if they had previously been familiarized with the objects occurring in the episode, as compared to episodes involving unfamiliar objects. We thus conclude that the interaction with the semantic system plays an important role for episodic memory.

If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates

PubMed Central

Kuhlmann, Michael; Hofmann, Markus J.; Jacobs, Arthur M.

2017-01-01

How do humans perform difficult forced-choice evaluations, e.g., of words that have been previously rated as being neutral? Here we tested the hypothesis that in this case, the valence of semantic associates is of significant influence. From corpus based co-occurrence statistics as a measure of association strength we computed individual neighborhoods for single neutral words comprised of the 10 words with the largest association strength. We then selected neutral words according to the valence of the associated words included in the neighborhoods, which were either mostly positive, mostly negative, mostly neutral or mixed positive and negative, and tested them using a valence decision task (VDT). The data showed that the valence of semantic neighbors can predict valence judgments to neutral words. However, all but the positive neighborhood items revealed a high tendency to elicit negative responses. For the positive and negative neighborhood categories responses congruent with the neighborhood's valence were faster than incongruent responses. We interpret this effect as a semantic network process that supports the evaluation of neutral words by assessing the valence of the associative semantic neighborhood. In this perspective, valence is considered a semantic super-feature, at least partially represented in associative activation patterns of semantic networks. PMID:28348538

If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates.

PubMed

Kuhlmann, Michael; Hofmann, Markus J; Jacobs, Arthur M

2017-01-01

How do humans perform difficult forced-choice evaluations, e.g., of words that have been previously rated as being neutral? Here we tested the hypothesis that in this case, the valence of semantic associates is of significant influence. From corpus based co-occurrence statistics as a measure of association strength we computed individual neighborhoods for single neutral words comprised of the 10 words with the largest association strength. We then selected neutral words according to the valence of the associated words included in the neighborhoods, which were either mostly positive, mostly negative, mostly neutral or mixed positive and negative, and tested them using a valence decision task (VDT). The data showed that the valence of semantic neighbors can predict valence judgments to neutral words. However, all but the positive neighborhood items revealed a high tendency to elicit negative responses. For the positive and negative neighborhood categories responses congruent with the neighborhood's valence were faster than incongruent responses. We interpret this effect as a semantic network process that supports the evaluation of neutral words by assessing the valence of the associative semantic neighborhood. In this perspective, valence is considered a semantic super-feature, at least partially represented in associative activation patterns of semantic networks.

Selective Attention to Semantic and Syntactic Features Modulates Sentence Processing Networks in Anterior Temporal Cortex

PubMed Central

Rogalsky, Corianne

2009-01-01

Numerous studies have identified an anterior temporal lobe (ATL) region that responds preferentially to sentence-level stimuli. It is unclear, however, whether this activity reflects a response to syntactic computations or some form of semantic integration. This distinction is difficult to investigate with the stimulus manipulations and anomaly detection paradigms traditionally implemented. The present functional magnetic resonance imaging study addresses this question via a selective attention paradigm. Subjects monitored for occasional semantic anomalies or occasional syntactic errors, thus directing their attention to semantic integration, or syntactic properties of the sentences. The hemodynamic response in the sentence-selective ATL region (defined with a localizer scan) was examined during anomaly/error-free sentences only, to avoid confounds due to error detection. The majority of the sentence-specific region of interest was equally modulated by attention to syntactic or compositional semantic features, whereas a smaller subregion was only modulated by the semantic task. We suggest that the sentence-specific ATL region is sensitive to both syntactic and integrative semantic functions during sentence processing, with a smaller portion of this area preferentially involved in the later. This study also suggests that selective attention paradigms may be effective tools to investigate the functional diversity of networks involved in sentence processing. PMID:18669589

Semantic Web technologies for the big data in life sciences.

PubMed

Wu, Hongyan; Yamaguchi, Atsuko

2014-08-01

The life sciences field is entering an era of big data with the breakthroughs of science and technology. More and more big data-related projects and activities are being performed in the world. Life sciences data generated by new technologies are continuing to grow in not only size but also variety and complexity, with great speed. To ensure that big data has a major influence in the life sciences, comprehensive data analysis across multiple data sources and even across disciplines is indispensable. The increasing volume of data and the heterogeneous, complex varieties of data are two principal issues mainly discussed in life science informatics. The ever-evolving next-generation Web, characterized as the Semantic Web, is an extension of the current Web, aiming to provide information for not only humans but also computers to semantically process large-scale data. The paper presents a survey of big data in life sciences, big data related projects and Semantic Web technologies. The paper introduces the main Semantic Web technologies and their current situation, and provides a detailed analysis of how Semantic Web technologies address the heterogeneous variety of life sciences big data. The paper helps to understand the role of Semantic Web technologies in the big data era and how they provide a promising solution for the big data in life sciences.

Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions.

PubMed

Bratsas, Charalampos; Koutkias, Vassilis; Kaimakamis, Evangelos; Bamidis, Panagiotis; Maglaveras, Nicos

2007-01-01

Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader-narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

Investigating Students' Perceived Discipline Relevance Subsequent to Playing Educational Computer Games: A Personal Interest and Self-Determination Theory Approach

ERIC Educational Resources Information Center

Sorebo, Oystein; Haehre, Reidar

2012-01-01

The purpose of this study is to explain students' perceived relevance of playing an educational game as a means for development of discipline competence. Based on self-determination theory and the concept of personal interest, we propose that: Satisfying students' basic needs for competence, autonomy, and relatedness when playing educational games…

ICCE/ICCAI 2000 Keynote Papers.

ERIC Educational Resources Information Center

2000

This document contains the four keynote papers from ICCE/ICCAI 2000 (International Conference on Computers in Education/International Conference on Computer-Assisted Instruction). "Using Technologies To Model Student Problem Spaces" (David Jonassen) contrasts examples of semantic network, expert system, and systems modeling…

Semantics driven approach for knowledge acquisition from EMRs.

PubMed

Perera, Sujan; Henson, Cory; Thirunarayan, Krishnaprasad; Sheth, Amit; Nair, Suhas

2014-03-01

Semantic computing technologies have matured to be applicable to many critical domains such as national security, life sciences, and health care. However, the key to their success is the availability of a rich domain knowledge base. The creation and refinement of domain knowledge bases pose difficult challenges. The existing knowledge bases in the health care domain are rich in taxonomic relationships, but they lack nontaxonomic (domain) relationships. In this paper, we describe a semiautomatic technique for enriching existing domain knowledge bases with causal relationships gleaned from Electronic Medical Records (EMR) data. We determine missing causal relationships between domain concepts by validating domain knowledge against EMR data sources and leveraging semantic-based techniques to derive plausible relationships that can rectify knowledge gaps. Our evaluation demonstrates that semantic techniques can be employed to improve the efficiency of knowledge acquisition.

Students' Perceptions of Motivational Climate and Enjoyment in Finnish Physical Education: A Latent Profile Analysis.

PubMed

Jaakkola, Timo; Wang, C K John; Soini, Markus; Liukkonen, Jarmo

2015-09-01

The purpose of this study was to identify student clusters with homogenous profiles in perceptions of task- and ego-involving, autonomy, and social relatedness supporting motivational climate in school physical education. Additionally, we investigated whether different motivational climate groups differed in their enjoyment in PE. Participants of the study were 2 594 girls and 1 803 boys, aged 14-15 years. Students responded to questionnaires assessing their perception of motivational climate and enjoyment in physical education. Latent profile analyses produced a five-cluster solution labeled 1) 'low autonomy, relatedness, task, and moderate ego climate' group', 2) 'low autonomy, relatedness, and high task and ego climate, 3) 'moderate autonomy, relatedness, task and ego climate' group 4) 'high autonomy, relatedness, task, and moderate ego climate' group, and 5) 'high relatedness and task but moderate autonomy and ego climate' group. Analyses of variance showed that students in clusters 4 and 5 perceived the highest level of enjoyment whereas students in cluster 1 experienced the lowest level of enjoyment. The results showed that the students' perceptions of various motivational climates created differential levels of enjoyment in PE classes. Key pointsLatent profile analyses produced a five-cluster solution labeled 1) 'low autonomy, relatedness, task, and moderate ego climate' group', 2) 'low autonomy, relatedness, and high task and ego climate, 3) 'moderate autonomy, relatedness, task and ego climate' group 4) 'high autonomy, relatedness, task, and moderate ego climate' group, and 5) 'high relatedness and task but moderate autonomy and ego climate' group.Analyses of variance showed that clusters 4 and 5 perceived the highest level of enjoyment whereas cluster 1 experienced the lowest level of enjoyment. The results showed that the students' perceptions of motivational climate create differential levels of enjoyment in PE classes.

Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia.

PubMed

Evans, William S; Quimby, Megan; Dickey, Michael Walsh; Dickerson, Bradford C

2016-01-01

Although anomia treatments have often focused on training small sets of words in the hopes of promoting generalization to untrained items, an alternative is to directly train a larger set of words more efficiently. The current case study reports on a novel treatment for a patient with semantic variant Primary Progressive Aphasia (svPPA), in which the patient was taught to make and practice flashcards for personally-relevant words using an open-source computer program (Anki). Results show that the patient was able to relearn and retain a large subset of her studied words for up to 20 months, the full duration of the study period. At the end of treatment, she showed good retention for 139 words. While only a subset of the 591 studied overall, this is still far more words than is typically targeted in svPPA interventions. Furthermore, she showed evidence of generalization to perceptually distinct stimuli during confrontation naming and temporary gains in semantic fluency, suggesting limited gains in semantic knowledge as a result of training. This case represents a successful example of patient-centered treatment, where the patient used a computer-based intervention independently at home. It also illustrates how data captured from computer-based treatments during routine clinical care can provide valuable "practice-based evidence" for motivating further treatment research.

Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia

PubMed Central

Evans, William S.; Quimby, Megan; Dickey, Michael Walsh; Dickerson, Bradford C.

2016-01-01

Although anomia treatments have often focused on training small sets of words in the hopes of promoting generalization to untrained items, an alternative is to directly train a larger set of words more efficiently. The current case study reports on a novel treatment for a patient with semantic variant Primary Progressive Aphasia (svPPA), in which the patient was taught to make and practice flashcards for personally-relevant words using an open-source computer program (Anki). Results show that the patient was able to relearn and retain a large subset of her studied words for up to 20 months, the full duration of the study period. At the end of treatment, she showed good retention for 139 words. While only a subset of the 591 studied overall, this is still far more words than is typically targeted in svPPA interventions. Furthermore, she showed evidence of generalization to perceptually distinct stimuli during confrontation naming and temporary gains in semantic fluency, suggesting limited gains in semantic knowledge as a result of training. This case represents a successful example of patient-centered treatment, where the patient used a computer-based intervention independently at home. It also illustrates how data captured from computer-based treatments during routine clinical care can provide valuable “practice-based evidence” for motivating further treatment research. PMID:27899886

Single Sided Messaging v. 0.6.6

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

Curry, Matthew Leon; Farmer, Matthew Shane; Hassani, Amin

Single-Sided Messaging (SSM) is a portable, multitransport networking library that enables applications to leverage potential one-sided capabilities of underlying network transports. It also provides desirable semantics that services for highperformance, massively parallel computers can leverage, such as an explicit cancel operation for pending transmissions, as well as enhanced matching semantics favoring large numbers of buffers attached to a single match entry. This release supports TCP/IP, shared memory, and Infiniband.

High-performance analysis of filtered semantic graphs

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

Buluc, Aydin; Fox, Armando; Gilbert, John R.

2012-01-01

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry "attributes" of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and edges of interest, or else must first materialize a subgraph or subgraphs consisting of only the vertices and edges of interest. The filtered approach is superior due to its generality, ease of use, and memory efficiency, but may carry amore » performance cost. In the Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, the user writes filters in a high-level language, but those filters result in relatively low performance due to the bottleneck of having to call into the Python interpreter for each edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance C++ /MPI Combinatorial BLAS engine, and show that the productivity gained by using a high-level filtering language comes without sacrificing performance.« less

Peers and teachers as sources of relatedness perceptions, motivation, and affective responses in physical education.

PubMed

Cox, Anne; Duncheon, Nicole; McDavid, Lindley

2009-12-01

Research has demonstrated the importance of relatedness perceptions to self-determined motivation in physical education. Therefore, studies have begun to examine the social factors contributing to feelings of relatedness. The purpose of this study was to examine teacher (perceived emotional support) and peer (acceptance, friendship quality) relationship variables to feelings of relatedness, motivation, and affective responses in junior high physical education students (N = 411). Results revealed that perceived relatedness mediated the relationship between variables and self-determined motivation and related directly to the amount of enjoyment and worry students experienced. These findings demonstrate that relationships with both teachers and peers are important for students' relatedness perceptions, motivation, enjoyment, and worry in physical education.

Relatedness-based Multi-Entity Summarization

PubMed Central

Gunaratna, Kalpa; Yazdavar, Amir Hossein; Thirunarayan, Krishnaprasad; Sheth, Amit; Cheng, Gong

2017-01-01

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches. PMID:29051696

fMR-adaptation indicates selectivity to audiovisual content congruency in distributed clusters in human superior temporal cortex.

PubMed

van Atteveldt, Nienke M; Blau, Vera C; Blomert, Leo; Goebel, Rainer

2010-02-02

Efficient multisensory integration is of vital importance for adequate interaction with the environment. In addition to basic binding cues like temporal and spatial coherence, meaningful multisensory information is also bound together by content-based associations. Many functional Magnetic Resonance Imaging (fMRI) studies propose the (posterior) superior temporal cortex (STC) as the key structure for integrating meaningful multisensory information. However, a still unanswered question is how superior temporal cortex encodes content-based associations, especially in light of inconsistent results from studies comparing brain activation to semantically matching (congruent) versus nonmatching (incongruent) multisensory inputs. Here, we used fMR-adaptation (fMR-A) in order to circumvent potential problems with standard fMRI approaches, including spatial averaging and amplitude saturation confounds. We presented repetitions of audiovisual stimuli (letter-speech sound pairs) and manipulated the associative relation between the auditory and visual inputs (congruent/incongruent pairs). We predicted that if multisensory neuronal populations exist in STC and encode audiovisual content relatedness, adaptation should be affected by the manipulated audiovisual relation. The results revealed an occipital-temporal network that adapted independently of the audiovisual relation. Interestingly, several smaller clusters distributed over superior temporal cortex within that network, adapted stronger to congruent than to incongruent audiovisual repetitions, indicating sensitivity to content congruency. These results suggest that the revealed clusters contain multisensory neuronal populations that encode content relatedness by selectively responding to congruent audiovisual inputs, since unisensory neuronal populations are assumed to be insensitive to the audiovisual relation. These findings extend our previously revealed mechanism for the integration of letters and speech sounds and demonstrate that fMR-A is sensitive to multisensory congruency effects that may not be revealed in BOLD amplitude per se.

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.

A Semantic Based Policy Management Framework for Cloud Computing Environments

ERIC Educational Resources Information Center

Takabi, Hassan

2013-01-01

Cloud computing paradigm has gained tremendous momentum and generated intensive interest. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this dissertation, we mainly focus on issues related to policy management and access…

Centrality-based Selection of Semantic Resources for Geosciences

NASA Astrophysics Data System (ADS)

Cerba, Otakar; Jedlicka, Karel

2017-04-01

Semantical questions intervene almost in all disciplines dealing with geographic data and information, because relevant semantics is crucial for any way of communication and interaction among humans as well as among machines. But the existence of such a large number of different semantic resources (such as various thesauri, controlled vocabularies, knowledge bases or ontologies) makes the process of semantics implementation much more difficult and complicates the use of the advantages of semantics. This is because in many cases users are not able to find the most suitable resource for their purposes. The research presented in this paper introduces a methodology consisting of an analysis of identical relations in Linked Data space, which covers a majority of semantic resources, to find a suitable resource of semantic information. Identical links interconnect representations of an object or a concept in various semantic resources. Therefore this type of relations is considered to be crucial from the view of Linked Data, because these links provide new additional information, including various views on one concept based on different cultural or regional aspects (so-called social role of Linked Data). For these reasons it is possible to declare that one reasonable criterion for feasible semantic resources for almost all domains, including geosciences, is their position in a network of interconnected semantic resources and level of linking to other knowledge bases and similar products. The presented methodology is based on searching of mutual connections between various instances of one concept using "follow your nose" approach. The extracted data on interconnections between semantic resources are arranged to directed graphs and processed by various metrics patterned on centrality computing (degree, closeness or betweenness centrality). Semantic resources recommended by the research could be used for providing semantically described keywords for metadata records or as names of items in data models. Such an approach enables much more efficient data harmonization, integration, sharing and exploitation. * * * * This publication was supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports. This publication was supported by project Data-Driven Bioeconomy (DataBio) from the ICT-15-2016-2017, Big Data PPP call.

Revealing the Dynamic Modulations That Underpin a Resilient Neural Network for Semantic Cognition: An fMRI Investigation in Patients With Anterior Temporal Lobe Resection.

PubMed

Rice, Grace E; Caswell, Helen; Moore, Perry; Lambon Ralph, Matthew A; Hoffman, Paul

2018-06-06

One critical feature of any well-engineered system is its resilience to perturbation and minor damage. The purpose of the current study was to investigate how resilience is achieved in higher cognitive systems, which we explored through the domain of semantic cognition. Convergent evidence implicates the bilateral anterior temporal lobes (ATLs) as a conceptual knowledge hub. While bilateral damage to this region produces profound semantic impairment, unilateral atrophy/resection or transient perturbation has a limited effect. Two neural mechanisms might underpin this resilience to unilateral ATL damage: 1) the undamaged ATL upregulates its activation in order to compensate; and/or 2) prefrontal regions involved in control of semantic retrieval upregulate to compensate for the impoverished semantic representations that follow from ATL damage. To test these possibilities, 34 postsurgical temporal lobe epilepsy patients and 20 age-matched controls were scanned whilst completing semantic tasks. Pictorial tasks, which produced bilateral frontal and temporal activation, showed few activation differences between patients and control participants. Written word tasks, however, produced a left-lateralized activation pattern and greater differences between the groups. Patients with right ATL resection increased activation in left inferior frontal gyrus (IFG). Patients with left ATL resection upregulated both the right ATL and right IFG. Consistent with recent computational models, these results indicate that 1) written word semantic processing in patients with ATL resection is supported by upregulation of semantic knowledge and control regions, principally in the undamaged hemisphere, and 2) pictorial semantic processing is less affected, presumably because it draws on a more bilateral network.

The effects of semantic congruency: a research of audiovisual P300-speller.

PubMed

Cao, Yong; An, Xingwei; Ke, Yufeng; Jiang, Jin; Yang, Hanjun; Chen, Yuqian; Jiao, Xuejun; Qi, Hongzhi; Ming, Dong

2017-07-25

Over the past few decades, there have been many studies of aspects of brain-computer interface (BCI). Of particular interests are event-related potential (ERP)-based BCI spellers that aim at helping mental typewriting. Nowadays, audiovisual unimodal stimuli based BCI systems have attracted much attention from researchers, and most of the existing studies of audiovisual BCIs were based on semantic incongruent stimuli paradigm. However, no related studies had reported that whether there is difference of system performance or participant comfort between BCI based on semantic congruent paradigm and that based on semantic incongruent paradigm. The goal of this study was to investigate the effects of semantic congruency in system performance and participant comfort in audiovisual BCI. Two audiovisual paradigms (semantic congruent and incongruent) were adopted, and 11 healthy subjects participated in the experiment. High-density electrical mapping of ERPs and behavioral data were measured for the two stimuli paradigms. The behavioral data indicated no significant difference between congruent and incongruent paradigms for offline classification accuracy. Nevertheless, eight of the 11 participants reported their priority to semantic congruent experiment, two reported no difference between the two conditions, and only one preferred the semantic incongruent paradigm. Besides, the result indicted that higher amplitude of ERP was found in incongruent stimuli based paradigm. In a word, semantic congruent paradigm had a better participant comfort, and maintained the same recognition rate as incongruent paradigm. Furthermore, our study suggested that the paradigm design of spellers must take both system performance and user experience into consideration rather than merely pursuing a larger ERP response.

Effects of among-offspring relatedness on the origins and evolution of parental care and filial cannibalism.

PubMed

Bonsall, M B; Klug, H

2011-06-01

Parental care is expected to increase the likelihood of offspring survival at the cost of investment in future reproductive success. However, alternative parental behaviours, such as filial cannibalism, can decrease current reproductive success and consequently individual fitness. We evaluate the role of among-offspring relatedness on the evolution of parental care and filial cannibalism. Building on our previous work, we show how the evolution of care is influenced by the effect of among-offspring relatedness on both the strength of competition and filial cannibalism. When there is a positive relationship between among-offspring competition and relatedness, parental care will be favoured when among-offspring relatedness is relatively low, and the maintenance of both care and no-care strategies is expected. If the relationship between among-offspring competition and relatedness is negative, parental care is most strongly favoured when broods contain highly related offspring. Further, we highlight the range of conditions over which the level of this among-offspring relatedness can affect the co-occurrence of different care/no care and cannibalism/no cannibalism strategies. Coexistence of multiple strategies is independent of the effects of among-offspring relatedness on cannibalism but more likely when among-offspring relatedness and competition are positively associated. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Experimental evolution reveals that high relatedness protects multicellular cooperation from cheaters

PubMed Central

Bastiaans, Eric; Debets, Alfons J. M.; Aanen, Duur K.

2016-01-01

In multicellular organisms, there is a potential risk that cheating mutants gain access to the germline. Development from a single-celled zygote resets relatedness among cells to its maximum value each generation, which should accomplish segregation of cheating mutants from non-cheaters and thereby protect multicellular cooperation. Here we provide the crucial direct comparison between high- and low-relatedness conditions to test this hypothesis. We allow two variants of the fungus Neurospora crassa to evolve, one with and one without the ability to form chimeras with other individuals, thus generating two relatedness levels. While multicellular cooperation remains high in the high-relatedness lines, it significantly decreases in all replicate low-relatedness lines, resulting in an average threefold decrease in spore yield. This reduction is caused by cheating mutants with reduced investment in somatic functions, but increased competitive success when fusing with non-cheaters. Our experiments demonstrate that high genetic relatedness is crucial to sustain multicellular cooperation. PMID:27139112

Representational constraints on children's suggestibility.

PubMed

Ceci, Stephen J; Papierno, Paul B; Kulkofsky, Sarah

2007-06-01

In a multistage experiment, twelve 4- and 9-year-old children participated in a triad rating task. Their ratings were mapped with multidimensional scaling, from which euclidean distances were computed to operationalize semantic distance between items in target pairs. These children and age-mates then participated in an experiment that employed these target pairs in a story, which was followed by a misinformation manipulation. Analyses linked individual and developmental differences in suggestibility to children's representations of the target items. Semantic proximity was a strong predictor of differences in suggestibility: The closer a suggested distractor was to the original item's representation, the greater was the distractor's suggestive influence. The triad participants' semantic proximity subsequently served as the basis for correctly predicting memory performance in the larger group. Semantic proximity enabled a priori counterintuitive predictions of reverse age-related trends to be confirmed whenever the distance between representations of items in a target pair was greater for younger than for older children.

Not all triads are created equal: further support for the importance of visual and semantic proximity in object identification.

PubMed

Schweizer, Tom A; Dixon, Mike J; Desmarais, Geneviève; Smith, Stephen D

2002-01-01

Identification deficits were investigated in ELM, a temporal lobe stroke patient with category-specific deficits. We replicated previous work done on FS, a patient with category specific deficits as a result of herpes viral encephalitis. ELM was tested using novel, computer generated shapes that were paired with artifact labels. We paired semantically close or disparate labels to shapes and ELM attempted to learn these pairings. Overall, ELM's shape-label confusions were most detrimentally affected when we used labels that referred to objects that were visually and semantically close. However, as with FS, ELM had as many errors when shapes were paired with the labels "donut," "tire," and "washer" as he did when they were paired with visually and semantically close artifact labels. Two explanations are put forth to account for the anomalous performance by both patients on the triad of donut-tire-washer.

Towards semantically sensitive text clustering: a feature space modeling technology based on dimension extension.

PubMed

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach.

Developing A Web-based User Interface for Semantic Information Retrieval

NASA Technical Reports Server (NTRS)

Berrios, Daniel C.; Keller, Richard M.

2003-01-01

While there are now a number of languages and frameworks that enable computer-based systems to search stored data semantically, the optimal design for effective user interfaces for such systems is still uncle ar. Such interfaces should mask unnecessary query detail from users, yet still allow them to build queries of arbitrary complexity without significant restrictions. We developed a user interface supporting s emantic query generation for Semanticorganizer, a tool used by scient ists and engineers at NASA to construct networks of knowledge and dat a. Through this interface users can select node types, node attribute s and node links to build ad-hoc semantic queries for searching the S emanticOrganizer network.

Towards Semantically Sensitive Text Clustering: A Feature Space Modeling Technology Based on Dimension Extension

PubMed Central

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach. PMID:25794172

CelOWS: an ontology based framework for the provision of semantic web services related to biological models.

PubMed

Matos, Ely Edison; Campos, Fernanda; Braga, Regina; Palazzi, Daniele

2010-02-01

The amount of information generated by biological research has lead to an intensive use of models. Mathematical and computational modeling needs accurate description to share, reuse and simulate models as formulated by original authors. In this paper, we introduce the Cell Component Ontology (CelO), expressed in OWL-DL. This ontology captures both the structure of a cell model and the properties of functional components. We use this ontology in a Web project (CelOWS) to describe, query and compose CellML models, using semantic web services. It aims to improve reuse and composition of existent components and allow semantic validation of new models.

Parametric effects of syntactic-semantic conflict in Broca's area during sentence processing.

PubMed

Thothathiri, Malathi; Kim, Albert; Trueswell, John C; Thompson-Schill, Sharon L

2012-03-01

The hypothesized role of Broca's area in sentence processing ranges from domain-general executive function to domain-specific computation that is specific to certain syntactic structures. We examined this issue by manipulating syntactic structure and conflict between syntactic and semantic cues in a sentence processing task. Functional neuroimaging revealed that activation within several Broca's area regions of interest reflected the parametric variation in syntactic-semantic conflict. These results suggest that Broca's area supports sentence processing by mediating between multiple incompatible constraints on sentence interpretation, consistent with this area's well-known role in conflict resolution in other linguistic and non-linguistic tasks. Copyright © 2011 Elsevier Inc. All rights reserved.

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

PubMed Central

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language. PMID:22615554

The layer-oriented approach to declarative languages for biological modeling.

PubMed

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

COEUS: “semantic web in a box” for biomedical applications

PubMed Central

2012-01-01

Background As the “omics” revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter’s complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. Results COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a “semantic web in a box” approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. Conclusions The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/. PMID:23244467

Convergence of Health Level Seven Version 2 Messages to Semantic Web Technologies for Software-Intensive Systems in Telemedicine Trauma Care.

PubMed

Menezes, Pedro Monteiro; Cook, Timothy Wayne; Cavalini, Luciana Tricai

2016-01-01

To present the technical background and the development of a procedure that enriches the semantics of Health Level Seven version 2 (HL7v2) messages for software-intensive systems in telemedicine trauma care. This study followed a multilevel model-driven approach for the development of semantically interoperable health information systems. The Pre-Hospital Trauma Life Support (PHTLS) ABCDE protocol was adopted as the use case. A prototype application embedded the semantics into an HL7v2 message as an eXtensible Markup Language (XML) file, which was validated against an XML schema that defines constraints on a common reference model. This message was exchanged with a second prototype application, developed on the Mirth middleware, which was also used to parse and validate both the original and the hybrid messages. Both versions of the data instance (one pure XML, one embedded in the HL7v2 message) were equally validated and the RDF-based semantics recovered by the receiving side of the prototype from the shared XML schema. This study demonstrated the semantic enrichment of HL7v2 messages for intensive-software telemedicine systems for trauma care, by validating components of extracts generated in various computing environments. The adoption of the method proposed in this study ensures the compliance of the HL7v2 standard in Semantic Web technologies.

COEUS: "semantic web in a box" for biomedical applications.

PubMed

Lopes, Pedro; Oliveira, José Luís

2012-12-17

As the "omics" revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter's complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a "semantic web in a box" approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/.

A DNA-based semantic fusion model for remote sensing data.

PubMed

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

A DNA-Based Semantic Fusion Model for Remote Sensing Data

PubMed Central

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H.

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology. PMID:24116207

WebGIS based on semantic grid model and web services

NASA Astrophysics Data System (ADS)

Zhang, WangFei; Yue, CaiRong; Gao, JianGuo

2009-10-01

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

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

PubMed Central

Huang, Jingshan; Dou, Dejing; Dang, Jiangbo; Pardue, J Harold; Qin, Xiao; Huan, Jun; Gerthoffer, William T; Tan, Ming

2012-01-01

Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research. PMID:22371823

Fast Semantic Segmentation of 3d Point Clouds with Strongly Varying Density

NASA Astrophysics Data System (ADS)

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

2016-06-01

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point's (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

related: an R package for analysing pairwise relatedness from codominant molecular markers.

PubMed

Pew, Jack; Muir, Paul H; Wang, Jinliang; Frasier, Timothy R

2015-05-01

Analyses of pairwise relatedness represent a key component to addressing many topics in biology. However, such analyses have been limited because most available programs provide a means to estimate relatedness based on only a single estimator, making comparison across estimators difficult. Second, all programs to date have been platform specific, working only on a specific operating system. This has the undesirable outcome of making choice of relatedness estimator limited by operating system preference, rather than being based on scientific rationale. Here, we present a new R package, called related, that can calculate relatedness based on seven estimators, can account for genotyping errors, missing data and inbreeding, and can estimate 95% confidence intervals. Moreover, simulation functions are provided that allow for easy comparison of the performance of different estimators and for analyses of how much resolution to expect from a given data set. Because this package works in R, it is platform independent. Combined, this functionality should allow for more appropriate analyses and interpretation of pairwise relatedness and will also allow for the integration of relatedness data into larger R workflows. © 2014 John Wiley & Sons Ltd.

Relating UMLS semantic types and task-based ontology to computer-interpretable clinical practice guidelines.

PubMed

Kumar, Anand; Ciccarese, Paolo; Quaglini, Silvana; Stefanelli, Mario; Caffi, Ezio; Boiocchi, Lorenzo

2003-01-01

Medical knowledge in clinical practice guideline (GL) texts is the source of task-based computer-interpretable clinical guideline models (CIGMs). We have used Unified Medical Language System (UMLS) semantic types (STs) to understand the percentage of GL text which belongs to a particular ST. We also use UMLS semantic network together with the CIGM-specific ontology to derive a semantic meaning behind the GL text. In order to achieve this objective, we took nine GL texts from the National Guideline Clearinghouse (NGC) and marked up the text dealing with a particular ST. The STs we took into consideration were restricted taking into account the requirements of a task-based CIGM. We used DARPA Agent Markup Language and Ontology Inference Layer (DAML + OIL) to create the UMLS and CIGM specific semantic network. For the latter, as a bench test, we used the 1999 WHO-International Society of Hypertension Guidelines for the Management of Hypertension. We took into consideration the UMLS STs closest to the clinical tasks. The percentage of the GL text dealing with the ST "Health Care Activity" and subtypes "Laboratory Procedure", "Diagnostic Procedure" and "Therapeutic or Preventive Procedure" were measured. The parts of text belonging to other STs or comments were separated. A mapping of terms belonging to other STs was done to the STs under "HCA" for representation in DAML + OIL. As a result, we found that the three STs under "HCA" were the predominant STs present in the GL text. In cases where the terms of related STs existed, they were mapped into one of the three STs. The DAML + OIL representation was able to describe the hierarchy in task-based CIGMs. To conclude, we understood that the three STs could be used to represent the semantic network of the task-bases CIGMs. We identified some mapping operators which could be used for the mapping of other STs into these.

BioPortal: An Open-Source Community-Based Ontology Repository

NASA Astrophysics Data System (ADS)

Noy, N.; NCBO Team

2011-12-01

Advances in computing power and new computational techniques have changed the way researchers approach science. In many fields, one of the most fruitful approaches has been to use semantically aware software to break down the barriers among disparate domains, systems, data sources, and technologies. Such software facilitates data aggregation, improves search, and ultimately allows the detection of new associations that were previously not detectable. Achieving these analyses requires software systems that take advantage of the semantics and that can intelligently negotiate domains and knowledge sources, identifying commonality across systems that use different and conflicting vocabularies, while understanding apparent differences that may be concealed by the use of superficially similar terms. An ontology, a semantically rich vocabulary for a domain of interest, is the cornerstone of software for bridging systems, domains, and resources. However, as ontologies become the foundation of all semantic technologies in e-science, we must develop an infrastructure for sharing ontologies, finding and evaluating them, integrating and mapping among them, and using ontologies in applications that help scientists process their data. BioPortal [1] is an open-source on-line community-based ontology repository that has been used as a critical component of semantic infrastructure in several domains, including biomedicine and bio-geochemical data. BioPortal, uses the social approaches in the Web 2.0 style to bring structure and order to the collection of biomedical ontologies. It enables users to provide and discuss a wide array of knowledge components, from submitting the ontologies themselves, to commenting on and discussing classes in the ontologies, to reviewing ontologies in the context of their own ontology-based projects, to creating mappings between overlapping ontologies and discussing and critiquing the mappings. Critically, it provides web-service access to all its content, enabling its integration in semantically enriched applications. [1] Noy, N.F., Shah, N.H., et al., BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res, 2009. 37(Web Server issue): p. W170-3.

Drug knowledge expressed as computable semantic triples.

PubMed

Elkin, Peter L; Carter, John S; Nabar, Manasi; Tuttle, Mark; Lincoln, Michael; Brown, Steven H

2011-01-01

The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,000 deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CT™ codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of "Drug" HasIndication "SNOMED CT™". The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care.

Computationally Efficient Clustering of Audio-Visual Meeting Data

NASA Astrophysics Data System (ADS)

Hung, Hayley; Friedland, Gerald; Yeo, Chuohao

This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the participants in a group discussion using the example of business meeting recordings. The recording setup involves relatively few audio-visual sensors, comprising a limited number of cameras and microphones. We first demonstrate computationally efficient algorithms that can identify who spoke and when, a problem in speech processing known as speaker diarization. We also extract visual activity features efficiently from MPEG4 video by taking advantage of the processing that was already done for video compression. Then, we present a method of associating the audio-visual data together so that the content of each participant can be managed individually. The methods presented in this article can be used as a principal component that enables many higher-level semantic analysis tasks needed in search, retrieval, and navigation.

Computational methods to extract meaning from text and advance theories of human cognition.

PubMed

McNamara, Danielle S

2011-01-01

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. Copyright © 2010 Cognitive Science Society, Inc.

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques

PubMed Central

2014-01-01

Background Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. Results We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. Conclusions SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats. PMID:25093070

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques.

PubMed

Bölling, Christian; Weidlich, Michael; Holzhütter, Hermann-Georg

2014-01-01

Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats.

When Singular and Plural are Both Grammatical: Semantic and Morphophonological Effects in Agreement

PubMed Central

Mirković, Jelena; MacDonald, Maryellen C.

2013-01-01

The utterance planning processes allowing speakers to produce agreement between subjects and verbs (the catspl arepl asleep) have been the topic of extensive study as a window into language production mechanisms. A key question has been the extent to which agreement processing is influenced by semantic and phonological factors. Most prior studies have found limited effects of non-syntactic, particularly phonological factors, leading to conclusions that agreement is computed by a process influenced strongly by syntactic factors and with only a minor contribution of semantics. This conclusion may have been influenced by use of agreement error data as the main dependent variable, because errors are rare, potentially reducing sensitivity to the interaction of several factors. Two studies investigate agreement processing in Serbian, which allows both singular and plural verb forms to agree with plural nouns in some constructions. We use these constructions to further investigate the contribution of semantic factors to agreement, by manipulating levels of individuation of the members of a set. In addition, we investigate the effect of morphophonological homophony onto the participants’ productions of agreeing forms. The findings are discussed in the context of three models of agreement (Marking & Morphing, competition and controller misidentification), which differ in the extent to which they allow the influence of non-syntactic factors on agreement. We also compare the behavioral findings with the predictions of four computational implementations of the Marking & Morphing account. We discuss the implications of the behavioral and computational findings for models of agreement and the language production more broadly. Rosemary: Some biscuits or a piece of cake… ‘goes’ or ‘go’ better with an afternoon tea? PMID:24039340

On Propagating Interpersonal Trust in Social Networks

NASA Astrophysics Data System (ADS)

Ziegler, Cai-Nicolas

The age of information glut has fostered the proliferation of data and documents on the Web, created by man and machine alike. Hence, there is an enormous wealth of minable knowledge that is yet to be extracted, in particular, on the Semantic Web. However, besides understanding information stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contribution to Semantic Web trust management through this work is twofold. First, we introduce a classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we devise an advocacy for local group trust metrics, guiding us to the second part, which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion. Moreover, we provide extensions for the Appleseed nucleus that make our trust metric handle distrust statements.

Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

PubMed

Uthayan, K R; Mala, G S Anandha

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

Organizing Diverse, Distributed Project Information

NASA Technical Reports Server (NTRS)

Keller, Richard M.

2003-01-01

SemanticOrganizer is a software application designed to organize and integrate information generated within a distributed organization or as part of a project that involves multiple, geographically dispersed collaborators. SemanticOrganizer incorporates the capabilities of database storage, document sharing, hypermedia navigation, and semantic-interlinking into a system that can be customized to satisfy the specific information-management needs of different user communities. The program provides a centralized repository of information that is both secure and accessible to project collaborators via the World Wide Web. SemanticOrganizer's repository can be used to collect diverse information (including forms, documents, notes, data, spreadsheets, images, and sounds) from computers at collaborators work sites. The program organizes the information using a unique network-structured conceptual framework, wherein each node represents a data record that contains not only the original information but also metadata (in effect, standardized data that characterize the information). Links among nodes express semantic relationships among the data records. The program features a Web interface through which users enter, interlink, and/or search for information in the repository. By use of this repository, the collaborators have immediate access to the most recent project information, as well as to archived information. A key advantage to SemanticOrganizer is its ability to interlink information together in a natural fashion using customized terminology and concepts that are familiar to a user community.

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

PubMed

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

2017-04-01

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

Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

PubMed Central

Uthayan, K. R.; Anandha Mala, G. S.

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology. PMID:25922851

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

PubMed

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

2013-09-01

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

Measuring content overlap during handoff communication using distributional semantics: An exploratory study.

PubMed

Abraham, Joanna; Kannampallil, Thomas G; Srinivasan, Vignesh; Galanter, William L; Tagney, Gail; Cohen, Trevor

2017-01-01

We develop and evaluate a methodological approach to measure the degree and nature of overlap in handoff communication content within and across clinical professions. This extensible, exploratory approach relies on combining techniques from conversational analysis and distributional semantics. We audio-recorded handoff communication of residents and nurses on the General Medicine floor of a large academic hospital (n=120 resident and n=120 nurse handoffs). We measured semantic similarity, a proxy for content overlap, between resident-resident and nurse-nurse communication using multiple steps: a qualitative conversational content analysis; an automated semantic similarity analysis using Reflective Random Indexing (RRI); and comparing semantic similarity generated by RRI analysis with human ratings of semantic similarity. There was significant association between the semantic similarity as computed by the RRI method and human rating (ρ=0.88). Based on the semantic similarity scores, content overlap was relatively higher for content related to patient active problems, assessment of active problems, patient-identifying information, past medical history, and medications/treatments. In contrast, content overlap was limited on content related to allergies, family-related information, code status, and anticipatory guidance. Our approach using RRI analysis provides new opportunities for characterizing the nature and degree of overlap in handoff communication. Although exploratory, this method provides a basis for identifying content that can be used for determining shared understanding across clinical professions. Additionally, this approach can inform the development of flexibly standardized handoff tools that reflect clinical content that are most appropriate for fostering shared understanding during transitions of care. Copyright © 2016 Elsevier Inc. All rights reserved.

Toward Agent Programs with Circuit Semantics

NASA Technical Reports Server (NTRS)

Nilsson, Nils J.

1992-01-01

New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments-environments in which the agent's current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of 'circuit semantics' for programs based on 'teleo-reactive trees' is introduced. Program execution builds a combinational circuit which receives sensory inputs and controls actions. These formalisms embody a high degree of inherent conditionality and thus yield programs that are suitably reactive to their environments. At the same time, the actions computed by the programs are guided by the overall goals of the agent. The paper also speculates about how programs using these ideas could be automatically generated by artificial intelligence planning systems and adapted by learning methods.

Genotyping-by-sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias.

PubMed

Attard, Catherine R M; Beheregaray, Luciano B; Möller, Luciana M

2018-05-01

There has been remarkably little attention to using the high resolution provided by genotyping-by-sequencing (i.e., RADseq and similar methods) for assessing relatedness in wildlife populations. A major hurdle is the genotyping error, especially allelic dropout, often found in this type of data that could lead to downward-biased, yet precise, estimates of relatedness. Here, we assess the applicability of genotyping-by-sequencing for relatedness inferences given its relatively high genotyping error rate. Individuals of known relatedness were simulated under genotyping error, allelic dropout and missing data scenarios based on an empirical ddRAD data set, and their true relatedness was compared to that estimated by seven relatedness estimators. We found that an estimator chosen through such analyses can circumvent the influence of genotyping error, with the estimator of Ritland (Genetics Research, 67, 175) shown to be unaffected by allelic dropout and to be the most accurate when there is genotyping error. We also found that the choice of estimator should not rely solely on the strength of correlation between estimated and true relatedness as a strong correlation does not necessarily mean estimates are close to true relatedness. We also demonstrated how even a large SNP data set with genotyping error (allelic dropout or otherwise) or missing data still performs better than a perfectly genotyped microsatellite data set of tens of markers. The simulation-based approach used here can be easily implemented by others on their own genotyping-by-sequencing data sets to confirm the most appropriate and powerful estimator for their data. © 2017 John Wiley & Sons Ltd.

Fully convolutional network with cluster for semantic segmentation

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

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

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words "lick" or "pick") and between affirmative and negated sentence meanings. Copyright © 2017 Grisoni et al.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words “lick” or “pick”) and between affirmative and negated sentence meanings. PMID:28411271

Computer-Based and Paper-Based Measurement of Semantic Knowledge

DTIC Science & Technology

1989-01-01

of Personality Assessment , 34, 353-361. McArthur, D. L., & Choppin, B. H. (1984). Computerized diagnostic testing. Journal 15 of Educational...Computers in Human Behavior, 1, 49-58. Lushene, R. E., O’Neii, H. F., & Dunn, T. (1974). Equivalent validity of a completely computerized MMPI. Journal

Developing Expert Systems for the Analysis of Syntactic and Semantic Patterns.

ERIC Educational Resources Information Center

Hellwig, Harold H.

Noting that expert computer systems respond to various contexts in terms of knowledge representation, this paper explains that heuristic rules of production, procedural representation, and frame representation have been adapted to such areas as medical diagnosis, signal interpretation, design and planning of electrical circuits and computer system…

A Framework for the Specification of the Semantics and the Dynamics of Instructional Applications

ERIC Educational Resources Information Center

Buendia-Garcia, Felix; Diaz, Paloma

2003-01-01

An instructional application consists of a set of resources and activities to implement interacting, interrelated, and structured experiences oriented towards achieving specific educational objectives. The development of computer-based instructional applications has to follow a well defined process, so models for computer-based instructional…

Convergence of Health Level Seven Version 2 Messages to Semantic Web Technologies for Software-Intensive Systems in Telemedicine Trauma Care

PubMed Central

Cook, Timothy Wayne; Cavalini, Luciana Tricai

2016-01-01

Objectives To present the technical background and the development of a procedure that enriches the semantics of Health Level Seven version 2 (HL7v2) messages for software-intensive systems in telemedicine trauma care. Methods This study followed a multilevel model-driven approach for the development of semantically interoperable health information systems. The Pre-Hospital Trauma Life Support (PHTLS) ABCDE protocol was adopted as the use case. A prototype application embedded the semantics into an HL7v2 message as an eXtensible Markup Language (XML) file, which was validated against an XML schema that defines constraints on a common reference model. This message was exchanged with a second prototype application, developed on the Mirth middleware, which was also used to parse and validate both the original and the hybrid messages. Results Both versions of the data instance (one pure XML, one embedded in the HL7v2 message) were equally validated and the RDF-based semantics recovered by the receiving side of the prototype from the shared XML schema. Conclusions This study demonstrated the semantic enrichment of HL7v2 messages for intensive-software telemedicine systems for trauma care, by validating components of extracts generated in various computing environments. The adoption of the method proposed in this study ensures the compliance of the HL7v2 standard in Semantic Web technologies. PMID:26893947

Laboratory for Computer Science Progress Report 16, 1 July 1978 - 30 June 1979,

DTIC Science & Technology

1980-08-01

name strongly distinguishes the XLMS node from ordinary nameless semantic network nodes. The name of a node has two parts: the " genus ", itself a node...and the "specializer", a node or an atomic symbol. The genus and specializer of a node are almost always semantically meaningful, though their...meaning is almost never suppliec by XLMS, but rather by some system built on top of XLMS. The genus of a node almost always plays a crucial role in its

A data base processor semantics specification package

NASA Technical Reports Server (NTRS)

Fishwick, P. A.

1983-01-01

A Semantics Specification Package (DBPSSP) for the Intel Data Base Processor (DBP) is defined. DBPSSP serves as a collection of cross assembly tools that allow the analyst to assemble request blocks on the host computer for passage to the DBP. The assembly tools discussed in this report may be effectively used in conjunction with a DBP compatible data communications protocol to form a query processor, precompiler, or file management system for the database processor. The source modules representing the components of DBPSSP are fully commented and included.

Social structure of collared peccaries (Pecari tajacu): does relatedness matter?

PubMed

Biondo, Cibele; Izar, Patrícia; Miyaki, Cristina Y; Bussab, Vera S R

2014-11-01

Relatedness is considered an important factor in shaping social structure as the association among kin might facilitate cooperation via inclusive fitness benefits. We addressed here the influence of relatedness on the social structure of a Neotropical ungulate, the collared peccary (Pecari tajacu). As peccaries are highly social and cooperative, live in stable cohesive herds and show certain degree of female philopatry and high mean relatedness within herds, we hypothesized that kin would be spatially closer and display more amicable and less agonistic interactions than non-kin. We recorded spatial association patterns and rates of interactions of two captive groups. Pairwise relatedness was calculated based on microsatellite data. As predicted, we found that kin were spatially closer than non-kin, which suggests that relatedness is a good predictor of spatial association in peccaries. However, relatedness did not predict the rates of social interactions. Although our results indirectly indicate some role of sex, age and familiarity, further studies are needed to clarify the factors that shape the rates of interactions in collared peccaries. This article is part of a Special Issue entitled: Neotropical Behaviour. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text.

PubMed

Altszyler, Edgar; Ribeiro, Sidarta; Sigman, Mariano; Fernández Slezak, Diego

2017-11-01

Computer-based dreams content analysis relies on word frequencies within predefined categories in order to identify different elements in text. As a complementary approach, we explored the capabilities and limitations of word-embedding techniques to identify word usage patterns among dream reports. These tools allow us to quantify words associations in text and to identify the meaning of target words. Word-embeddings have been extensively studied in large datasets, but only a few studies analyze semantic representations in small corpora. To fill this gap, we compared Skip-gram and Latent Semantic Analysis (LSA) capabilities to extract semantic associations from dream reports. LSA showed better performance than Skip-gram in small size corpora in two tests. Furthermore, LSA captured relevant word associations in dream collection, even in cases with low-frequency words or small numbers of dreams. Word associations in dreams reports can thus be quantified by LSA, which opens new avenues for dream interpretation and decoding. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Towards a semantic PACS: Using Semantic Web technology to represent imaging data.

PubMed

Van Soest, Johan; Lustberg, Tim; Grittner, Detlef; Marshall, M Scott; Persoon, Lucas; Nijsten, Bas; Feltens, Peter; Dekker, Andre

2014-01-01

The DICOM standard is ubiquitous within medicine. However, improved DICOM semantics would significantly enhance search operations. Furthermore, databases of current PACS systems are not flexible enough for the demands within image analysis research. In this paper, we investigated if we can use Semantic Web technology, to store and represent metadata of DICOM image files, as well as linking additional computational results to image metadata. Therefore, we developed a proof of concept containing two applications: one to store commonly used DICOM metadata in an RDF repository, and one to calculate imaging biomarkers based on DICOM images, and store the biomarker values in an RDF repository. This enabled us to search for all patients with a gross tumor volume calculated to be larger than 50 cc. We have shown that we can successfully store the DICOM metadata in an RDF repository and are refining our proof of concept with regards to volume naming, value representation, and the applications themselves.

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.

Temporal Relatedness: Personality and Behavioral Correlates

ERIC Educational Resources Information Center

Getsinger, Stephen H.

1975-01-01

Two studies explored the relationship of temporal relatedness to self actualization, sex, and certain temporal behaviors. Subjects who obtained higher time-relatedness scores demonstrated greater self-actualization, evaluated the present time mode more positively, overestimated time intervals in an estimation task, and performed less accurately in…

Working memory training and semantic structuring improves remembering future events, not past events.

PubMed

Richter, Kim Merle; Mödden, Claudia; Eling, Paul; Hildebrandt, Helmut

2015-01-01

Objectives. Memory training in combination with practice in semantic structuring and word fluency has been shown to improve memory performance. This study investigated the efficacy of a working memory training combined with exercises in semantic structuring and word fluency and examined whether training effects generalize to other cognitive tasks. Methods. In this double-blind randomized control study, 36 patients with memory impairments following brain damage were allocated to either the experimental or the active control condition, with both groups receiving 9 hours of therapy. The experimental group received a computer-based working memory training and exercises in word fluency and semantic structuring. The control group received the standard memory therapy provided in the rehabilitation center. Patients were tested on a neuropsychological test battery before and after therapy, resulting in composite scores for working memory; immediate, delayed, and prospective memory; word fluency; and attention. Results. The experimental group improved significantly in working memory and word fluency. The training effects also generalized to prospective memory tasks. No specific effect on episodic memory could be demonstrated. Conclusion. Combined treatment of working memory training with exercises in semantic structuring is an effective method for cognitive rehabilitation of organic memory impairment. © The Author(s) 2014.

A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool.

PubMed

Mazandu, Gaston K; Chimusa, Emile R; Mbiyavanga, Mamana; Mulder, Nicola J

2016-02-01

Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs. A-DaGO-Fun is freely available to the research community at http://web.cbio.uct.ac.za/ITGOM/adagofun. It is implemented in Linux using Python under free software (GNU General Public Licence). gmazandu@cbio.uct.ac.za or Nicola.Mulder@uct.ac.za Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

NASA Astrophysics Data System (ADS)

Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

2016-09-01

Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

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.

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

PubMed

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

2015-01-01

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

Discrepancies in Autonomy and Relatedness Promoting Behaviors of Substance Using Mothers and Their Children: The Effects of a Family Systems Intervention.

PubMed

Zhang, Jing; Slesnick, Natasha

2017-03-01

Parents' and children's autonomy and relatedness behaviors are associated with a wide range of child outcomes. Yet, little is known about how parents and children's autonomy and relatedness behaviors jointly influence child outcomes. The current study captured this joint influence by exploring the longitudinal trajectory of mother-child discrepancies in autonomy and relatedness behaviors and its association with child problem behaviors. The effects of a family systems intervention on the trajectory of mother-child discrepancies were also examined. The sample included 183 substance using mothers and their children (M age = 11.54 years, SD = 2.55, range 8-16; 48 % females). Both the mother and child completed an assessment at baseline, 6- and 18-month post-baseline. A person-centered analysis identified subgroups varying in mother-child discrepancy patterns in their autonomy and relatedness behaviors. The results also showed that participation in the family systems therapy was associated with decreased mother-child discrepancies, and also a synchronous increase in mother's and child's autonomy and relatedness. Additionally, increased mother-child discrepancies and mother-child dyads showing no change in autonomy and relatedness was associated with higher levels of children's problem behaviors. The findings reveal a dynamic process of mother-child discrepancies in autonomy and relatedness behaviors related to child outcomes. The findings also support the effectiveness of the family systems therapy, and highlight the importance of understanding the complexities in family interactions when explaining children's problem behaviors.

Optics and Symbolic Computing

DTIC Science & Technology

1987-03-01

applicatior for AI are in variation of classification parameters for knowledge acquisition ( changing of classes into which objects are placed), and...computation. The well-structured data formats of vectors, matrices, etc. used in numeric computing give way to data structures that can change their shapes...by "flexible data structures. The semantic meanings of objects are readily changed by adding and deleting the variable lists of attributes. Another

Template construction grammar: from visual scene description to language comprehension and agrammatism.

PubMed

Barrès, Victor; Lee, Jinyong

2014-01-01

How does the language system coordinate with our visual system to yield flexible integration of linguistic, perceptual, and world-knowledge information when we communicate about the world we perceive? Schema theory is a computational framework that allows the simulation of perceptuo-motor coordination programs on the basis of known brain operating principles such as cooperative computation and distributed processing. We present first its application to a model of language production, SemRep/TCG, which combines a semantic representation of visual scenes (SemRep) with Template Construction Grammar (TCG) as a means to generate verbal descriptions of a scene from its associated SemRep graph. SemRep/TCG combines the neurocomputational framework of schema theory with the representational format of construction grammar in a model linking eye-tracking data to visual scene descriptions. We then offer a conceptual extension of TCG to include language comprehension and address data on the role of both world knowledge and grammatical semantics in the comprehension performances of agrammatic aphasic patients. This extension introduces a distinction between heavy and light semantics. The TCG model of language comprehension offers a computational framework to quantitatively analyze the distributed dynamics of language processes, focusing on the interactions between grammatical, world knowledge, and visual information. In particular, it reveals interesting implications for the understanding of the various patterns of comprehension performances of agrammatic aphasics measured using sentence-picture matching tasks. This new step in the life cycle of the model serves as a basis for exploring the specific challenges that neurolinguistic computational modeling poses to the neuroinformatics community.

Episodic Memory Does Not Add Up: Verbatim-Gist Superposition Predicts Violations of the Additive Law of Probability

PubMed Central

Brainerd, C. J.; Wang, Zheng; Reyna, Valerie. F.; Nakamura, K.

2015-01-01

Fuzzy-trace theory’s assumptions about memory representation are cognitive examples of the familiar superposition property of physical quantum systems. When those assumptions are implemented in a formal quantum model (QEMc), they predict that episodic memory will violate the additive law of probability: If memory is tested for a partition of an item’s possible episodic states, the individual probabilities of remembering the item as belonging to each state must sum to more than 1. We detected this phenomenon using two standard designs, item false memory and source false memory. The quantum implementation of fuzzy-trace theory also predicts that violations of the additive law will vary in strength as a function of reliance on gist memory. That prediction, too, was confirmed via a series of manipulations (e.g., semantic relatedness, testing delay) that are thought to increase gist reliance. Surprisingly, an analysis of the underlying structure of violations of the additive law revealed that as a general rule, increases in remembering correct episodic states do not produce commensurate reductions in remembering incorrect states. PMID:26236091

Impaired integration of object knowledge and visual input in a case of ventral simultanagnosia with bilateral damage to area V4.

PubMed

Leek, E Charles; d'Avossa, Giovanni; Tainturier, Marie-Josèphe; Roberts, Daniel J; Yuen, Sung Lai; Hu, Mo; Rafal, Robert

2012-01-01

This study examines how brain damage can affect the cognitive processes that support the integration of sensory input and prior knowledge during shape perception. It is based on the first detailed study of acquired ventral simultanagnosia, which was found in a patient (M.T.) with posterior occipitotemporal lesions encompassing V4 bilaterally. Despite showing normal object recognition for single items in both accuracy and response times (RTs), and intact low-level vision assessed across an extensive battery of tests, M.T. was impaired in object identification with overlapping figures displays. Task performance was modulated by familiarity: Unlike controls, M.T. was faster with overlapping displays of abstract shapes than with overlapping displays of common objects. His performance with overlapping common object displays was also influenced by both the semantic relatedness and visual similarity of the display items. These findings challenge claims that visual perception is driven solely by feedforward mechanisms and show how brain damage can selectively impair high-level perceptual processes supporting the integration of stored knowledge and visual sensory input.

Automated Inspection of Power Line Corridors to Measure Vegetation Undercut Using Uav-Based Images

NASA Astrophysics Data System (ADS)

Maurer, M.; Hofer, M.; Fraundorfer, F.; Bischof, H.

2017-08-01

Power line corridor inspection is a time consuming task that is performed mostly manually. As the development of UAVs made huge progress in recent years, and photogrammetric computer vision systems became well established, it is time to further automate inspection tasks. In this paper we present an automated processing pipeline to inspect vegetation undercuts of power line corridors. For this, the area of inspection is reconstructed, geo-referenced, semantically segmented and inter class distance measurements are calculated. The presented pipeline performs an automated selection of the proper 3D reconstruction method for on the one hand wiry (power line), and on the other hand solid objects (surrounding). The automated selection is realized by performing pixel-wise semantic segmentation of the input images using a Fully Convolutional Neural Network. Due to the geo-referenced semantic 3D reconstructions a documentation of areas where maintenance work has to be performed is inherently included in the distance measurements and can be extracted easily. We evaluate the influence of the semantic segmentation according to the 3D reconstruction and show that the automated semantic separation in wiry and dense objects of the 3D reconstruction routine improves the quality of the vegetation undercut inspection. We show the generalization of the semantic segmentation to datasets acquired using different acquisition routines and to varied seasons in time.

HyQue: evaluating hypotheses using Semantic Web technologies.

PubMed

Callahan, Alison; Dumontier, Michel; Shah, Nigam H

2011-05-17

Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks. We present HyQue, a Semantic Web tool for querying scientific knowledge bases with the purpose of evaluating user submitted hypotheses. HyQue features a knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated against experimental and literature-sourced evidence through a combination of SPARQL queries and evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. We evaluate hypotheses of varying levels of detail about the genetic network controlling galactose metabolism in Saccharomyces cerevisiae to demonstrate the feasibility of deploying such semantic computing tools over a growing body of structured knowledge in Bio2RDF. HyQue is a query-based hypothesis evaluation system that can currently evaluate hypotheses about the galactose metabolism in S. cerevisiae. Hypotheses as well as the supporting or refuting data are represented in RDF and directly linked to one another allowing scientists to browse from data to hypothesis and vice versa. HyQue hypotheses and data are available at http://semanticscience.org/projects/hyque.

Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2016-01-01

Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of…

Computational Psycholinguistic Analysis and Its Application in Psychological Assessment of College Students

ERIC Educational Resources Information Center

Kucera, Dalibor; Havigerová, Jana M.

2015-01-01

The paper deals with the issue of computational psycholinguistic analysis (CPA) and its experimental application in basic psychological and pedagogical assessment. CPA is a new method which may potentially provide interesting, psychologically relevant information about the author of a particular text, regardless of the text's factual (semantic)…

The large-scale structure of software-intensive systems

PubMed Central

Booch, Grady

2012-01-01

The computer metaphor is dominant in most discussions of neuroscience, but the semantics attached to that metaphor are often quite naive. Herein, we examine the ontology of software-intensive systems, the nature of their structure and the application of the computer metaphor to the metaphysical questions of self and causation. PMID:23386964

Global Journal of Computer Science and Technology. Volume 1.2

ERIC Educational Resources Information Center

Dixit, R. K.

2009-01-01

Articles in this issue of "Global Journal of Computer Science and Technology" include: (1) Input Data Processing Techniques in Intrusion Detection Systems--Short Review (Suhair H. Amer and John A. Hamilton, Jr.); (2) Semantic Annotation of Stock Photography for CBIR Using MPEG-7 standards (R. Balasubramani and V. Kannan); (3) An Experimental Study…

28 CFR 301.202 - Determination of work-relatedness.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Determination of work-relatedness. 301... INMATE ACCIDENT COMPENSATION Lost-Time Wages § 301.202 Determination of work-relatedness. (a) When the institution safety manager receives notice, or has reason to believe, a work-related injury may result in time...

Representations of relatedness with parents and friends and autonomous academic motivation during the late adolescence-early adulthood period: reciprocal or unidirectional effects?

PubMed

Guay, Frédéric; Marsh, Herbert W; Senécal, Caroline; Dowson, Martin

2008-12-01

The literature on the determinants of academic motivation indicates that social and affective processes connected to students' interpersonal relationships are central elements in understanding students' academic motivation and other school-related outcomes. The aim of this study was to answer the following questions: Does autonomous motivation drive representations of relatedness, do representations of relatedness drive autonomous motivation, or are these constructs reciprocally related over time? The sample consists of 834 adolescents aged 18 years (SD=1.88) who participated in a 3-year longitudinal study. Results from the structural equation models provided good support for the effect of representations of relatedness with parents on autonomous academic motivation but no convincing support for the effect of motivation on representations of relatedness with parents. In addition, no significant effect in either direction was found between representations of relatedness with friends and autonomous academic motivation. It might be important to inform parents that they may still have an influence on their adolescent's representations of relatedness and subsequently on his/her autonomous academic motivation even during the late adolescence-early adulthood period, a period when some parents may be tempted to believe that they can do little to motivate their offspring.

Work-related olfactory disorder: a case series and review.

PubMed

Park, Soon Woo; Kang, Young Joong; Eom, Huisu; Cho, Hyun-Jin; Ahn, Jungho; Lee, Sang-Gil

2018-01-01

The olfactory bulb is anatomically exposed and thus can be directly damaged by external stimulation. This can occur as an occupational injury owing to contact with organic solvents or other causes. We present cases of eight patients who sustained occupation-related exposure to potentially toxic substances and later presented with signs and symptoms of anosmia. We examined the occupational and medical characteristics of the patients and evaluated their work-relatedness. Case 1: A 50-year-old man performed high-frequency heat treatments for approximately 11 years. He experienced decreased senses for olfaction and taste during the later years culminating in the diagnosis of anosmia after 3 years (high work-relatedness). Case 2: A 54-year-old man whose work involved exposure to various organic solvents, such as spray painting and application of paint and thinners for approximately 4 years, was subsequently diagnosed with anosmia based on rhinorrhea, headache, and loss of olfaction (high work-relatedness). Case 3: A 44-year-old-man who performed spray painting for approximately 17 years developed anosmia (high work-relatedness). Case 4: A 44-year-old man was involved in ship engine cleaning once a month, for approximately 7 h per cleaning session; he was diagnosed with anosmia based on loss of olfaction (low work-relatedness). Case 5: A 41-year-old man worked in ship building block construction for approximately 13 years; anosmia diagnosis was based on loss of olfaction (low work-relatedness). Case 6: A 47-year-old woman performed product inspection and labeling at a plant manufacturing automobile parts; anosmia diagnosis was based on decreased olfaction and taste (low work-relatedness). Case 7: A 50-year-old woman performed epoxy coating in a plant manufacturing automobile parts; anosmia diagnosis was based on diminishing olfaction (low work-relatedness). Case 8: A 57-year-old woman performed cleaning of the area where mobile phone parts were manufactured; anosmia diagnosis was based on diminishing olfaction (low work-relatedness). The study results confirmed work-relatedness when the subject was young, and the duration of exposure was long without any other cause of anosmia. Regarding compensation for occupational diseases, work-relatedness can be recognized as a relative concept.

Gestural cue analysis in automated semantic miscommunication annotation

PubMed Central

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

2011-01-01

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

Synergetic computer and holonics - information dynamics of a semantic computer

NASA Astrophysics Data System (ADS)

Shimizu, H.; Yamaguchi, Y.

1987-12-01

The dynamics of semantic information in biosystem is studied based on holons, generators of mutual relations. Any biosystem has an internal world, a so-called "self", which has an intrinsic purpose rendering the system continuously alive and developed as much as possible against a fluctuating external world. External signals to the system through sensory organs are classified by the self into two basic categories, semantic information with some meaning and value for the purpose and inputs from background and noise sources. Due to this breaking of semantic symmetry, any input signals are transformed into a figure and background, respectively. As a typical example, the visual perception of vertebrates is studied. For such semantic transformation the external signal is first decomposed and converted into a number of elementary signs named "syntons" which are then transmitted into a sensory area of cortex corresponding to an image synthesizer. The synthesizer is a sort of autonomic parallel processor composed of autonomic units, "holons", which are characterized by many internal modes. Syntons are fed into the holons one by one. A set of the elementary meanings, the so-called "semons", provided to the synton are encoded in the internal modes of the holon; that is, each internal mode encodes a semon. A dynamic information theory for the transformation of external signals to semantic information is developed based on our model which we call holovision. Holovision is a dynamic model of visual perception that processes an autonomic ability to self-organize visual images. Autonomous oscillators are utilized as the line processors to encode line elements with specific orientations in their phases as semons. An information space is defined according to the assembly of holons; the spatial plane on which holons are arranged is a syntactic subspace while the internal modes of the holons span a semantic subspace in the orthogonal direction. In this information space, the image of a figure is self-organized - as a sort of spatiotemporal pattern - by autonomic coordinations of the holons that select relevant internal modes, accompanied with compression of irrelevant syntons that correspond to the background. Holons coded by a synton are relevantly connected by means of coherent relations, i.e., dynamic connections with time-coherence, in order to represent the image that varies in time depending on the instantaneous state of the external object. These connections depend on the internal modes that are cooperatively selectively selected by the holons. The image is regarded as a bridge between the external and internal world that has both external and internal consistency. The meaning of the image, i.e., transformed semantic information, is spontaneously transferred from semantic items that have a coherent relation with the image, and the external signal is perceived by the self through the image. We demonstrate that images are indeed self-organized in holovision in the previously described sense. Simulated processes of the creation of semantic information in holovision are shown to display typical features of the forgoing steps of information compression. Based on these results, we propose quantitative indices that represent the value of semantic information in the image processor as well as in the memory.

A method of extracting ontology module using concept relations for sharing knowledge in mobile cloud computing environment.

PubMed

Lee, Keonsoo; Rho, Seungmin; Lee, Seok-Won

2014-01-01

In mobile cloud computing environment, the cooperation of distributed computing objects is one of the most important requirements for providing successful cloud services. To satisfy this requirement, all the members, who are employed in the cooperation group, need to share the knowledge for mutual understanding. Even if ontology can be the right tool for this goal, there are several issues to make a right ontology. As the cost and complexity of managing knowledge increase according to the scale of the knowledge, reducing the size of ontology is one of the critical issues. In this paper, we propose a method of extracting ontology module to increase the utility of knowledge. For the given signature, this method extracts the ontology module, which is semantically self-contained to fulfill the needs of the service, by considering the syntactic structure and semantic relation of concepts. By employing this module, instead of the original ontology, the cooperation of computing objects can be performed with less computing load and complexity. In particular, when multiple external ontologies need to be combined for more complex services, this method can be used to optimize the size of shared knowledge.

A Semantic Transformation Methodology for the Secondary Use of Observational Healthcare Data in Postmarketing Safety Studies.

PubMed

Pacaci, Anil; Gonul, Suat; Sinaci, A Anil; Yuksel, Mustafa; Laleci Erturkmen, Gokce B

2018-01-01

Background: Utilization of the available observational healthcare datasets is key to complement and strengthen the postmarketing safety studies. Use of common data models (CDM) is the predominant approach in order to enable large scale systematic analyses on disparate data models and vocabularies. Current CDM transformation practices depend on proprietarily developed Extract-Transform-Load (ETL) procedures, which require knowledge both on the semantics and technical characteristics of the source datasets and target CDM. Purpose: In this study, our aim is to develop a modular but coordinated transformation approach in order to separate semantic and technical steps of transformation processes, which do not have a strict separation in traditional ETL approaches. Such an approach would discretize the operations to extract data from source electronic health record systems, alignment of the source, and target models on the semantic level and the operations to populate target common data repositories. Approach: In order to separate the activities that are required to transform heterogeneous data sources to a target CDM, we introduce a semantic transformation approach composed of three steps: (1) transformation of source datasets to Resource Description Framework (RDF) format, (2) application of semantic conversion rules to get the data as instances of ontological model of the target CDM, and (3) population of repositories, which comply with the specifications of the CDM, by processing the RDF instances from step 2. The proposed approach has been implemented on real healthcare settings where Observational Medical Outcomes Partnership (OMOP) CDM has been chosen as the common data model and a comprehensive comparative analysis between the native and transformed data has been conducted. Results: Health records of ~1 million patients have been successfully transformed to an OMOP CDM based database from the source database. Descriptive statistics obtained from the source and target databases present analogous and consistent results. Discussion and Conclusion: Our method goes beyond the traditional ETL approaches by being more declarative and rigorous. Declarative because the use of RDF based mapping rules makes each mapping more transparent and understandable to humans while retaining logic-based computability. Rigorous because the mappings would be based on computer readable semantics which are amenable to validation through logic-based inference methods.

From ontology selection and semantic web to an integrated information system for food-borne diseases and food safety.

PubMed

Yan, Xianghe; Peng, Yun; Meng, Jianghong; Ruzante, Juliana; Fratamico, Pina M; Huang, Lihan; Juneja, Vijay; Needleman, David S

2011-01-01

Several factors have hindered effective use of information and resources related to food safety due to inconsistency among semantically heterogeneous data resources, lack of knowledge on profiling of food-borne pathogens, and knowledge gaps among research communities, government risk assessors/managers, and end-users of the information. This paper discusses technical aspects in the establishment of a comprehensive food safety information system consisting of the following steps: (a) computational collection and compiling publicly available information, including published pathogen genomic, proteomic, and metabolomic data; (b) development of ontology libraries on food-borne pathogens and design automatic algorithms with formal inference and fuzzy and probabilistic reasoning to address the consistency and accuracy of distributed information resources (e.g., PulseNet, FoodNet, OutbreakNet, PubMed, NCBI, EMBL, and other online genetic databases and information); (c) integration of collected pathogen profiling data, Foodrisk.org ( http://www.foodrisk.org ), PMP, Combase, and other relevant information into a user-friendly, searchable, "homogeneous" information system available to scientists in academia, the food industry, and government agencies; and (d) development of a computational model in semantic web for greater adaptability and robustness.

Fuzzy Versions of Epistemic and Deontic Logic

NASA Technical Reports Server (NTRS)

Gounder, Ramasamy S.; Esterline, Albert C.

1998-01-01

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

Effects of Grammatical Structure of Compound Words on Word Recognition in Chinese

PubMed Central

Cui, Lei; Cong, Fengjiao; Wang, Jue; Zhang, Wenxin; Zheng, Yuwei; Hyönä, Jukka

2018-01-01

Two lexical priming experiments were conducted to examine effects of grammatical structure of Chinese two-constituent compounds on their recognition. The target compound words conformed to two types of grammatical structure: subordinate and coordinative compounds. Subordinate compounds follow a structure where the first constituent modifies the second constituent (e.g., , meaning snowball); here the meaning of the second constituent (head) is modified by the first constituent (modifier). On the other hand, in coordinative compounds both constituents contribute equally to the word meaning (e.g., , wind and rain, meaning storm where the two constituent equally contribute to the word meaning). In Experiment 1 that was a replication attempt of Liu and McBride-Chang (2010), possible priming effects of word structure and semantic relatedness were examined. In lexical decision latencies only a semantic priming effect was observed. In Experiment 2, compound word structure and individual constituents were primed by the prime and target sharing either the first or second constituent. A structure priming effect was obtained in lexical decision times for subordinate compounds when the prime and target compound shared the same constituent. This suggests that a compound word constituent (either the modifier or the head) has to be simultaneously active with the structure information in order for the structure information to exert an effect on compound word recognition in Chinese. For the coordinative compounds the structure priming effect was non-significant. When the meaning of the whole word was primed (Experiment 1), no structure effect was observable. The pattern of results suggests that effects of structure priming are constituent-specific and no general structure priming was observable. PMID:29593594

Discrepancies in Autonomy and Relatedness Promoting Behaviors of Substance Using Mothers and Their Children: The Effects of a Family Systems Intervention

PubMed Central

Slesnick, Natasha

2016-01-01

Parents’ and children’s autonomy and relatedness behaviors are associated with a wide range of child outcomes. Yet, little is known about how parents and children’s autonomy and relatedness behaviors jointly influence child outcomes. The current study captured this joint influence by exploring the longitudinal trajectory of mother–child discrepancies in autonomy and relatedness behaviors and its association with child problem behaviors. The effects of a family systems intervention on the trajectory of mother–child discrepancies were also examined. The sample included 183 substance using mothers and their children (M age = 11.54 years, SD = 2.55, range 8–16; 48 % females). Both the mother and child completed an assessment at baseline, 6- and 18-month post-baseline. A person-centered analysis identified subgroups varying in mother–child discrepancy patterns in their autonomy and relatedness behaviors. The results also showed that participation in the family systems therapy was associated with decreased mother–child discrepancies, and also a synchronous increase in mother’s and child’s autonomy and relatedness. Additionally, increased mother–child discrepancies and mother–child dyads showing no change in autonomy and relatedness was associated with higher levels of children’s problem behaviors. The findings reveal a dynamic process of mother–child discrepancies in autonomy and relatedness behaviors related to child outcomes. The findings also support the effectiveness of the family systems therapy, and highlight the importance of understanding the complexities in family interactions when explaining children’s problem behaviors. PMID:27480271

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms.

PubMed

Papež, Václav; Denaxas, Spiros; Hemingway, Harry

2017-01-01

Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.

Student Perceptions and Motivation in the Classroom: Exploring Relatedness and Value

ERIC Educational Resources Information Center

Kaufman, Annette; Dodge, Tonya

2009-01-01

According to Self-Determination Theory, feelings of relatedness and value of a behavior are critical factors that affect internalization and integration. The purpose of the current study was to identify factors that influence relatedness and value in an academic setting. Specifically, the study investigated the effects of autonomy, mastery goals,…

Social Relatedness and Physical Health Are More Strongly Related in Older Than Younger Adults: Findings from the Korean Adult Longitudinal Study

PubMed Central

Choi, Eunsoo; Kwon, Yuri; Lee, Minha; Choi, Jongan; Choi, Incheol

2018-01-01

Previous research indicates that social relatedness is beneficial to physical health; however, findings on the relative strength of the relationship between these variables have been inconsistent. The present study employed cross-sectional survey (Study 1) and a daily diary survey (Study 2) to examine the link between social relatedness and physical health by age. Using a representative sample of Korean adults (N = 371) aged from 20 to 69, Study 1 examines the link between social relatedness (loneliness, perceived social support) and physical health (physical symptoms, chronic health conditions) using age as a moderator. The results show that participants' age moderates the association between social relatedness and physical health. Study 2 (N = 384) further corroborated the findings from Study 1 by showing that when controlling for the physical symptoms experienced prior to the daily diary reports, the level of loneliness experienced over a 13-day period exacerbates the age differences in the physical symptoms. The present study thus provides converging evidence that social relatedness plays a significant role in physical health, particularly in the older population. PMID:29403415

Introspections on the Semantic Gap

DTIC Science & Technology

2015-04-14

cloud comput - ing. Zhang received an MS in computer science from Stony Brook University. Contact him at dozhang@ cs.stonybrook.edu. Donald E. Porter...designated by other documentation. ... 2 March/April 2015 Copublished by the IEEE Computer and Reliability Societies 1540-7993/15/$31.00 © 2015 IEEE IEEE S...pauses the VM, and the VMI tool introspects the process descriptor list. In contrast, an asynchronous mechanism would intro - spect memory

CityGML - Interoperable semantic 3D city models

NASA Astrophysics Data System (ADS)

Gröger, Gerhard; Plümer, Lutz

2012-07-01

CityGML is the international standard of the Open Geospatial Consortium (OGC) for the representation and exchange of 3D city models. It defines the three-dimensional geometry, topology, semantics and appearance of the most relevant topographic objects in urban or regional contexts. These definitions are provided in different, well-defined Levels-of-Detail (multiresolution model). The focus of CityGML is on the semantical aspects of 3D city models, its structures, taxonomies and aggregations, allowing users to employ virtual 3D city models for advanced analysis and visualization tasks in a variety of application domains such as urban planning, indoor/outdoor pedestrian navigation, environmental simulations, cultural heritage, or facility management. This is in contrast to purely geometrical/graphical models such as KML, VRML, or X3D, which do not provide sufficient semantics. CityGML is based on the Geography Markup Language (GML), which provides a standardized geometry model. Due to this model and its well-defined semantics and structures, CityGML facilitates interoperable data exchange in the context of geo web services and spatial data infrastructures. Since its standardization in 2008, CityGML has become used on a worldwide scale: tools from notable companies in the geospatial field provide CityGML interfaces. Many applications and projects use this standard. CityGML is also having a strong impact on science: numerous approaches use CityGML, particularly its semantics, for disaster management, emergency responses, or energy-related applications as well as for visualizations, or they contribute to CityGML, improving its consistency and validity, or use CityGML, particularly its different Levels-of-Detail, as a source or target for generalizations. This paper gives an overview of CityGML, its underlying concepts, its Levels-of-Detail, how to extend it, its applications, its likely future development, and the role it plays in scientific research. Furthermore, its relationship to other standards from the fields of computer graphics and computer-aided architectural design and to the prospective INSPIRE model are discussed, as well as the impact CityGML has and is having on the software industry, on applications of 3D city models, and on science generally.

Autonomy and Relatedness in Inner-City Families of Substance Abusing Adolescents.

PubMed

Samuolis, Jessica; Hogue, Aaron; Dauber, Sarah; Liddle, Howard A

2006-01-01

This study examined parent-adolescent autonomous-relatedness functioning in inner-city, ethnic minority families of adolescents exhibiting drug abuse and related problem behaviors. Seventy-four parent-adolescent dyads completed a structured interaction task prior to the start of treatment that was coded using an established autonomous-relatedness measure. Adolescent drug use, externalizing, and internalizing behaviors were assessed. Parents and adolescents completed assessment instruments measuring parenting style and family conflict. Confirmatory factor analysis found significant differences in the underlying dimensions of parent and adolescent autonomous-relatedness in this sample versus previous samples. It was also found that autonomous-relatedness was associated with worse adolescent symptomatology and family impairment. Results based on both self-report and observational measures contribute to the understanding of key family constructs in this population and provide insight for both researchers and the treatment community.

Autonomy and relatedness in psychopathology and treatment: a cross-cultural formulation.

PubMed

Sato, T

2001-02-01

A cross-cultural view of psychopathology is proposed, contending that there are two basic systems of self-organization. These two systems of self-organization, labeled autonomy and relatedness, are essential to a person's well-being regardless of the culture or society to which the person belongs. The degree of autonomy and relatedness required to maintain mental health in a specific society is affected by cultural mores. People in collectivistic (primarily non-Western) cultures require high levels of relatedness and moderate levels of autonomy to maintain mental health. People in individualistic (primarily Western) cultures require high levels of autonomy and moderate levels of relatedness to maintain mental health. This view, based on a review of past work in various areas of psychology, is discussed in the context of various forms of psychotherapy existing in individualistic and collectivistic cultures.

Autonomy and Relatedness in Inner-City Families of Substance Abusing Adolescents

PubMed Central

Samuolis, Jessica; Hogue, Aaron; Dauber, Sarah; Liddle, Howard A.

2010-01-01

This study examined parent-adolescent autonomous-relatedness functioning in inner-city, ethnic minority families of adolescents exhibiting drug abuse and related problem behaviors. Seventy-four parent-adolescent dyads completed a structured interaction task prior to the start of treatment that was coded using an established autonomous-relatedness measure. Adolescent drug use, externalizing, and internalizing behaviors were assessed. Parents and adolescents completed assessment instruments measuring parenting style and family conflict. Confirmatory factor analysis found significant differences in the underlying dimensions of parent and adolescent autonomous-relatedness in this sample versus previous samples. It was also found that autonomous-relatedness was associated with worse adolescent symptomatology and family impairment. Results based on both self-report and observational measures contribute to the understanding of key family constructs in this population and provide insight for both researchers and the treatment community. PMID:20376203

Using the Unified Modelling Language (UML) to guide the systemic description of biological processes and systems.

PubMed

Roux-Rouquié, Magali; Caritey, Nicolas; Gaubert, Laurent; Rosenthal-Sabroux, Camille

2004-07-01

One of the main issues in Systems Biology is to deal with semantic data integration. Previously, we examined the requirements for a reference conceptual model to guide semantic integration based on the systemic principles. In the present paper, we examine the usefulness of the Unified Modelling Language (UML) to describe and specify biological systems and processes. This makes unambiguous representations of biological systems, which would be suitable for translation into mathematical and computational formalisms, enabling analysis, simulation and prediction of these systems behaviours.

Programming with process groups: Group and multicast semantics

NASA Technical Reports Server (NTRS)

Birman, Kenneth P.; Cooper, Robert; Gleeson, Barry

1991-01-01

Process groups are a natural tool for distributed programming and are increasingly important in distributed computing environments. Discussed here is a new architecture that arose from an effort to simplify Isis process group semantics. The findings include a refined notion of how the clients of a group should be treated, what the properties of a multicast primitive should be when systems contain large numbers of overlapping groups, and a new construct called the causality domain. A system based on this architecture is now being implemented in collaboration with the Chorus and Mach projects.

On the impact of relatedness on SNP association analysis.

PubMed

Gross, Arnd; Tönjes, Anke; Scholz, Markus

2017-12-06

When testing for SNP (single nucleotide polymorphism) associations in related individuals, observations are not independent. Simple linear regression assuming independent normally distributed residuals results in an increased type I error and the power of the test is also affected in a more complicate manner. Inflation of type I error is often successfully corrected by genomic control. However, this reduces the power of the test when relatedness is of concern. In the present paper, we derive explicit formulae to investigate how heritability and strength of relatedness contribute to variance inflation of the effect estimate of the linear model. Further, we study the consequences of variance inflation on hypothesis testing and compare the results with those of genomic control correction. We apply the developed theory to the publicly available HapMap trio data (N=129), the Sorbs (a self-contained population with N=977 characterised by a cryptic relatedness structure) and synthetic family studies with different sample sizes (ranging from N=129 to N=999) and different degrees of relatedness. We derive explicit and easily to apply approximation formulae to estimate the impact of relatedness on the variance of the effect estimate of the linear regression model. Variance inflation increases with increasing heritability. Relatedness structure also impacts the degree of variance inflation as shown for example family structures. Variance inflation is smallest for HapMap trios, followed by a synthetic family study corresponding to the trio data but with larger sample size than HapMap. Next strongest inflation is observed for the Sorbs, and finally, for a synthetic family study with a more extreme relatedness structure but with similar sample size as the Sorbs. Type I error increases rapidly with increasing inflation. However, for smaller significance levels, power increases with increasing inflation while the opposite holds for larger significance levels. When genomic control is applied, type I error is preserved while power decreases rapidly with increasing variance inflation. Stronger relatedness as well as higher heritability result in increased variance of the effect estimate of simple linear regression analysis. While type I error rates are generally inflated, the behaviour of power is more complex since power can be increased or reduced in dependence on relatedness and the heritability of the phenotype. Genomic control cannot be recommended to deal with inflation due to relatedness. Although it preserves type I error, the loss in power can be considerable. We provide a simple formula for estimating variance inflation given the relatedness structure and the heritability of a trait of interest. As a rule of thumb, variance inflation below 1.05 does not require correction and simple linear regression analysis is still appropriate.