semantic set size: Topics by Science.gov

Sample records for semantic set size

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

PubMed

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

2012-04-01

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

PubMed

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

2004-01-01

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

PubMed

Wennerberg, Pinar; Schulz, Klaus; Buitelaar, Paul

2011-02-01

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

PubMed

Perea, Manuel; Rosa, Eva

2002-08-01

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

PubMed

Yao, Bo; Vasiljevic, Milica; Weick, Mario; Sereno, Margaret E; O'Donnell, Patrick J; Sereno, Sara C

2013-01-01

Size is an important visuo-spatial characteristic of the physical world. In language processing, previous research has demonstrated a processing advantage for words denoting semantically "big" (e.g., jungle) versus "small" (e.g., needle) concrete objects. We investigated whether semantic size plays a role in the recognition of words expressing abstract concepts (e.g., truth). Semantically "big" and "small" concrete and abstract words were presented in a lexical decision task. Responses to "big" words, regardless of their concreteness, were faster than those to "small" words. Critically, we explored the relationship between semantic size and affective characteristics of words as well as their influence on lexical access. Although a word's semantic size was correlated with its emotional arousal, the temporal locus of arousal effects may depend on the level of concreteness. That is, arousal seemed to have an earlier (lexical) effect on abstract words, but a later (post-lexical) effect on concrete words. Our findings provide novel insights into the semantic representations of size in abstract concepts and highlight that affective attributes of words may not always index lexical access.
The role of semantically related distractors during encoding and retrieval of words in long-term memory.

PubMed

Meade, Melissa E; Fernandes, Myra A

2016-07-01

We examined the influence of divided attention (DA) on recognition of words when the concurrent task was semantically related or unrelated to the to-be-recognised target words. Participants were asked to either study or retrieve a target list of semantically related words while simultaneously making semantic decisions (i.e., size judgements) to another set of related or unrelated words heard concurrently. We manipulated semantic relatedness of distractor to target words, and whether DA occurred during the encoding or retrieval phase of memory. Recognition accuracy was significantly diminished relative to full attention, following DA conditions at encoding, regardless of relatedness of distractors to study words. However, response times (RTs) were slower with related compared to unrelated distractors. Similarly, under DA at retrieval, recognition RTs were slower when distractors were semantically related than unrelated to target words. Unlike the effect from DA at encoding, recognition accuracy was worse under DA at retrieval when the distractors were related compared to unrelated to the target words. Results suggest that availability of general attentional resources is critical for successful encoding, whereas successful retrieval is particularly reliant on access to a semantic code, making it sensitive to related distractors under DA conditions.
Semantic Size of Abstract Concepts: It Gets Emotional When You Can’t See It

PubMed Central

Yao, Bo; Vasiljevic, Milica; Weick, Mario; Sereno, Margaret E.; O’Donnell, Patrick J.; Sereno, Sara C.

2013-01-01

Size is an important visuo-spatial characteristic of the physical world. In language processing, previous research has demonstrated a processing advantage for words denoting semantically “big” (e.g., jungle) versus “small” (e.g., needle) concrete objects. We investigated whether semantic size plays a role in the recognition of words expressing abstract concepts (e.g., truth). Semantically “big” and “small” concrete and abstract words were presented in a lexical decision task. Responses to “big” words, regardless of their concreteness, were faster than those to “small” words. Critically, we explored the relationship between semantic size and affective characteristics of words as well as their influence on lexical access. Although a word’s semantic size was correlated with its emotional arousal, the temporal locus of arousal effects may depend on the level of concreteness. That is, arousal seemed to have an earlier (lexical) effect on abstract words, but a later (post-lexical) effect on concrete words. Our findings provide novel insights into the semantic representations of size in abstract concepts and highlight that affective attributes of words may not always index lexical access. PMID:24086421
Joint classification and contour extraction of large 3D point clouds

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

PubMed Central

Adams, Sarah C.; Kiefer, Markus

2012-01-01

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

PubMed

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

2012-07-01

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

NASA Astrophysics Data System (ADS)

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

2012-02-01

Existing literature describes a variety of techniques for semantic annotation of DICOM CT images, i.e. the automatic detection and localization of anatomical structures. Semantic annotation facilitates enhanced image navigation, linkage of DICOM image content and non-image clinical data, content-based image retrieval, and image registration. A key challenge for semantic annotation algorithms is inter-patient variability. However, while the algorithms described in published literature have been shown to cope adequately with the variability in test sets comprising adult CT scans, the problem presented by the even greater variability in pediatric anatomy has received very little attention. Most existing semantic annotation algorithms can only be extended to work on scans of both adult and pediatric patients by adapting parameters heuristically in light of patient size. In contrast, our approach, which uses random regression forests ('RRF'), learns an implicit model of scale variation automatically using training data. In consequence, anatomical structures can be localized accurately in both adult and pediatric CT studies without the need for parameter adaptation or additional information about patient scale. We show how the RRF algorithm is able to learn scale invariance from a combined training set containing a mixture of pediatric and adult scans. Resulting localization accuracy for both adult and pediatric data remains comparable with that obtained using RRFs trained and tested using only adult data.
Semantic size does not matter: "bigger" words are not recognized faster.

PubMed

Kang, Sean H K; Yap, Melvin J; Tse, Chi-Shing; Kurby, Christopher A

2011-06-01

Sereno, O'Donnell, and Sereno (2009) reported that words are recognized faster in a lexical decision task when their referents are physically large than when they are small, suggesting that "semantic size" might be an important variable that should be considered in visual word recognition research and modelling. We sought to replicate their size effect, but failed to find a significant latency advantage in lexical decision for "big" words (cf. "small" words), even though we used the same word stimuli as Sereno et al. and had almost three times as many subjects. We also examined existing data from visual word recognition megastudies (e.g., English Lexicon Project) and found that semantic size is not a significant predictor of lexical decision performance after controlling for the standard lexical variables. In summary, the null results from our lab experiment--despite a much larger subject sample size than Sereno et al.--converged with our analysis of megastudy lexical decision performance, leading us to conclude that semantic size does not matter for word recognition. Discussion focuses on why semantic size (unlike some other semantic variables) is unlikely to play a role in lexical decision.
When does word meaning affect immediate serial recall in semantic dementia?

PubMed

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

2004-03-01

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

PubMed

Reilly, Megan; Desai, Rutvik H

2017-12-01

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

PubMed Central

Eddy, Marianna D.; Holcomb, Phillip J.

2009-01-01

This experiment examined invariance in object representations through measuring event-related potentials (ERPs) to pictures in a masked repetition priming paradigm. Pairs of pictures were presented where the prime was either the same size or half the size of the target object and the target was either presented in a normal orientation or was a normal sized mirror reflection of the prime object. Previous masked repetition priming studies have found a cascade of priming effect sensitive to perceptual (N190/P190) and semantic (N400) properties of the stimulus. This experiment found that both early (N190/P190 effects) and later effects (N400) were invariant to size, whereas only the N190/P190 effect was invariant to mirror reflection. The combination of a small prime and a mirror reflected target led to no significant priming effects. Taken together, the results of this set of experiments suggests that object recognition, more specifically, activating an object representation, occurs in a hierarchical fashion where overlapping perceptual information between the prime and target is necessary, although not always sufficient, to activate a higher level semantic representation. PMID:19560248
DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Rui; Praggastis, Brenda L.; Smith, William P.

While streaming data have become increasingly more popular in business and research communities, semantic models and processing software for streaming data have not kept pace. Traditional semantic solutions have not addressed transient data streams. Semantic web languages (e.g., RDF, OWL) have typically addressed static data settings and linked data approaches have predominantly addressed static or growing data repositories. Streaming data settings have some fundamental differences; in particular, data are consumed on the fly and data may expire. Stream reasoning, a combination of stream processing and semantic reasoning, has emerged with the vision of providing "smart" processing of streaming data. C-SPARQLmore » is a prominent stream reasoning system that handles semantic (RDF) data streams. Many stream reasoning systems including C-SPARQL use a sliding window and use data arrival time to evict data. For data streams that include expiration times, a simple arrival time scheme is inadequate if the window size does not match the expiration period. In this paper, we propose a cache-enabled, order-aware, ontology-based stream reasoning framework. This framework consumes RDF streams with expiration timestamps assigned by the streaming source. Our framework utilizes both arrival and expiration timestamps in its cache eviction policies. In addition, we introduce the notion of "semantic importance" which aims to address the relevance of data to the expected reasoning, thus enabling the eviction algorithms to be more context- and reasoning-aware when choosing what data to maintain for question answering. We evaluate this framework by implementing three different prototypes and utilizing five metrics. The trade-offs of deploying the proposed framework are also discussed.« less
tESA: a distributional measure for calculating semantic relatedness.

PubMed

Rybinski, Maciej; Aldana-Montes, José Francisco

2016-12-28

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

PubMed

Liu, Baolin; Meng, Xianyao; Wang, Zhongning; Wu, Guangning

2011-11-14

In the present study, we used event-related potentials (ERPs) to examine whether semantic integration occurs for ecologically unrelated audio-visual information. Videos with synchronous audio-visual information were used as stimuli, where the auditory stimuli were sine wave sounds with different sound levels, and the visual stimuli were simple geometric figures with different areas. In the experiment, participants were shown an initial display containing a single shape (drawn from a set of 6 shapes) with a fixed size (14cm(2)) simultaneously with a 3500Hz tone of a fixed intensity (80dB). Following a short delay, another shape/tone pair was presented and the relationship between the size of the shape and the intensity of the tone varied across trials: in the V+A- condition, a large shape was paired with a soft tone; in the V+A+ condition, a large shape was paired with a loud tone, and so forth. The ERPs results revealed that N400 effect was elicited under the VA- condition (V+A- and V-A+) as compared to the VA+ condition (V+A+ and V-A-). It was shown that semantic integration would occur when simultaneous, ecologically unrelated auditory and visual stimuli enter the human brain. We considered that this semantic integration was based on semantic constraint of audio-visual information, which might come from the long-term learned association stored in the human brain and short-term experience of incoming information. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Jumping across biomedical contexts using compressive data fusion

PubMed Central

Zitnik, Marinka; Zupan, Blaz

2016-01-01

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

ERP index of the morphological family size effect during word recognition.

PubMed

Kwon, Youan; Nam, Kichun; Lee, Yoonhyoung

2012-12-01

The purpose of this study was to examine whether the N400 is affected by the semantic richness of associated neighboring word members or by the density of the orthographic syllable neighborhood. Another purpose of this study was to investigate the source of the different LPC in respect to the semantic richness. To do so, the density of the syllable neighborhood and the size of the morphological family of a word were orthogonally manipulated. ERPs from 24 participants were collected during a go/no-go semantic categorization task. The results showed that the N400 effect was mainly influenced by the density of the syllable neighborhood rather than by the morphological family size. The results also showed that words with a larger morphological family size generate significantly larger LPC than words with a smaller morphological family size. The present study did not support the assumption that the main source of the N400 effect is the semantic richness of the associated neighbors. The present results suggest that the N400 is more sensitive to the density of the syllable neighborhood and LPC is sensitive to the density of the semantic neighborhood reflected by the morphological family size. Copyright © 2012 Elsevier Ltd. All rights reserved.
Causal Evidence for a Mechanism of Semantic Integration in the Angular Gyrus as Revealed by High-Definition Transcranial Direct Current Stimulation

PubMed Central

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

2016-01-01

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

PubMed

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

2016-03-30

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

ERIC Educational Resources Information Center

Erten, Ismail Hakki; Tekin, Mustafa

2008-01-01

This paper reports on a study which investigated the effect on vocabulary recall of introducing new words via two different methods. A one-group quasi-experimental research design with alternating time series measures was employed. A group of 60 fourth graders were taught 80 carefully selected words either in semantically related sets or…
Analyzing structural changes in SNOMED CT's Bacterial infectious diseases using a visual semantic delta.

PubMed

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

2017-03-01

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

PubMed Central

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

2017-01-01

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

PubMed Central

Grego, Tiago; Couto, Francisco M.

2013-01-01

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

PubMed

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

2012-07-01

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

NASA Astrophysics Data System (ADS)

Hassanpour, Saeed; O'Connor, Martin J.; Das, Amar K.

Rule bases are increasingly being used as repositories of knowledge content on the Semantic Web. As the size and complexity of these rule bases increases, developers and end users need methods of rule abstraction to facilitate rule management. In this paper, we describe a rule abstraction method for Semantic Web Rule Language (SWRL) rules that is based on lexical analysis and a set of heuristics. Our method results in a tree data structure that we exploit in creating techniques to visualize, paraphrase, and categorize SWRL rules. We evaluate our approach by applying it to several biomedical ontologies that contain SWRL rules, and show how the results reveal rule patterns within the rule base. We have implemented our method as a plug-in tool for Protégé-OWL, the most widely used ontology modeling software for the Semantic Web. Our tool can allow users to rapidly explore content and patterns in SWRL rule bases, enabling their acquisition and management.
Mining Hierarchies and Similarity Clusters from Value Set Repositories.

PubMed

Peterson, Kevin J; Jiang, Guoqian; Brue, Scott M; Shen, Feichen; Liu, Hongfang

2017-01-01

A value set is a collection of permissible values used to describe a specific conceptual domain for a given purpose. By helping to establish a shared semantic understanding across use cases, these artifacts are important enablers of interoperability and data standardization. As the size of repositories cataloging these value sets expand, knowledge management challenges become more pronounced. Specifically, discovering value sets applicable to a given use case may be challenging in a large repository. In this study, we describe methods to extract implicit relationships between value sets, and utilize these relationships to overlay organizational structure onto value set repositories. We successfully extract two different structurings, hierarchy and clustering, and show how tooling can leverage these structures to enable more effective value set discovery.
Semantic memory: a feature-based analysis and new norms for Italian.

PubMed

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

2013-06-01

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

ERIC Educational Resources Information Center

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

2013-01-01

Semantic Web technologies have been applied in educational settings for different purposes in recent years, with the type of application being mainly defined by the way in which knowledge is represented and exploited. The basic technology for knowledge representation in Semantic Web settings is the ontology, which represents a common, shareable…
ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

PubMed

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

2017-01-01

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

PubMed

Kelley, Matthew R; Parihar, Sushmeena A

2018-01-19

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

PubMed Central

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

2009-01-01

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

PubMed

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

2003-12-01

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

ERIC Educational Resources Information Center

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

2017-01-01

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

PubMed Central

Solbrig, Harold R; Chute, Christopher G

2012-01-01

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

PubMed Central

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

2011-01-01

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

Weaknesses in Lexical-Semantic Knowledge Among College Students With Specific Learning Disabilities: Evidence From a Semantic Fluency Task

PubMed Central

McGregor, Karla K.; Oleson, Jacob

2017-01-01

Purpose The purpose of this study is to determine whether deficits in executive function and lexical-semantic memory compromise the linguistic performance of young adults with specific learning disabilities (LD) enrolled in postsecondary studies. Method One hundred eighty-five students with LD (n = 53) or normal language development (ND, n = 132) named items in the categories animals and food for 1 minute for each category and completed tests of lexical-semantic knowledge and executive control of memory. Groups were compared on total names, mean cluster size, frequency of embedded clusters, frequency of cluster switches, and change in fluency over time. Secondary analyses of variability within the LD group were also conducted. Results The LD group was less fluent than the ND group. Within the LD group, lexical-semantic knowledge predicted semantic fluency and cluster size; executive control of memory predicted semantic fluency and cluster switches. The LD group produced smaller clusters and fewer embedded clusters than the ND group. Groups did not differ in switching or change over time. Conclusions Deficits in the lexical-semantic system associated with LD may persist into young adulthood, even among those who have managed their disability well enough to attend college. Lexical-semantic deficits are associated with compromised semantic fluency, and the two problems are more likely among students with more severe disabilities. PMID:28267833
Weaknesses in Lexical-Semantic Knowledge Among College Students With Specific Learning Disabilities: Evidence From a Semantic Fluency Task.

PubMed

Hall, Jessica; McGregor, Karla K; Oleson, Jacob

2017-03-01

The purpose of this study is to determine whether deficits in executive function and lexical-semantic memory compromise the linguistic performance of young adults with specific learning disabilities (LD) enrolled in postsecondary studies. One hundred eighty-five students with LD (n = 53) or normal language development (ND, n = 132) named items in the categories animals and food for 1 minute for each category and completed tests of lexical-semantic knowledge and executive control of memory. Groups were compared on total names, mean cluster size, frequency of embedded clusters, frequency of cluster switches, and change in fluency over time. Secondary analyses of variability within the LD group were also conducted. The LD group was less fluent than the ND group. Within the LD group, lexical-semantic knowledge predicted semantic fluency and cluster size; executive control of memory predicted semantic fluency and cluster switches. The LD group produced smaller clusters and fewer embedded clusters than the ND group. Groups did not differ in switching or change over time. Deficits in the lexical-semantic system associated with LD may persist into young adulthood, even among those who have managed their disability well enough to attend college. Lexical-semantic deficits are associated with compromised semantic fluency, and the two problems are more likely among students with more severe disabilities.
Semantic distance as a critical factor in icon design for in-car infotainment systems.

PubMed

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

2017-11-01

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

PubMed Central

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

2018-01-01

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

PubMed

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

2017-11-01

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

PubMed

Mura, T; Amieva, H; Goldberg, M; Dartigues, J-F; Ankri, J; Zins, M; Berr, C

2016-11-01

The aim of our study was to examine the effect sizes of different cognitive function determinants in middle and early old age. Cognitive functions were assessed in 11 711 volunteers (45 to 75 years old), included in the French CONSTANCES cohort between January 2012 and May 2014, using the free and cued selective reminding test (FCSRT), verbal fluency tasks, digit-symbol substitution test (DSST) and trail making test (TMT), parts A and B. The effect sizes of socio-demographic (age, sex, education), lifestyle (alcohol, tobacco, physical activity), cardiovascular (diabetes, blood pressure) and psychological (depressive symptomatology) variables were computed as omega-squared coefficients (ω 2 ; part of the variation of a neuropsychological score that is independently explained by a given variable). These sets of variables explained from R 2 = 10% (semantic fluency) to R 2 = 26% (DSST) of the total variance. In all tests, socio-demographic variables accounted for the greatest part of the explained variance. Age explained from ω 2 = 0.5% (semantic fluency) to ω 2 = 7.5% (DSST) of the total score variance, gender from ω 2 = 5.2% (FCSRT) to a negligible part (semantic fluency or TMT) and education from ω 2 = 7.2% (DSST) to ω 2 = 1.4% (TMT-A). Behavioral, cardiovascular and psychological variables only slightly influenced the cognitive test results (all ω 2 < 0.8%, most ω 2 < 0.1%). Socio-demographic variables (age, gender and education) are the main variables associated with cognitive performance variations between 45 and 75 years of age in the general population. © 2016 EAN.
Semantic Size and Contextual Congruency Effects during Reading: Evidence from Eye Movements

ERIC Educational Resources Information Center

Wei, Wei; Cook, Anne E.

2016-01-01

Recent lexical decision studies have produced conflicting evidence about whether an object's semantic size influences word recognition. The present study examined this variable in online reading. Target words representing small and large objects were embedded in sentence contexts that were either neutral, congruent, or incongruent with respect to…
Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.
Effective Tooling for Linked Data Publishing in Scientific Research

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

Purohit, Sumit; Smith, William P.; Chappell, Alan R.

Challenges that make it difficult to find, share, and combine published data, such as data heterogeneity and resource discovery, have led to increased adoption of semantic data standards and data publishing technologies. To make data more accessible, interconnected and discoverable, some domains are being encouraged to publish their data as Linked Data. Consequently, this trend greatly increases the amount of data that semantic web tools are required to process, store, and interconnect. In attempting to process and manipulate large data sets, tools–ranging from simple text editors to modern triplestores– eventually breakdown upon reaching undefined thresholds. This paper offers a systematicmore » approach that data publishers can use to categorize suitable tools to meet their data publishing needs. We present a real-world use case, the Resource Discovery for Extreme Scale Collaboration (RDESC), which features a scientific dataset(maximum size of 1.4 billion triples) used to evaluate a toolbox for data publishing in climate research. This paper also introduces a semantic data publishing software suite developed for the RDESC project.« less
The influence of speech rate and accent on access and use of semantic information.

PubMed

Sajin, Stanislav M; Connine, Cynthia M

2017-04-01

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

PubMed

Bermeitinger, Christina; Wentura, Dirk; Frings, Christian

2011-06-01

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

ERIC Educational Resources Information Center

Forster, Kenneth I.

2004-01-01

Previous work indicates that semantic categorization decisions for nonexemplars (e.g., deciding that TURBAN is not an animal name) are faster for high-frequency words than low-frequency words. However, there is evidence that this result might depend on category size. When narrow categories are used (e.g., Months, Numbers), there is no frequency…
Improving EFL Writing through Study of Semantic Concepts in Formulaic Language

ERIC Educational Resources Information Center

Schenck, Andrew D.; Choi, Wonkyung

2015-01-01

Within Asian EFL contexts such as South Korea, large class sizes, poor sources of input and an overreliance on the Grammar-Translation Method may negatively impact semantic and pragmatic development of writing content. Since formulaic language is imbued with syntactic, semantic and pragmatic linguistic features, it represents an ideal means to…
On the Antecedents of an Electrophysiological Signature of Retrieval Mode.

PubMed

Williams, Angharad N; Evans, Lisa H; Herron, Jane E; Wilding, Edward L

2016-01-01

It has been proposed that people employ a common set of sustained operations (retrieval mode) when preparing to remember different kinds of episodic information. In two experiments, however, there was no evidence for the pattern of brain activity commonly assumed to index these operations. In both experiments event-related potentials (ERPs) were recorded time-locked to alternating preparatory cues signalling that participants should prepare for different retrieval tasks. One cue signalled episodic retrieval: remember the location where the object was presented in a prior study phase. The other signalled semantic retrieval: identify the location where the object is most commonly found (Experiment 1) or identify the typical size of the object (Experiment 2). In both experiments, only two trials of the same task were completed in succession. This enabled ERP contrasts between 'repeat' trials (the cue on the preceding trial signalled the same retrieval task), and 'switch' trials (the cue differed from the preceding trial). There were differences between the ERPs elicited by the preparatory task cues in Experiment 1 only: these were evident only on switch trials and comprised more positive-going activity over right-frontal scalp for the semantic than for the episodic task. These findings diverge from previous outcomes where the activity differentiating cues signalling preparation for episodic or semantic retrieval has been restricted to right-frontal scalp sites, comprising more positive-going activity for the episodic than for the semantic task. While these findings are consistent with the view that there is not a common set of operations engaged when people prepare to remember different kinds of episodic information, an alternative account is offered here, which is that these outcomes are a consequence of structural and temporal components of the experiment designs.
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).
Rapid Parallel Semantic Processing of Numbers without Awareness

ERIC Educational Resources Information Center

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

2011-01-01

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

PubMed

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

2015-05-01

To improve semantic interoperability of electronic health records (EHRs) by ontology-based mediation across syntactically heterogeneous representations of the same or similar clinical information. Our approach is based on a semantic layer that consists of: (1) a set of ontologies supported by (2) a set of semantic patterns. The first aspect of the semantic layer helps standardize the clinical information modeling task and the second shields modelers from the complexity of ontology modeling. We applied this approach to heterogeneous representations of an excerpt of a heart failure summary. Using a set of finite top-level patterns to derive semantic patterns, we demonstrate that those patterns, or compositions thereof, can be used to represent information from clinical models. Homogeneous querying of the same or similar information, when represented according to heterogeneous clinical models, is feasible. Our approach focuses on the meaning embedded in EHRs, regardless of their structure. This complex task requires a clear ontological commitment (ie, agreement to consistently use the shared vocabulary within some context), together with formalization rules. These requirements are supported by semantic patterns. Other potential uses of this approach, such as clinical models validation, require further investigation. We show how an ontology-based representation of a clinical summary, guided by semantic patterns, allows homogeneous querying of heterogeneous information structures. Whether there are a finite number of top-level patterns is an open question. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Mechanisms of masked evaluative priming: task sets modulate behavioral and electrophysiological priming for picture and words differentially

PubMed Central

Liegel, Nathalie; Zovko, Monika; Wentura, Dirk

2017-01-01

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

NASA Astrophysics Data System (ADS)

Nešić, Saša

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

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

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

DTIC Science & Technology

2013-06-01

Control Research & Technology Symposium (ICCRTS) held 19-21 June, 2013 in Alexandria, VA. 14. ABSTRACT The challenges of effectively managing semantic...technologies over disadvantaged or degraded environments are numerous and complex. One of the greatest challenges is the size of raw data. Large...approach mitigates this challenge by performing data reduction through the adoption of format recognition technologies, semantic data extractions, and the
Multivariate Pattern Analysis Reveals Category-Related Organization of Semantic Representations in Anterior Temporal Cortex.

PubMed

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

2016-09-28

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

ERIC Educational Resources Information Center

Maton, Karl

2013-01-01

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

PubMed

Strobelt, Hendrik; Oelke, Daniela; Rohrdantz, Christian; Stoffel, Andreas; Keim, Daniel A; Deussen, Oliver

2009-01-01

Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images.
Mechanisms of masked evaluative priming: task sets modulate behavioral and electrophysiological priming for picture and words differentially.

PubMed

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

2017-04-01

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

PubMed Central

Gill, Emma

2017-01-01

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

ERIC Educational Resources Information Center

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

2012-01-01

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

PubMed

Taha, Kamal

2013-05-01

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

PubMed

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

2015-12-01

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

PubMed Central

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

2016-01-01

Children with autism spectrum disorder (ASD) and specific language impairment (SLI) often have immature lexical-semantic knowledge; however, the organization of lexical-semantic knowledge is poorly understood. This study examined lexical processing in school-age children with ASD, SLI, and typical development, who were matched on receptive vocabulary. Children completed a lexical decision task, involving words with high and low semantic network sizes and nonwords. Children also completed nonverbal updating and shifting tasks. Children responded more accurately to words from high than from low semantic networks; however, follow-up analyses identified weaker semantic network effects in the SLI group. Additionally, updating and shifting abilities predicted lexical processing, demonstrating similarity in the mechanisms which underlie semantic processing in children with ASD, SLI, and typical development. PMID:26210517
Type-specific proactive interference in patients with semantic and phonological STM deficits.

PubMed

Harris, Lara; Olson, Andrew; Humphreys, Glyn

2014-01-01

Prior neuropsychological evidence suggests that semantic and phonological components of short-term memory (STM) are functionally and neurologically distinct. The current paper examines proactive interference (PI) from semantic and phonological information in two STM-impaired patients, DS (semantic STM deficit) and AK (phonological STM deficit). In Experiment 1 probe recognition tasks with open and closed sets of stimuli were used. Phonological PI was assessed using nonword items, and semantic and phonological PI was assessed using words. In Experiment 2 phonological and semantic PI was elicited by an item recognition probe test with stimuli that bore phonological and semantic relations to the probes. The data suggested heightened phonological PI for the semantic STM patient, and exaggerated effects of semantic PI in the phonological STM case. The findings are consistent with an account of extremely rapid decay of activated type-specific representations in cases of severely impaired phonological and semantic STM.
Facilitation and Interference in Identification of Pictures and Words

DTIC Science & Technology

1994-10-05

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

ERIC Educational Resources Information Center

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

2008-01-01

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

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

ERIC Educational Resources Information Center

Onishi, Masayuki

1997-01-01

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

ERIC Educational Resources Information Center

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

2005-01-01

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

PubMed

Wang, Changji; Li, Yuan; Xia, Xiaonan; Zheng, Kangjia

2014-01-01

Revocation functionality is necessary and crucial to identity-based cryptosystems. Revocable identity-based encryption (RIBE) has attracted a lot of attention in recent years, many RIBE schemes have been proposed in the literature but shown to be either insecure or inefficient. In this paper, we propose a new scalable RIBE scheme with decryption key exposure resilience by combining Lewko and Waters' identity-based encryption scheme and complete subtree method, and prove our RIBE scheme to be semantically secure using dual system encryption methodology. Compared to existing scalable and semantically secure RIBE schemes, our proposed RIBE scheme is more efficient in term of ciphertext size, public parameters size and decryption cost at price of a little looser security reduction. To the best of our knowledge, this is the first construction of scalable and semantically secure RIBE scheme with constant size public system parameters.
Modelling Metamorphism by Abstract Interpretation

NASA Astrophysics Data System (ADS)

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

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

Using RDF to Model the Structure and Process of Systems

NASA Astrophysics Data System (ADS)

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

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

PubMed

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

2011-01-01

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

NASA Astrophysics Data System (ADS)

Li, Qian; Balagurunathan, Yoganand; Liu, Ying; Schabath, Matthew; Gillies, Robert J.

2016-03-01

Background: Lung-RADS is the new oncology classification guideline proposed by American College of Radiology (ACR), which provides recommendation for further follow up in lung cancer screening. However, only two features (solidity and size) are included in this system. We hypothesize that additional sematic features can be used to better characterize lung nodules and diagnose cancer. Objective: We propose to develop and characterize a systematic methodology based on semantic image traits to more accurately predict occurrence of cancerous nodules. Methods: 24 radiological image traits were systematically scored on a point scale (up to 5) by a trained radiologist, and lung-RADS was independently scored. A linear discriminant model was used on the semantic features to access their performance in predicting cancer status. The semantic predictors were then compared to lung-RADS classification in 199 patients (60 cancers, 139 normal controls) obtained from the National Lung Screening Trial. Result: There were different combinations of semantic features that were strong predictors of cancer status. Of these, contour, border definition, size, solidity, focal emphysema, focal fibrosis and location emerged as top candidates. The performance of two semantic features (short axial diameter and contour) had an AUC of 0.945, and was comparable to that of lung-RADS (AUC: 0.871). Conclusion: We propose that a semantics-based discrimination approach may act as a complement to the lung-RADS to predict cancer status.
Workspaces in the Semantic Web

NASA Technical Reports Server (NTRS)

Wolfe, Shawn R.; Keller, RIchard M.

2005-01-01

Due to the recency and relatively limited adoption of Semantic Web technologies. practical issues related to technology scaling have received less attention than foundational issues. Nonetheless, these issues must be addressed if the Semantic Web is to realize its full potential. In particular, we concentrate on the lack of scoping methods that reduce the size of semantic information spaces so they are more efficient to work with and more relevant to an agent's needs. We provide some intuition to motivate the need for such reduced information spaces, called workspaces, give a formal definition, and suggest possible methods of deriving them.
An Attribute-Treatment Interaction Study: Lexical-Set versus Semantically-Unrelated Vocabulary Instruction

ERIC Educational Resources Information Center

Hashemi, Mohammad Reza; Gowdasiaei, Farah

2005-01-01

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

ERIC Educational Resources Information Center

Davidson, Kathryn; Eng, Kortney; Barner, David

2012-01-01

We tested the hypothesis that, when children learn to correctly count sets, they make a semantic induction about the meanings of their number words. We tested the logical understanding of number words in 84 children that were classified as "cardinal-principle knowers" by the criteria set forth by Wynn (1992). Results show that these children often…
Teaching Vocabulary to Turkish Young Learners in Semantically Related and Semantically Unrelated Sets by Using Digital Storytelling

ERIC Educational Resources Information Center

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

2016-01-01

Teaching vocabulary is a comprehensive process in foreign language learning requiring specific techniques of appropriate instruction and accurate strategy. The present study was conducted to examine the effects of teaching vocabulary to Turkish young learners in a semantic clustering way through digital storytelling. To investigate this aim, six…
Adapting to Conversation with Semantic Dementia: Using Enactment as a Compensatory Strategy in Everyday Social Interaction

ERIC Educational Resources Information Center

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

2013-01-01

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

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

NASA Astrophysics Data System (ADS)

McGuinness, Deborah; Fox, Peter; Hendler, James

2010-05-01

The goal of this effort is to design and implement a configurable and extensible semantic eScience framework (SESF). Configuration requires research into accommodating different levels of semantic expressivity and user requirements from use cases. Extensibility is being achieved in a modular approach to the semantic encodings (i.e. ontologies) performed in community settings, i.e. an ontology framework into which specific applications all the way up to communities can extend the semantics for their needs.We report on how we are accommodating the rapid advances in semantic technologies and tools and the sustainable software path for the future (certain) technical advances. In addition to a generalization of the current data science interface, we will present plans for an upper-level interface suitable for use by clearinghouses, and/or educational portals, digital libraries, and other disciplines.SESF builds upon previous work in the Virtual Solar-Terrestrial Observatory. The VSTO utilizes leading edge knowledge representation, query and reasoning techniques to support knowledge-enhanced search, data access, integration, and manipulation. It encodes term meanings and their inter-relationships in ontologies anduses these ontologies and associated inference engines to semantically enable the data services. The Semantically-Enabled Science Data Integration (SESDI) project implemented data integration capabilities among three sub-disciplines; solar radiation, volcanic outgassing and atmospheric structure using extensions to existingmodular ontolgies and used the VSTO data framework, while adding smart faceted search and semantic data registrationtools. The Semantic Provenance Capture in Data Ingest Systems (SPCDIS) has added explanation provenance capabilities to an observational data ingest pipeline for images of the Sun providing a set of tools to answer diverseend user questions such as ``Why does this image look bad?. http://tw.rpi.edu/portal/SESF
The Semantic eScience Framework

NASA Astrophysics Data System (ADS)

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

2009-12-01

The goal of this effort is to design and implement a configurable and extensible semantic eScience framework (SESF). Configuration requires research into accommodating different levels of semantic expressivity and user requirements from use cases. Extensibility is being achieved in a modular approach to the semantic encodings (i.e. ontologies) performed in community settings, i.e. an ontology framework into which specific applications all the way up to communities can extend the semantics for their needs.We report on how we are accommodating the rapid advances in semantic technologies and tools and the sustainable software path for the future (certain) technical advances. In addition to a generalization of the current data science interface, we will present plans for an upper-level interface suitable for use by clearinghouses, and/or educational portals, digital libraries, and other disciplines.SESF builds upon previous work in the Virtual Solar-Terrestrial Observatory. The VSTO utilizes leading edge knowledge representation, query and reasoning techniques to support knowledge-enhanced search, data access, integration, and manipulation. It encodes term meanings and their inter-relationships in ontologies anduses these ontologies and associated inference engines to semantically enable the data services. The Semantically-Enabled Science Data Integration (SESDI) project implemented data integration capabilities among three sub-disciplines; solar radiation, volcanic outgassing and atmospheric structure using extensions to existingmodular ontolgies and used the VSTO data framework, while adding smart faceted search and semantic data registrationtools. The Semantic Provenance Capture in Data Ingest Systems (SPCDIS) has added explanation provenance capabilities to an observational data ingest pipeline for images of the Sun providing a set of tools to answer diverseend user questions such as ``Why does this image look bad?.
Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans

PubMed Central

Si, Guangsen; Xu, Zeshui

2018-01-01

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

PubMed

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

2018-04-03

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

PubMed

Griffon, N; Charlet, J; Darmoni, Sj

2013-01-01

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

NASA Astrophysics Data System (ADS)

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

This paper describes how to perform syntactic parsing and semantic analysis in a dialog system. The paper especially deals with how to disambiguate potentially ambiguous sentences using the contextual information. Although syntactic parsing and semantic analysis are often studied independently of each other, correct parsing of a sentence often requires the semantic information on the input and/or the contextual information prior to the input. Accordingly, we merge syntactic parsing with semantic analysis, which enables syntactic parsing taking advantage of the semantic content of an input and its context. One of the biggest problems of semantic analysis is how to interpret dependency structures. We employ a framework for semantic representations that circumvents the problem. Within the framework, the meaning of any predicate is converted into a semantic representation which only permits a single type of predicate: an identifying predicate "aru". The semantic representations are expressed as sets of "attribute-value" pairs, and those semantic representations are stored in the context information. Our system disambiguates syntactic/semantic ambiguities of inputs referring to the attribute-value pairs in the context information. We have experimentally confirmed the effectiveness of our approach; specifically, the experiment confirmed high accuracy of parsing and correctness of generated semantic representations.
Issues in Semantic Memory: A Response to Glass and Holyoak. Technical Report No. 101.

ERIC Educational Resources Information Center

Shoben, Edward J.; And Others

Glass and Holyoak (1975) have raised two issues related to the distinction between set-theoretic and network theories of semantic memory, contending that: (a) their version of a network theory, the Marker Search model, is conceptually and empirically superior to the Feature Comparison model version of a set-theoretic theory; and (b) the contrast…
Contributions of Response Set and Semantic Relatedness to Cross-Modal Stroop-Like Picture--Word Interference in Children and Adults

ERIC Educational Resources Information Center

Hanauer, John B.; Brooks, Patricia J.

2005-01-01

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

DTIC Science & Technology

1989-12-15

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

PubMed Central

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

2011-01-01

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

Developing Visualization Techniques for Semantics-based Information Networks

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Hall, David R.

2003-01-01

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

NASA Technical Reports Server (NTRS)

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

1995-01-01

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

PubMed

Sereno, Sara C; O'Donnell, Patrick J; Sereno, Margaret E

2009-06-01

A largely unexplored aspect of lexical access in visual word recognition is "semantic size"--namely, the real-world size of an object to which a word refers. A total of 42 participants performed a lexical decision task on concrete nouns denoting either big or small objects (e.g., bookcase or teaspoon). Items were matched pairwise on relevant lexical dimensions. Participants' reaction times were reliably faster to semantically "big" versus "small" words. The results are discussed in terms of possible mechanisms, including more active representations for "big" words, due to the ecological importance attributed to large objects in the environment and the relative speed of neural responses to large objects.
Weakly Supervised Segmentation-Aided Classification of Urban Scenes from 3d LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Guinard, S.; Landrieu, L.

2017-05-01

We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.
Lexical Semantic Field as One of the Keys to Second Language Teaching

ERIC Educational Resources Information Center

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

2017-01-01

The article is devoted to the problem of the development of skills connected with the acquisition of foreign lexis (Lexis = all possible words or phrases in a language) on the basis of semantic fields (Semantic field = a lexical set of related items, e.g., colour, red, green, blue). This becomes possible due to grouping well-known and unknown to…
An Investigation of Two Instructional Settings in the Use of Semantic Mapping with Poor Readers. Program Report 85-4.

ERIC Educational Resources Information Center

Pittelman, Susan D.; And Others

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

PubMed

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

2010-02-01

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

PubMed

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

2001-01-01

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

NASA Astrophysics Data System (ADS)

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

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

PubMed

Yeari, Menahem; van den Broek, Paul

2016-09-01

It is a well-accepted view that the prior semantic (general) knowledge that readers possess plays a central role in reading comprehension. Nevertheless, computational models of reading comprehension have not integrated the simulation of semantic knowledge and online comprehension processes under a unified mathematical algorithm. The present article introduces a computational model that integrates the landscape model of comprehension processes with latent semantic analysis representation of semantic knowledge. In three sets of simulations of previous behavioral findings, the integrated model successfully simulated the activation and attenuation of predictive and bridging inferences during reading, as well as centrality estimations and recall of textual information after reading. Analyses of the computational results revealed new theoretical insights regarding the underlying mechanisms of the various comprehension phenomena.
Cross border semantic interoperability for clinical research: the EHR4CR semantic resources and services

PubMed Central

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

2016-01-01

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

NASA Astrophysics Data System (ADS)

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

2014-07-01

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

ERIC Educational Resources Information Center

Sheng, Li; McGregor, Karla K.

2010-01-01

Purpose: To determine whether children with specific language impairment (SLI) show deficits in lexical-semantic organization and, if so, whether these deficits are commensurate with their delay in vocabulary size and whether the deficits affect all children with SLI. Method: Fourteen children with SLI, 14 age matches (AM), and 14 expressive…
Fine-grained semantic categorization across the abstract and concrete domains.

PubMed

Ghio, Marta; Vaghi, Matilde Maria Serena; Tettamanti, Marco

2013-01-01

A consolidated approach to the study of the mental representation of word meanings has consisted in contrasting different domains of knowledge, broadly reflecting the abstract-concrete dichotomy. More fine-grained semantic distinctions have emerged in neuropsychological and cognitive neuroscience work, reflecting semantic category specificity, but almost exclusively within the concrete domain. Theoretical advances, particularly within the area of embodied cognition, have more recently put forward the idea that distributed neural representations tied to the kinds of experience maintained with the concepts' referents might distinguish conceptual meanings with a high degree of specificity, including those within the abstract domain. Here we report the results of two psycholinguistic rating studies incorporating such theoretical advances with two main objectives: first, to provide empirical evidence of fine-grained distinctions within both the abstract and the concrete semantic domains with respect to relevant psycholinguistic dimensions; second, to develop a carefully controlled linguistic stimulus set that may be used for auditory as well as visual neuroimaging studies focusing on the parametrization of the semantic space beyond the abstract-concrete dichotomy. Ninety-six participants rated a set of 210 sentences across pre-selected concrete (mouth, hand, or leg action-related) and abstract (mental state-, emotion-, mathematics-related) categories, with respect either to different semantic domain-related scales (rating study 1), or to concreteness, familiarity, and context availability (rating study 2). Inferential statistics and correspondence analyses highlighted distinguishing semantic and psycholinguistic traits for each of the pre-selected categories, indicating that a simple abstract-concrete dichotomy is not sufficient to account for the entire semantic variability within either domains.
Fine-Grained Semantic Categorization across the Abstract and Concrete Domains

PubMed Central

Tettamanti, Marco

2013-01-01

A consolidated approach to the study of the mental representation of word meanings has consisted in contrasting different domains of knowledge, broadly reflecting the abstract-concrete dichotomy. More fine-grained semantic distinctions have emerged in neuropsychological and cognitive neuroscience work, reflecting semantic category specificity, but almost exclusively within the concrete domain. Theoretical advances, particularly within the area of embodied cognition, have more recently put forward the idea that distributed neural representations tied to the kinds of experience maintained with the concepts' referents might distinguish conceptual meanings with a high degree of specificity, including those within the abstract domain. Here we report the results of two psycholinguistic rating studies incorporating such theoretical advances with two main objectives: first, to provide empirical evidence of fine-grained distinctions within both the abstract and the concrete semantic domains with respect to relevant psycholinguistic dimensions; second, to develop a carefully controlled linguistic stimulus set that may be used for auditory as well as visual neuroimaging studies focusing on the parametrization of the semantic space beyond the abstract-concrete dichotomy. Ninety-six participants rated a set of 210 sentences across pre-selected concrete (mouth, hand, or leg action-related) and abstract (mental state-, emotion-, mathematics-related) categories, with respect either to different semantic domain-related scales (rating study 1), or to concreteness, familiarity, and context availability (rating study 2). Inferential statistics and correspondence analyses highlighted distinguishing semantic and psycholinguistic traits for each of the pre-selected categories, indicating that a simple abstract-concrete dichotomy is not sufficient to account for the entire semantic variability within either domains. PMID:23825625
Vection is modulated by the semantic meaning of stimuli and experimental instructions.

PubMed

Ogawa, Masaki; Seno, Takeharu

2014-01-01

Vection strength is modulated by the semantic meanings of stimuli. In experiment 1--even though vection stimuli were of uniform size, color, and luminance--when they also had semantic meaning as falling objects, vection was inhibited. Specifically, stimuli perceived as feathers, petals, and leaves did not effectively induce vection. In experiment 2 we used the downward motion of identical dots to induce vection. Participants observed stimuli while holding either an umbrella or a wooden sword. Results showed that vection was inhibited when participants held the umbrella and the stimuli was perceived as rain or snow falling. The two experiments suggest that vection is modulated by the semantic meaning of stimuli.
Privacy Preservation in Context-Aware Systems

DTIC Science & Technology

2011-01-01

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

PubMed

Marelli, Marco; Amenta, Simona; Crepaldi, Davide

2015-01-01

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

PubMed

Borovsky, Arielle; Ellis, Erica M; Evans, Julia L; Elman, Jeffrey L

2016-11-01

Although the size of a child's vocabulary associates with language-processing skills, little is understood regarding how this relation emerges. This investigation asks whether and how the structure of vocabulary knowledge affects language processing in English-learning 24-month-old children (N = 32; 18 F, 14 M). Parental vocabulary report was used to calculate semantic density in several early-acquired semantic categories. Performance on two language-processing tasks (lexical recognition and sentence processing) was compared as a function of semantic density. In both tasks, real-time comprehension was facilitated for higher density items, whereas lower density items experienced more interference. The findings indicate that language-processing skills develop heterogeneously and are influenced by the semantic network surrounding a known word. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

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.
Semantic similarity between ontologies at different scales

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

Zhang, Qingpeng; Haglin, David J.

In the past decade, existing and new knowledge and datasets has been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea viamore » studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three Gene Ontology slims (Plant, Yeast, and Candida, among which the latter two belong to the same kingdom—Fungi) using four popular measures commonly applied to biomedical ontologies (Resnik, Lin, Jiang-Conrath, and SimRel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performance of Jiang-Conrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by (a) consistently showing that Yeast and Candida are more similar (as compared to Plant) at different scales, and (b) small deviations of the similarity values after excluding a majority of nodes from several lower scales. This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.« less
Self-referential processing is distinct from semantic elaboration: evidence from long-term memory effects in a patient with amnesia and semantic impairments.

PubMed

Sui, Jie; Humphreys, Glyn W

2013-11-01

We report data demonstrating that self-referential encoding facilitates memory performance in the absence of effects of semantic elaboration in a severely amnesic patient also suffering semantic problems. In Part 1, the patient, GA, was trained to associate items with the self or a familiar other during the encoding phase of a memory task (self-ownership decisions in Experiment 1 and self-evaluation decisions in Experiment 2). Tests of memory showed a consistent self-reference advantage, relative to a condition where the reference was another person in both experiments. The pattern of the self-reference advantage was similar to that in healthy controls. In Part 2 we demonstrate that GA showed minimal effects of semantic elaboration on memory for items he semantically classified, compared with items subject to physical size decisions; in contrast, healthy controls demonstrated enhanced memory performance after semantic relative to physical encoding. The results indicate that self-referential encoding, not semantic elaboration, improves memory in amnesia. Self-referential processing may provide a unique scaffold to help improve learning in amnesic cases. Copyright © 2013 Elsevier Ltd. All rights reserved.
Task Oriented Evaluation of Module Extraction Techniques

NASA Astrophysics Data System (ADS)

Palmisano, Ignazio; Tamma, Valentina; Payne, Terry; Doran, Paul

Ontology Modularization techniques identify coherent and often reusable regions within an ontology. The ability to identify such modules, thus potentially reducing the size or complexity of an ontology for a given task or set of concepts is increasingly important in the Semantic Web as domain ontologies increase in terms of size, complexity and expressivity. To date, many techniques have been developed, but evaluation of the results of these techniques is sketchy and somewhat ad hoc. Theoretical properties of modularization algorithms have only been studied in a small number of cases. This paper presents an empirical analysis of a number of modularization techniques, and the modules they identify over a number of diverse ontologies, by utilizing objective, task-oriented measures to evaluate the fitness of the modules for a number of statistical classification problems.
Semantic deficits in Spanish-English bilingual children with language impairment.

PubMed

Sheng, Li; Peña, Elizabeth D; Bedore, Lisa M; Fiestas, Christine E

2012-02-01

To examine the nature and extent of semantic deficits in bilingual children with language impairment (LI). Thirty-seven Spanish-English bilingual children with LI (ranging from age 7;0 [years;months] to 9;10) and 37 typically developing (TD) age-matched peers generated 3 associations to 12 pairs of translation equivalents in English and Spanish. Responses were coded as paradigmatic (e.g., dinner-lunch, cena-desayuno [dinner-breakfast]), syntagmatic (e.g., delicious-pizza, delicioso-frijoles [delicious-beans]), and errors (e.g., wearing-where, vestirse-mal [to get dressed-bad]). A semantic depth score was derived in each language and conceptually by combining children's performance in both languages. The LI group achieved significantly lower semantic depth scores than the TD group after controlling for group differences in vocabulary size. Children showed higher conceptual scores than single-language scores. Both groups showed decreases in semantic depth scores across multiple elicitations. Analyses of individual performances indicated that semantic deficits (1 SD below the TD mean semantic depth score) were manifested in 65% of the children with LI and in 14% of the TD children. School-age bilingual children with and without LI demonstrated spreading activation of semantic networks. Consistent with the literature on monolingual children with LI, sparsely linked semantic networks characterize a considerable proportion of bilingual children with LI.
No one way ticket from orthography to semantics in recognition memory: N400 and P200 effects of associations.

PubMed

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

2016-05-15

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

PubMed

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

2016-10-01

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

PubMed

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

2017-03-01

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

NASA Technical Reports Server (NTRS)

Combs, Nathan

1993-01-01

A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content.
Semantic knowledge fractionations: verbal propositions vs. perceptual input? Evidence from a child with Klinefelter syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2013-04-01

This paper addresses the relative independence of different types of lexical- and factually-based semantic knowledge in JM, a 9-year-old boy with Klinefelter syndrome (KS). JM was matched to typically developing (TD) controls on the basis of chronological age. Lexical-semantic knowledge was investigated for common noun (CN) and mathematical vocabulary items (MV). Factually-based semantic knowledge was investigated for general and number facts. For CN items, JM's lexical stores were of a normal size but the volume of correct 'sensory feature' semantic knowledge he generated within verbal item descriptions was significantly reduced. He was also significantly impaired at naming item descriptions and pictures, particularly for fruit and vegetables. There was also weak object decision for fruit and vegetables. In contrast, for MV items, JM's lexical stores were elevated, with no significant difference in the amount and type of correct semantic knowledge generated within verbal item descriptions and normal naming. JM's fact retrieval accuracy was normal for all types of factual knowledge. JM's performance indicated a dissociation between the representation of CN and MV vocabulary items during development. JM's preserved semantic knowledge of facts in the face of impaired semantic knowledge of vocabulary also suggests that factually-based semantic knowledge representation is not dependent on normal lexical-semantic knowledge during development. These findings are discussed in relation to the emergence of distinct semantic knowledge representations during development, due to differing degrees of dependency upon the acquisition and representation of semantic knowledge from verbal propositions and perceptual input.
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.
Use of Modality and Negation in Semantically-Informed Syntactic MT

DTIC Science & Technology

2012-06-01

Longman Dictionary of Contemporary English (LDOCE). 422 Baker et al. Modality and Negation in SIMT We produced the full English MN lexicon semi...English sentence pairs, and a bilingual dictionary with 113,911 entries. For our development and test sets, we split the NIST MT-08 test set into two...for combining MT and semantics (termed distillation) to answer the informa- tion needs of monolingual speakers using multilingual sources. Proper
Differential interference effects of negative emotional states on subsequent semantic and perceptual processing

PubMed Central

Gorlick, Marissa A.; Mather, Mara

2012-01-01

Past studies have revealed that encountering negative events interferes with cognitive processing of subsequent stimuli. The present study investigated whether negative events affect semantic and perceptual processing differently. Presentation of negative pictures produced slower reaction times than neutral or positive pictures in tasks that require semantic processing, such as natural/man-made judgments about drawings of objects, commonness judgments about objects, and categorical judgments about pairs of words. In contrast, negative picture presentation did not slow down judgments in subsequent perceptual processing (e.g., color judgments about words, and size judgments about objects). The subjective arousal level of negative pictures did not modulate the interference effects on semantic/perceptual processing. These findings indicate that encountering negative emotional events interferes with semantic processing of subsequent stimuli more strongly than perceptual processing, and that not all types of subsequent cognitive processing are impaired by negative events. PMID:22142207
Verbal fluency in bilingual Spanish/English Alzheimer's disease patients.

PubMed

Salvatierra, Judy; Rosselli, Monica; Acevedo, Amarilis; Duara, Ranjan

2007-01-01

Studies have demonstrated that in verbal fluency tests, monolinguals with Alzheimer's disease (AD) show greater difficulties retrieving words based on semantic rather than phonemic rules. The present study aimed to determine whether this difficulty was reproduced in both languages of Spanish/English bilinguals with mild to moderate AD whose primary language was Spanish. Performance on semantic and phonemic verbal fluency of 11 bilingual AD patients was compared to the performance of 11 cognitively normal, elderly bilingual individuals matched for gender, age, level of education, and degree of bilingualism. Cognitively normal subjects retrieved significantly more items under the semantic condition compared to the phonemic, whereas the performance of AD patients was similar under both conditions, suggesting greater decline in semantic verbal fluency tests. This pattern was produced in both languages, implying a related semantic decline in both languages. Results from this study should be considered preliminary because of the small sample size.
Differential interference effects of negative emotional states on subsequent semantic and perceptual processing.

PubMed

Sakaki, Michiko; Gorlick, Marissa A; Mather, Mara

2011-12-01

Past studies have revealed that encountering negative events interferes with cognitive processing of subsequent stimuli. The present study investigates whether negative events affect semantic and perceptual processing differently. Presentation of negative pictures produced slower reaction times than neutral or positive pictures in tasks that require semantic processing, such as natural or man-made judgments about drawings of objects, commonness judgments about objects, and categorical judgments about pairs of words. In contrast, negative picture presentation did not slow down judgments in subsequent perceptual processing (e.g., color judgments about words, size judgments about objects). The subjective arousal level of negative pictures did not modulate the interference effects on semantic or perceptual processing. These findings indicate that encountering negative emotional events interferes with semantic processing of subsequent stimuli more strongly than perceptual processing, and that not all types of subsequent cognitive processing are impaired by negative events. (c) 2011 APA, all rights reserved.
Progress toward a Semantic eScience Framework; building on advanced cyberinfrastructure

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

PubMed

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

2014-06-01

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

NASA Technical Reports Server (NTRS)

Dowek, Gilles; Munoz, Cesar A.; Rocha, Camilo

2009-01-01

The Plan Execution Interchange Language (PLEXIL) is a synchronous language developed by NASA to support autonomous spacecraft operations. In this paper, we propose a rewriting logic semantics of PLEXIL in Maude, a high-performance logical engine. The rewriting logic semantics is by itself a formal interpreter of the language and can be used as a semantic benchmark for the implementation of PLEXIL executives. The implementation in Maude has the additional benefit of making available to PLEXIL designers and developers all the formal analysis and verification tools provided by Maude. The formalization of the PLEXIL semantics in rewriting logic poses an interesting challenge due to the synchronous nature of the language and the prioritized rules defining its semantics. To overcome this difficulty, we propose a general procedure for simulating synchronous set relations in rewriting logic that is sound and, for deterministic relations, complete. We also report on the finding of two issues at the design level of the original PLEXIL semantics that were identified with the help of the executable specification in Maude.
Setting semantics: conceptual set can determine the physical properties that capture attention.

PubMed

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

2014-08-01

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

Semantic processing of EHR data for clinical research.

PubMed

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

2015-12-01

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

PubMed

Shao, Zeshu; Roelofs, Ardi; Martin, Randi C; Meyer, Antje S

2015-11-01

In 2 studies, we examined whether explicit distractors are necessary and sufficient to evoke selective inhibition in 3 naming tasks: the semantic blocking, picture-word interference, and color-word Stroop task. Delta plots were used to quantify the size of the interference effects as a function of reaction time (RT). Selective inhibition was operationalized as the decrease in the size of the interference effect as a function of naming RT. For all naming tasks, mean naming RTs were significantly longer in the interference condition than in the control condition. The slopes of the interference effects for the longest naming RTs correlated with the magnitude of the mean interference effect in both the semantic blocking task and the picture-word interference task, suggesting that selective inhibition was involved to reduce the interference from strong semantic competitors either invoked by a single explicit competitor or strong implicit competitors in picture naming. However, there was no correlation between the slopes and the mean interference effect in the Stroop task, suggesting less importance of selective inhibition in this task despite explicit distractors. Whereas the results of the semantic blocking task suggest that an explicit distractor is not necessary for triggering inhibition, the results of the Stroop task suggest that such a distractor is not sufficient for evoking inhibition either. (c) 2015 APA, all rights reserved).
The effects of self-instruction training on a deaf child's semantic and pragmatic production.

PubMed

Swanson, H L

1987-10-01

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

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

PubMed Central

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

2017-01-01

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

NASA Astrophysics Data System (ADS)

Benjamin, David P.; Walker, Adrian

2005-03-01

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

PubMed

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

2014-11-01

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

PubMed

Li, Yanpeng; Liu, Hongfang

2015-01-01

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

USGS Publications Warehouse

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

2015-01-01

Use cases, information modeling, and linked data techniques are Semantic Web technologies used to develop a prototype system that integrates scientific observations from four independent USGS and cooperator data systems. The techniques were tested with a use case goal of creating a data set for use in exploring potential relationships among freshwater fish populations and environmental factors. The resulting prototype extracts data from the BioData Retrieval System, the Multistate Aquatic Resource Information System, the National Geochemical Survey, and the National Hydrography Dataset. A prototype user interface allows a scientist to select observations from these data systems and combine them into a single data set in RDF format that includes explicitly defined relationships and data definitions. The project was funded by the USGS Community for Data Integration and undertaken by the Community for Data Integration Semantic Web Working Group in order to demonstrate use of Semantic Web technologies by scientists. This allows scientists to simultaneously explore data that are available in multiple, disparate systems beyond those they traditionally have used.
SU30. Long-Term Memory Deficits in Schizophrenia: Are All Things Equal?

PubMed Central

Rossell, Susan

2017-01-01

Abstract Background: Kraepelin and Bleulernoted that patients with schizophrenia had significant cognitive deficits over a century ago; however, their observations with regard to long-term memory have not born out within empirical studies. They reported that episodic memory was intact but indicated that organization of memories, or semantic memory, was disordered. This study aimed to synthesize a century of research in the 2 long-term memory processes of episodic and semantic memory across the psychosis continuum: chronic patients, first-episode patients, high risk for psychosis cohorts, and persons with high schizotypy. Methods: A systematic review and meta-analysis was completed within the 2 domains of long-term memory across the psychosis continuum. Search terms included long-term memory, episodic, semantic, and derivations of these terms. The data were synthesized independently for episodic and semantic memory. Four independent populations were investigated: chronic patients, first-episode patients, high risk for psychosis cohorts, and persons with high schizotypy. Our approach followed the PRISMA guidelines. Thus, the pooled mean effect sizes are reported for 8 analyses. These effect sizes represent case cohort in comparison to a healthy control cohort. Results: The results were as follows, for episodic memory: chronic patients d = 1.12, first-episode patients d = 1.12, high risk d = 1.14, and high schizotypy d = 0.13. Thus, establishing that there is poor evidence of episodic memory deficits in persons with high schizotypy. For semantic memory, the literature showed a different pattern: chronic patients d = 1.2, first-episode patients d = 1.08, high risk d = 1.16, and high schizotypy d = 0.95. Thus, a consistent degree of semantic memory deficits across the continuum. Conclusion: The literature suggests a dissociated pattern of long-term memory deficits; whereby semantic memory abnormalities are more likely to be considered endophenotypes or cognitive markers for schizophrenia than episodic memory deficits. Differential patterns of semantic memory organization are argued to be present prior to the onset of the disorder. There is additional evidence to suggest that idiosyncratic storage of semantic material underlies the development of the usual beliefs and speech patterns present in the forms of delusions and formal thought disorder. Consequently, semantic memory might be a useful target for cognitive remediation.
SEMANTIC3D.NET: a New Large-Scale Point Cloud Classification Benchmark

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

NASA Astrophysics Data System (ADS)

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

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

ERIC Educational Resources Information Center

Shao, Zeshu; Roelofs, Ardi; Martin, Randi C.; Meyer, Antje S.

2015-01-01

In 2 studies, we examined whether explicit distractors are necessary and sufficient to evoke selective inhibition in 3 naming tasks: the semantic blocking, picture-word interference, and color-word Stroop task. Delta plots were used to quantify the size of the interference effects as a function of reaction time (RT). Selective inhibition was…
Supporting Semantic Annotation of Educational Content by Automatic Extraction of Hierarchical Domain Relationships

ERIC Educational Resources Information Center

Vrablecová, Petra; Šimko, Marián

2016-01-01

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

ERIC Educational Resources Information Center

Kwon, Youan; Nam, Kichun; Lee, Yoonhyoung

2012-01-01

The purpose of this study was to examine whether the N400 is affected by the semantic richness of associated neighboring word members or by the density of the orthographic syllable neighborhood. Another purpose of this study was to investigate the source of the different LPC in respect to the semantic richness. To do so, the density of the…
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
The role of the right hemisphere in semantic control: A case-series comparison of right and left hemisphere stroke

PubMed Central

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

2016-01-01

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

NASA Astrophysics Data System (ADS)

Cano, Ramiro; Abián, Alberto; Mena, Elena

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

A Robust Geometric Model for Argument Classification

NASA Astrophysics Data System (ADS)

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

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

PubMed

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

2017-01-01

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

ERIC Educational Resources Information Center

Key, Mary Ritchie

A theory of semantics focusing on relationships between meaning and sound patterns in language evolution is proposed. Using cognate sets from traditional comparative studies of closely-related languages in well-defined language families, the theory addresses the use and shifting of language components. The theory begins with the ego attempting to…
Attentional Sensitization of Unconscious Cognition: Task Sets Modulate Subsequent Masked Semantic Priming

ERIC Educational Resources Information Center

Kiefer, Markus; Martens, Ulla

2010-01-01

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

ERIC Educational Resources Information Center

Richardson, Ian M.

1990-01-01

A possible syllabus for English for Science and Technology is suggested based upon a set of causal relations, arising from a logical description of the presuppositional rhetoric of scientific passages that underlie most semantic functions. An empirical study is reported of the semantic functions present in 52 randomly selected passages.…
Semantic and Phonological Task-Set Priming and Stimulus Processing Investigated Using Magnetoencephalography (MEG)

ERIC Educational Resources Information Center

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

2007-01-01

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

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

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

2005-02-02

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

PubMed Central

Bosworth, Rain G.; Emmorey, Karen

2010-01-01

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

PubMed

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

2018-05-19

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

NASA Technical Reports Server (NTRS)

Narock, Thomas; Fox, Peter

2011-01-01

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

PubMed Central

Shen, Manqiong; Xie, Jiushu; Liu, Wenjuan; Lin, Wenjie; Chen, Zhuoming; Marmolejo-Ramos, Fernando; Wang, Ruiming

2016-01-01

Grounded cognition suggests that conceptual processing shares cognitive resources with perceptual processing. Hence, conceptual processing should be affected by perceptual processing, and vice versa. The current study explored the relationship between conceptual and perceptual processing of size. Within a pair of words, we manipulated the font size of each word, which was either congruent or incongruent with the actual size of the referred object. In Experiment 1a, participants compared object sizes that were referred to by word pairs. Higher accuracy was observed in the congruent condition (e.g., word pairs referring to larger objects in larger font sizes) than in the incongruent condition. This is known as the size-congruency effect. In Experiments 1b and 2, participants compared the font sizes of these word pairs. The size-congruency effect was not observed. In Experiments 3a and 3b, participants compared object and font sizes of word pairs depending on a task cue. Results showed that perceptual processing affected conceptual processing, and vice versa. This suggested that the association between conceptual and perceptual processes may be bidirectional but further modulated by semantic processing. Specifically, conceptual processing might only affect perceptual processing when semantic information is activated. The current study PMID:27512529
Classification with an edge: Improving semantic image segmentation with boundary detection

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

PubMed

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

2009-10-01

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

PubMed

Scheuer, Claudia Ines; Stivanin, Luciene; Mangilli, Laura Davidson

2004-01-01

[corrected] The relation between picture naming and the short and long term memories. to verify the ability of picture naming based on phonological and semantic queues, relating it to memory. 80 pictures selected from a set of 400 (Cycowicz et al., 1997) were presented to 80 children with ages ranging from 3 to 6 years. Responses were classified in semantic and phonologic errors and number of correct answers. The effect of the articulatory complexity was significant and the effect of the semantic complexity was not significant. Naming is the result of memory activation which is organized in categories, physical properties and function; phonologic effects do interfere in the activity of naming, whereas the semantic effects reflect that the long term memory is organized in categories which are dependant of the context and of the development.
Linguistic multi-criteria decision-making with representing semantics by programming

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

PubMed

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

2000-08-01

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

PubMed

Dombrowski, Ernst

2013-01-01

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

PubMed

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

2012-04-01

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

PubMed

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

2018-04-16

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

PubMed

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

1995-01-01

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

A semantic data dictionary method for database schema integration in CIESIN

NASA Astrophysics Data System (ADS)

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

1993-08-01

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

PubMed

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

2013-01-01

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

PubMed

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

2015-04-01

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

PubMed

Kotz, Sonja A; Paulmann, Silke

2007-06-02

To communicate emotionally entails that a listener understands a verbal message but also the emotional prosody going along with it. So far the time course and interaction of these emotional 'channels' is still poorly understood. The current set of event-related brain potential (ERP) experiments investigated both the interactive time course of emotional prosody with semantics and of emotional prosody independent of emotional semantics using a cross-splicing method. In a probe verification task (Experiment 1) prosodic expectancy violations elicited a positivity, while a combined prosodic-semantic expectancy violation elicited a negativity. Comparable ERP results were obtained in an emotional prosodic categorization task (Experiment 2). The present data support different ERP responses with distinct time courses and topographies elicited as a function of prosodic expectancy and combined prosodic-semantic expectancy during emotional prosodic processing and combined emotional prosody/emotional semantic processing. These differences suggest that the interaction of more than one emotional channel facilitates subtle transitions in an emotional sentence context.
The Nombela 2.0 semantic battery: an updated Spanish instrument for the study of semantic processing.

PubMed

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

2015-01-01

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

PubMed

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

2016-05-01

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

PubMed Central

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

2014-01-01

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

PubMed Central

Krajcsi, Attila; Lengyel, Gábor; Kojouharova, Petia

2016-01-01

Human number understanding is thought to rely on the analog number system (ANS), working according to Weber’s law. We propose an alternative account, suggesting that symbolic mathematical knowledge is based on a discrete semantic system (DSS), a representation that stores values in a semantic network, similar to the mental lexicon or to a conceptual network. Here, focusing on the phenomena of numerical distance and size effects in comparison tasks, first we discuss how a DSS model could explain these numerical effects. Second, we demonstrate that the DSS model can give quantitatively as appropriate a description of the effects as the ANS model. Finally, we show that symbolic numerical size effect is mainly influenced by the frequency of the symbols, and not by the ratios of their values. This last result suggests that numerical distance and size effects cannot be caused by the ANS, while the DSS model might be the alternative approach that can explain the frequency-based size effect. PMID:27917139
Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

PubMed Central

Nestor, P.G.; Han, S.D.; Niznikiewicz, M.; Salisbury, D.; Spencer, K.; Shenton, M.E.; McCarley, R.W.

2010-01-01

We view schizophrenia as producing a failure of attentional modulation that leads to a breakdown in the selective enhancement or inhibition of semantic/lexical representations whose biological substrata are widely distributed across left (dominant) temporal and frontal lobes. Supporting behavioral evidence includes word recall studies that have pointed to a disturbance in connectivity (associative strength) but not network size (number of associates) in patients with schizophrenia. Paralleling these findings are recent neural network simulation studies of the abnormal connectivity effect in schizophrenia through ‘lesioning’ network connection weights while holding constant network size. Supporting evidence at the level of biology are in vitro studies examining N-methyl-d-aspartate (NMDA) receptor antagonists on recurrent inhibition; simulations in neural populations with realistically modeled biophysical properties show NMDA antagonists produce a schizophrenia-like disturbance in pattern association. We propose a similar failure of NMDA-mediated recurrent inhibition as a candidate biological substrate for attention and semantic anomalies of schizophrenia. PMID:11454433
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.
Early Decomposition in Visual Word Recognition: Dissociating Morphology, Form, and Meaning

ERIC Educational Resources Information Center

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

2008-01-01

The role of morphological, semantic, and form-based factors in the early stages of visual word recognition was investigated across different SOAs in a masked priming paradigm, focusing on English derivational morphology. In a first set of experiments, stimulus pairs co-varying in morphological decomposability and in semantic and orthographic…
Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-WorldChemical Production

EPA Science Inventory

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

ERIC Educational Resources Information Center

Pudovkin, Alexander I.; Garfield, Eugene

2002-01-01

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

ERIC Educational Resources Information Center

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

2017-01-01

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

PubMed

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

2009-09-01

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

PubMed Central

Roberts, Kirk; Patra, Braja Gopal

2017-01-01

This paper presents a method for converting natural language questions about structured data in the electronic health record (EHR) into logical forms. The logical forms can then subsequently be converted to EHR-dependent structured queries. The natural language processing task, known as semantic parsing, has the potential to convert questions to logical forms with extremely high precision, resulting in a system that is usable and trusted by clinicians for real-time use in clinical settings. We propose a hybrid semantic parsing method, combining rule-based methods with a machine learning-based classifier. The overall semantic parsing precision on a set of 212 questions is 95.6%. The parser’s rules furthermore allow it to “know what it does not know”, enabling the system to indicate when unknown terms prevent it from understanding the question’s full logical structure. When combined with a module for converting a logical form into an EHR-dependent query, this high-precision approach allows for a question answering system to provide a user with a single, verifiably correct answer. PMID:29854217
Medical Image Analysis by Cognitive Information Systems - a Review.

PubMed

Ogiela, Lidia; Takizawa, Makoto

2016-10-01

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

PubMed

Rautek, Peter; Bruckner, Stefan; Gröller, Eduard

2007-01-01

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

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

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

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

NASA Astrophysics Data System (ADS)

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

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

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

ERIC Educational Resources Information Center

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

2011-01-01

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

PubMed

Grilli, Matthew D; Verfaellie, Mieke

2014-08-01

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

PubMed

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

2013-06-01

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

PubMed

Mellem, Monika S; Jasmin, Kyle M; Peng, Cynthia; Martin, Alex

2016-08-01

The anterior region of the left superior temporal gyrus/superior temporal sulcus (aSTG/STS) has been implicated in two very different cognitive functions: sentence processing and social-emotional processing. However, the vast majority of the sentence stimuli in previous reports have been of a social or social-emotional nature suggesting that sentence processing may be confounded with semantic content. To evaluate this possibility we had subjects read word lists that differed in phrase/constituent size (single words, 3-word phrases, 6-word sentences) and semantic content (social-emotional, social, and inanimate objects) while scanned in a 7T environment. This allowed us to investigate if the aSTG/STS responded to increasing constituent structure (with increased activity as a function of constituent size) with or without regard to a specific domain of concepts, i.e., social and/or social-emotional content. Activity in the left aSTG/STS was found to increase with constituent size. This region was also modulated by content, however, such that social-emotional concepts were preferred over social and object stimuli. Reading also induced content type effects in domain-specific semantic regions. Those preferring social-emotional content included aSTG/STS, inferior frontal gyrus, posterior STS, lateral fusiform, ventromedial prefrontal cortex, and amygdala, regions included in the "social brain", while those preferring object content included parahippocampal gyrus, retrosplenial cortex, and caudate, regions involved in object processing. These results suggest that semantic content affects higher-level linguistic processing and should be taken into account in future studies. Copyright © 2016. Published by Elsevier Ltd.
Semantic annotation in biomedicine: the current landscape.

PubMed

Jovanović, Jelena; Bagheri, Ebrahim

2017-09-22

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

ERIC Educational Resources Information Center

Lev-Ari, Shiri

2016-01-01

People differ in the size of their social network, and thus in the properties of the linguistic input they receive. This article examines whether differences in social network size influence individuals' linguistic skills in their native language, focusing on global comprehension of evaluative language. Study 1 exploits the natural variation in…
Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension

PubMed Central

Cheyette, Samuel J.; Plaut, David C.

2016-01-01

The study of the N400 event-related brain potential has provided fundamental insights into the nature of real-time comprehension processes, and its amplitude is modulated by a wide variety of stimulus and context factors. It is generally thought to reflect the difficulty of semantic access, but formulating a precise characterization of this process has proved difficult. Laszlo and colleagues (Laszlo & Plaut, 2012, Brain and Language, 120, 271-281; Laszlo & Armstrong, 2014, Brain and Language, 132, 22-27) used physiologically constrained neural networks to model the N400 as transient over-activation within semantic representations, arising as a consequence of the distribution of excitation and inhibition within and between cortical areas. The current work extends this approach to successfully model effects on both N400 amplitudes and behavior of word frequency, semantic richness, repetition, semantic and associative priming, and orthographic neighborhood size. The account is argued to be preferable to one based on “implicit semantic prediction error” (Rabovsky & McRae, 2014, Cognition, 132, 68-98) for a number of reasons, the most fundamental of which is that the current model actually produces N400-like waveforms in its real-time activation dynamics. PMID:27871623
Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension.

PubMed

Cheyette, Samuel J; Plaut, David C

2017-05-01

The study of the N400 event-related brain potential has provided fundamental insights into the nature of real-time comprehension processes, and its amplitude is modulated by a wide variety of stimulus and context factors. It is generally thought to reflect the difficulty of semantic access, but formulating a precise characterization of this process has proved difficult. Laszlo and colleagues (Laszlo & Plaut, 2012; Laszlo & Armstrong, 2014) used physiologically constrained neural networks to model the N400 as transient over-activation within semantic representations, arising as a consequence of the distribution of excitation and inhibition within and between cortical areas. The current work extends this approach to successfully model effects on both N400 amplitudes and behavior of word frequency, semantic richness, repetition, semantic and associative priming, and orthographic neighborhood size. The account is argued to be preferable to one based on "implicit semantic prediction error" (Rabovsky & McRae, 2014) for a number of reasons, the most fundamental of which is that the current model actually produces N400-like waveforms in its real-time activation dynamics. Copyright © 2016 Elsevier B.V. All rights reserved.
Neural correlates of the object-recall process in semantic memory.

PubMed

Assaf, Michal; Calhoun, Vince D; Kuzu, Cheedem H; Kraut, Michael A; Rivkin, Paul R; Hart, John; Pearlson, Godfrey D

2006-10-30

The recall of an object from features is a specific operation in semantic memory in which the thalamus and pre-supplementary motor area (pre-SMA) are integrally involved. Other higher-order semantic cortices are also likely to be involved. We used the object-recall-from-features paradigm, with more sensitive scanning techniques and larger sample size, to replicate and extend our previous results. Eighteen right-handed healthy participants performed an object-recall task and an association semantic task, while undergoing functional magnetic resonance imaging. During object-recall, subjects determined whether words pairs describing object features combined to recall an object; during the association task they decided if two words were related. Of brain areas specifically involved in object recall, in addition to the thalamus and pre-SMA, other regions included the left dorsolateral prefrontal cortex, inferior parietal lobule, and middle temporal gyrus, and bilateral rostral anterior cingulate and inferior frontal gyri. These regions are involved in semantic processing, verbal working memory and response-conflict detection and monitoring. The thalamus likely helps to coordinate activity of these different brain areas. Understanding the circuit that normally mediates this process is relevant for schizophrenia, where many regions in this circuit are functionally abnormal and semantic memory is impaired.
Dissociating the effects of semantic grouping and rehearsal strategies on event-related brain potentials.

PubMed

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

2014-12-01

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

PubMed

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

2007-01-01

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

PubMed

Löfkvist, Ulrika; Almkvist, Ove; Lyxell, Björn; Tallberg, Ing-Mari

2014-02-01

Lexical-semantic ability was investigated among children aged 6-9 years with cochlear implants (CI) and compared to clinical groups of children with language impairment (LI) and autism spectrum disorder (ASD) as well as to age-matched children with normal hearing (NH). In addition, the influence of age at implantation on lexical-semantic ability was investigated among children with CI. 97 children divided into four groups participated, CI (n=34), LI (n=12), ASD (n=12), and NH (n=39). A battery of tests, including picture naming, receptive vocabulary and knowledge of semantic features, was used for assessment. A semantic response analysis of the erroneous responses on the picture-naming test was also performed. The group of children with CI exhibited a naming ability comparable to that of the age-matched children with NH, and they also possessed a relevant semantic knowledge of certain words that they were unable to name correctly. Children with CI had a significantly better understanding of words compared to the children with LI and ASD, but a worse understanding than those with NH. The significant differences between groups remained after controlling for age and non-verbal cognitive ability. The children with CI demonstrated lexical-semantic abilities comparable to age-matched children with NH, while children with LI and ASD had a more atypical lexical-semantic profile and poorer sizes of expressive and receptive vocabularies. Dissimilar causes of neurodevelopmental processes seemingly affected lexical-semantic abilities in different ways in the clinical groups. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
AlzPharm: integration of neurodegeneration data using RDF.

PubMed

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

2007-05-09

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

PubMed Central

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

2007-01-01

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

PubMed

Taha, Kamal

2014-01-01

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

PubMed

Berg, Jody-Lynn; Swan, Natasha M; Banks, Sarah J; Miller, Justin B

2016-09-01

Cognitive set shifting requires flexible application of lower level processes. The Delis-Kaplan Executive Functioning System (DKEFS) Color-Word Interference Test (CWIT) is commonly used to clinically assess cognitive set shifting. An atypical pattern of performance has been observed on the CWIT; a subset of individuals perform faster, with equal or fewer errors, on the more difficult inhibition/switching than the inhibition trial. This study seeks to explore the cognitive underpinnings of this atypical pattern. It is hypothesized that atypical patterns on CWIT will be associated with better performance on underlying cognitive measures of attention, working memory, and learning when compared to typical CWIT patterns. Records from 239 clinical referrals (age: M = 68.09 years, SD = 10.62; education: M = 14.87 years, SD = 2.73) seen for a neuropsychological evaluation as part of diagnostic work up in an outpatient dementia and movement disorders clinic were sampled. The standard battery of tests included measures of attention, learning, fluency, executive functioning, and working memory. Analyses of variance (ANOVAs) were conducted to compare the cognitive performance of those with typical versus atypical CWIT patterns. An atypical pattern of performance was confirmed in 23% of our sample. Analyses revealed a significant group difference in acquisition of information on both nonverbal (Brief Visuospatial Memory Test-Revised, BVMT-R total recall), F(1, 213) = 16.61, p < .001, and verbal (Hopkins Verbal Learning Test-Revised, HVLT-R total recall) learning tasks, F(1, 181) = 6.43, p < .01, and semantic fluency (Animal Naming), F(1, 232) = 7.57, p = .006, with the atypical group performing better on each task. Effect sizes were larger for nonverbal (Cohen's d = 0.66) than verbal learning (Cohen's d = 0.47) and semantic fluency (Cohen's d = 0.43). Individuals demonstrating an atypical pattern of performance on the CWIT inhibition/switching trial also demonstrated relative strengths in semantic fluency and learning.
Adapting Semantic Natural Language Processing Technology to Address Information Overload in Influenza Epidemic Management

PubMed Central

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

2013-01-01

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

PubMed Central

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

2009-01-01

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

ERIC Educational Resources Information Center

Simpson, Andrew; Riggs, Kevin J.

2005-01-01

In a recent study Diamond, Kirkham and Amso (2002) obtained evidence consistent with the claim that the day-night task requires inhibition because the picture and its corresponding conflicting response are semantically related. In their study children responded more accurately in a dog-pig condition (see /day picture/ say "dog"; see /night…
Treatment Integrity of Elaborated Semantic Feature Analysis Aphasia Therapy Delivered in Individual and Group Settings

ERIC Educational Resources Information Center

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

2017-01-01

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

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

ERIC Educational Resources Information Center

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

2011-01-01

Most theories of semantic memory characterize knowledge of a given object as comprising a set of semantic features. But how does conceptual activation of these features proceed during object identification? We present the results of a pair of experiments that demonstrate that object recognition is a dynamically unfolding process in which function…
Atypical Neurophysiology Underlying Episodic and Semantic Memory in Adults with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Massand, Esha; Bowler, Dermot M.

2015-01-01

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

ERIC Educational Resources Information Center

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

2012-01-01

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

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…
Clustering and switching processes in semantic verbal fluency in the course of Alzheimer's disease subjects: results from the PAQUID longitudinal study.

PubMed

Raoux, Nadine; Amieva, Hélène; Le Goff, Mélanie; Auriacombe, Sophie; Carcaillon, Laure; Letenneur, Luc; Dartigues, Jean-François

2008-10-01

Reduced semantic fluency performances have been reported in the preclinical phase of Alzheimer's disease (AD). To investigate the cognitive processes underlying this early deficit, this study analyzed the verbal production of predemented subjects for the animals category with the qualitative parameters related to clustering (i.e. the ability to generate words belonging to semantic subcategories of animals) and switching (i.e. the ability to shift from one subcategory to another) proposed by Troyer. This qualitative analysis was applied to the PAQUID (Personnes Agées QUID) cohort, a 17-year longitudinal population-based study. The performances on the animal verbal fluency task of 51 incident cases of possible and probable AD were analyzed at the onset of dementia, 2 years and 5 years before dementia onset. Each case was matched for age, sex and education to two control subjects leading to a sample of 153 subjects. The mean cluster size and the raw number of switches were compared in the two samples. The results revealed a significantly lower switching index in the future AD subjects than in the elderly controls including 5 years before dementia incidence. A significant decline in this parameter was evidenced all along the prodromal phase until the clinical diagnosis of dementia. In contrast, the mean cluster size could not discriminate the two groups. Therefore the results support the hypothesis that impaired shifting abilities - rather than semantic memory storage degradation - could explain the early decline in semantic fluency performance occurring in the predementia phase of AD.
Quantitative and qualitative analysis of semantic verbal fluency in patients with temporal lobe epilepsy.

PubMed

Jaimes-Bautista, A G; Rodríguez-Camacho, M; Martínez-Juárez, I E; Rodríguez-Agudelo, Y

2017-08-29

Patients with temporal lobe epilepsy (TLE) perform poorly on semantic verbal fluency (SVF) tasks. Completing these tasks successfully involves multiple cognitive processes simultaneously. Therefore, quantitative analysis of SVF (number of correct words in one minute), conducted in most studies, has been found to be insufficient to identify cognitive dysfunction underlying SVF difficulties in TLE. To determine whether a sample of patients with TLE had SVF difficulties compared with a control group (CG), and to identify the cognitive components associated with SVF difficulties using quantitative and qualitative analysis. SVF was evaluated in 25 patients with TLE and 24 healthy controls; the semantic verbal fluency test included 5 semantic categories: animals, fruits, occupations, countries, and verbs. All 5 categories were analysed quantitatively (number of correct words per minute and interval of execution: 0-15, 16-30, 31-45, and 46-60seconds); the categories animals and fruits were also analysed qualitatively (clusters, cluster size, switches, perseverations, and intrusions). Patients generated fewer words for all categories and intervals and fewer clusters and switches for animals and fruits than the CG (P<.01). Differences between groups were not significant in terms of cluster size and number of intrusions and perseverations (P>.05). Our results suggest an association between SVF difficulties in TLE and difficulty activating semantic networks, impaired strategic search, and poor cognitive flexibility. Attention, inhibition, and working memory are preserved in these patients. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.
Factors responsible for performance on the day-night task: response set or semantics?

PubMed

Simpson, Andrew; Riggs, Kevin J

2005-07-01

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

PubMed Central

Robson, Holly; Lambon Ralph, Matthew A.; Jefferies, Elizabeth

2015-01-01

Comprehension deficits are common in stroke aphasia, including in cases with (i) semantic aphasia, characterized by poor executive control of semantic processing across verbal and non-verbal modalities; and (ii) Wernicke’s aphasia, associated with poor auditory–verbal comprehension and repetition, plus fluent speech with jargon. However, the varieties of these comprehension problems, and their underlying causes, are not well understood. Both patient groups exhibit some type of semantic ‘access’ deficit, as opposed to the ‘storage’ deficits observed in semantic dementia. Nevertheless, existing descriptions suggest that these patients might have different varieties of ‘access’ impairment—related to difficulty resolving competition (in semantic aphasia) versus initial activation of concepts from sensory inputs (in Wernicke’s aphasia). We used a case series design to compare patients with Wernicke’s aphasia and those with semantic aphasia on Warrington’s paradigmatic assessment of semantic ‘access’ deficits. In these verbal and non-verbal matching tasks, a small set of semantically-related items are repeatedly presented over several cycles so that the target on one trial becomes a distractor on another (building up interference and eliciting semantic ‘blocking’ effects). Patients with Wernicke’s aphasia and semantic aphasia were distinguished according to lesion location in the temporal cortex, but in each group, some individuals had additional prefrontal damage. Both of these aspects of lesion variability—one that mapped onto classical ‘syndromes’ and one that did not—predicted aspects of the semantic ‘access’ deficit. Both semantic aphasia and Wernicke’s aphasia cases showed multimodal semantic impairment, although as expected, the Wernicke’s aphasia group showed greater deficits on auditory-verbal than picture judgements. Distribution of damage in the temporal lobe was crucial for predicting the initially ‘beneficial’ effects of stimulus repetition: cases with Wernicke’s aphasia showed initial improvement with repetition of words and pictures, while in semantic aphasia, semantic access was initially good but declined in the face of competition from previous targets. Prefrontal damage predicted the ‘harmful’ effects of repetition: the ability to reselect both word and picture targets in the face of mounting competition was linked to left prefrontal damage in both groups. Therefore, patients with semantic aphasia and Wernicke’s aphasia have partially distinct impairment of semantic ‘access’ but, across these syndromes, prefrontal lesions produce declining comprehension with repetition in both verbal and non-verbal tasks. PMID:26454668
Varieties of semantic 'access' deficit in Wernicke's aphasia and semantic aphasia.

PubMed

Thompson, Hannah E; Robson, Holly; Lambon Ralph, Matthew A; Jefferies, Elizabeth

2015-12-01

Comprehension deficits are common in stroke aphasia, including in cases with (i) semantic aphasia, characterized by poor executive control of semantic processing across verbal and non-verbal modalities; and (ii) Wernicke's aphasia, associated with poor auditory-verbal comprehension and repetition, plus fluent speech with jargon. However, the varieties of these comprehension problems, and their underlying causes, are not well understood. Both patient groups exhibit some type of semantic 'access' deficit, as opposed to the 'storage' deficits observed in semantic dementia. Nevertheless, existing descriptions suggest that these patients might have different varieties of 'access' impairment-related to difficulty resolving competition (in semantic aphasia) versus initial activation of concepts from sensory inputs (in Wernicke's aphasia). We used a case series design to compare patients with Wernicke's aphasia and those with semantic aphasia on Warrington's paradigmatic assessment of semantic 'access' deficits. In these verbal and non-verbal matching tasks, a small set of semantically-related items are repeatedly presented over several cycles so that the target on one trial becomes a distractor on another (building up interference and eliciting semantic 'blocking' effects). Patients with Wernicke's aphasia and semantic aphasia were distinguished according to lesion location in the temporal cortex, but in each group, some individuals had additional prefrontal damage. Both of these aspects of lesion variability-one that mapped onto classical 'syndromes' and one that did not-predicted aspects of the semantic 'access' deficit. Both semantic aphasia and Wernicke's aphasia cases showed multimodal semantic impairment, although as expected, the Wernicke's aphasia group showed greater deficits on auditory-verbal than picture judgements. Distribution of damage in the temporal lobe was crucial for predicting the initially 'beneficial' effects of stimulus repetition: cases with Wernicke's aphasia showed initial improvement with repetition of words and pictures, while in semantic aphasia, semantic access was initially good but declined in the face of competition from previous targets. Prefrontal damage predicted the 'harmful' effects of repetition: the ability to reselect both word and picture targets in the face of mounting competition was linked to left prefrontal damage in both groups. Therefore, patients with semantic aphasia and Wernicke's aphasia have partially distinct impairment of semantic 'access' but, across these syndromes, prefrontal lesions produce declining comprehension with repetition in both verbal and non-verbal tasks. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.
Learning semantic histopathological representation for basal cell carcinoma classification

NASA Astrophysics Data System (ADS)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

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

PubMed Central

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

2011-01-01

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

NASA Astrophysics Data System (ADS)

Banda, Gourinath; Gallagher, John P.

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

PubMed

Vasuki, Vidya; Cohen, Trevor

2010-10-01

The rapid growth of biomedical literature is evident in the increasing size of the MEDLINE research database. Medical Subject Headings (MeSH), a controlled set of keywords, are used to index all the citations contained in the database to facilitate search and retrieval. This volume of citations calls for efficient tools to assist indexers at the US National Library of Medicine (NLM). Currently, the Medical Text Indexer (MTI) system provides assistance by recommending MeSH terms based on the title and abstract of an article using a combination of distributional and vocabulary-based methods. In this paper, we evaluate a novel approach toward indexer assistance by using nearest neighbor classification in combination with Reflective Random Indexing (RRI), a scalable alternative to the established methods of distributional semantics. On a test set provided by the NLM, our approach significantly outperforms the MTI system, suggesting that the RRI approach would make a useful addition to the current methodologies.
Semantic contribution to verbal short-term memory: are pleasant words easier to remember than neutral words in serial recall and serial recognition?

PubMed

Monnier, Catherine; Syssau, Arielle

2008-01-01

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

PubMed

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

2013-09-01

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

PubMed

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

2006-01-01

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

NASA Technical Reports Server (NTRS)

Luettgen, Gerald; Bhat, Girish; Cleaveland, Rance

1999-01-01

This paper investigates implementations of process algebras which are suitable for modeling concurrent real-time systems. It suggests an approach for efficiently implementing real-time semantics using dynamic priorities. For this purpose a proces algebra with dynamic priority is defined, whose semantics corresponds one-to-one to traditional real-time semantics. The advantage of the dynamic-priority approach is that it drastically reduces the state-space sizes of the systems in question while preserving all properties of their functional and real-time behavior. The utility of the technique is demonstrated by a case study which deals with the formal modeling and verification of the SCSI-2 bus-protocol. The case study is carried out in the Concurrency Workbench of North Carolina, an automated verification tool in which the process algebra with dynamic priority is implemented. It turns out that the state space of the bus-protocol model is about an order of magnitude smaller than the one resulting from real-time semantics. The accuracy of the model is proved by applying model checking for verifying several mandatory properties of the bus protocol.
The representation of semantic knowledge in a child with Williams syndrome.

PubMed

Robinson, Sally J; Temple, Christine M

2009-05-01

This study investigated whether there are distinct types of semantic knowledge with distinct representational bases during development. The representation of semantic knowledge in a teenage child (S.T.) with Williams syndrome was explored for the categories of animals, fruit, and vegetables, manipulable objects, and nonmanipulable objects. S.T.'s lexical stores were of a normal size but the volume of "sensory feature" semantic knowledge she generated in oral descriptions was reduced. In visual recognition decisions, S.T. made more false positives to nonitems than did controls. Although overall naming of pictures was unimpaired, S.T. exhibited a category-specific anomia for nonmanipulable objects and impaired naming of visual-feature descriptions of animals. S.T.'s performance was interpreted as reflecting the impaired integration of distinctive features from perceptual input, which may impact upon nonmanipulable objects to a greater extent than the other knowledge categories. Performance was used to inform adult-based models of semantic representation, with category structure proposed to emerge due to differing degrees of dependency upon underlying knowledge types, feature correlations, and the acquisition of information from modality-specific processing modules.
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

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

PubMed

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

2017-10-01

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

PubMed

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

2018-06-01

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

PubMed Central

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

2016-01-01

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

NASA Technical Reports Server (NTRS)

Berndt, Sarah; Doane, Mike

2012-01-01

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

PubMed

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

2016-09-06

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

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
vSPARQL: A View Definition Language for the Semantic Web

PubMed Central

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

2010-01-01

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

PubMed Central

2011-01-01

Background A critical aspect of the NIH Translational Research roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the provenance metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists. Results We identify a common set of challenges in managing provenance information across the pre-publication and post-publication phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata: (a) Provenance collection - during data generation (b) Provenance representation - to support interoperability, reasoning, and incorporate domain semantics (c) Provenance storage and propagation - to allow efficient storage and seamless propagation of provenance as the data is transferred across applications (d) Provenance query - to support queries with increasing complexity over large data size and also support knowledge discovery applications We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for Trypanosoma cruzi (T.cruzi SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness. Conclusions The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis. PMID:22126369
[Semantic verbal fluency of animals in amnesia-type mild cognitive impairment].

PubMed

Lopez-Higes, Ramón; Prados, José M; del Rio, David; Galindo-Fuentes, Marta; Reinoso, Ana Isabel; Lozano-Ibanez, Montserrat

2014-06-01

The quantitative and qualitative analysis of the semantic verbal fluency task has revealed that people with dementia produced fewer words and smaller semantic clustering than people without dementia. However, in people with amnestic mild cognitive impairment (aMCI), research has shown conflicting results regarding the amount and number of semantic clusters that are made. The aim of this study was to provide new data to this controversial issue. Twenty-two older adults diagnosed with aMCI (8 men and 14 women) and 43 older adults (7 men and 36 women) with normal cognitive functioning that served as control group, participated in this study. All patients were evaluated at the Center for Prevention of Cognitive Decline of Madrid (Spain), completing the verbal fluency test (animals) besides other neuropsychological tests. As expected, animal production was lower in the aMCI group than in the control group, but no differences were observed either in the average size of the semantic clusters or the number of switches between them. The results are consistent with previous research suggesting aMCI is not only characterized by episodic memory and working memory deficits. Semantic memory decline is also present. However, the data do not clarify how strategic executive processes are involved, as seems to be in Alzheimer's disease.
A normative set of 98 pairs of nonsensical pictures (droodles).

PubMed

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

2010-08-01

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

NASA Astrophysics Data System (ADS)

Huffer, E.; Gleason, J. L.

2015-12-01

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

PubMed Central

Jackson, Alice F.; Bolger, Donald J.

2014-01-01

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

PubMed

Jackson, Alice F; Bolger, Donald J

2014-01-01

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

PubMed

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

2002-07-01

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

PubMed

Sogancioglu, Gizem; Öztürk, Hakime; Özgür, Arzucan

2017-07-15

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

PubMed Central

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

2014-01-01

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

PubMed

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

2016-11-01

Researchers have been at odds on whether affective or semantic priming is faster or stronger. The present study selects a series of facial expression photos and words, which have definite emotional meaning or gender meaning, to set up experiment including both affective and semantic priming. The intensity of emotion and gender information in the prime as well as the strength of emotional or semantic (in gender) relationship between the prime and the target is matched. Three groups of participants are employed separately in our experiment varied with stimulus onset asynchrony (SOA) as 50, 250 or 500 ms. The results show that the difference between two types of priming effect is revealed when the SOA is at 50 ms, in which the affective priming effect is presented when the prime has negative emotion. It indicates that SOA can affect the comparison between the affective and semantic priming, and the former takes the priority in the automatic processing level.
ER2OWL: Generating OWL Ontology from ER Diagram

NASA Astrophysics Data System (ADS)

Fahad, Muhammad

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

PubMed

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

2014-01-01

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

PubMed Central

2011-01-01

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

Improving life sciences information retrieval using semantic web technology.

PubMed

Quan, Dennis

2007-05-01

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

PubMed Central

2011-01-01

Background The Comparative Data Analysis Ontology (CDAO) is an ontology developed, as part of the EvoInfo and EvoIO groups supported by the National Evolutionary Synthesis Center, to provide semantic descriptions of data and transformations commonly found in the domain of phylogenetic analysis. The core concepts of the ontology enable the description of phylogenetic trees and associated character data matrices. Results Using CDAO as the semantic back-end, we developed a triple-store, named CDAO-Store. CDAO-Store is a RDF-based store of phylogenetic data, including a complete import of TreeBASE. CDAO-Store provides a programmatic interface, in the form of web services, and a web-based front-end, to perform both user-defined as well as domain-specific queries; domain-specific queries include search for nearest common ancestors, minimum spanning clades, filter multiple trees in the store by size, author, taxa, tree identifier, algorithm or method. In addition, CDAO-Store provides a visualization front-end, called CDAO-Explorer, which can be used to view both character data matrices and trees extracted from the CDAO-Store. CDAO-Store provides import capabilities, enabling the addition of new data to the triple-store; files in PHYLIP, MEGA, nexml, and NEXUS formats can be imported and their CDAO representations added to the triple-store. Conclusions CDAO-Store is made up of a versatile and integrated set of tools to support phylogenetic analysis. To the best of our knowledge, CDAO-Store is the first semantically-aware repository of phylogenetic data with domain-specific querying capabilities. The portal to CDAO-Store is available at http://www.cs.nmsu.edu/~cdaostore. PMID:21496247
CDAO-store: ontology-driven data integration for phylogenetic analysis.

PubMed

Chisham, Brandon; Wright, Ben; Le, Trung; Son, Tran Cao; Pontelli, Enrico

2011-04-15

The Comparative Data Analysis Ontology (CDAO) is an ontology developed, as part of the EvoInfo and EvoIO groups supported by the National Evolutionary Synthesis Center, to provide semantic descriptions of data and transformations commonly found in the domain of phylogenetic analysis. The core concepts of the ontology enable the description of phylogenetic trees and associated character data matrices. Using CDAO as the semantic back-end, we developed a triple-store, named CDAO-Store. CDAO-Store is a RDF-based store of phylogenetic data, including a complete import of TreeBASE. CDAO-Store provides a programmatic interface, in the form of web services, and a web-based front-end, to perform both user-defined as well as domain-specific queries; domain-specific queries include search for nearest common ancestors, minimum spanning clades, filter multiple trees in the store by size, author, taxa, tree identifier, algorithm or method. In addition, CDAO-Store provides a visualization front-end, called CDAO-Explorer, which can be used to view both character data matrices and trees extracted from the CDAO-Store. CDAO-Store provides import capabilities, enabling the addition of new data to the triple-store; files in PHYLIP, MEGA, nexml, and NEXUS formats can be imported and their CDAO representations added to the triple-store. CDAO-Store is made up of a versatile and integrated set of tools to support phylogenetic analysis. To the best of our knowledge, CDAO-Store is the first semantically-aware repository of phylogenetic data with domain-specific querying capabilities. The portal to CDAO-Store is available at http://www.cs.nmsu.edu/~cdaostore.
Preliminary Development of a Multidimensional Semantic Patient Experience Measurement Questionnaire.

PubMed

Kleiss, James A

2016-10-01

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

NASA Astrophysics Data System (ADS)

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

2015-11-01

Semantic heterogeneity remains a barrier to data comparability and standardisation of results in different fields of spatial research. Because of its thematic complexity, differing acquisition methods and national nomenclatures, interoperability of biodiversity monitoring information is especially difficult. Since data collection methods and interpretation manuals broadly vary there is a need for automatised, objective methodologies for the generation of comparable data-sets. Ontology-based applications offer vast opportunities in data management and standardisation. This study examines two data-sets of protected heathlands in Germany and Belgium which are based on remote sensing image classification and semantically formalised in an OWL2 ontology. The proposed methodology uses semantic relations of the two data-sets, which are (semi-)automatically derived from remote sensing imagery, to generate objective and comparable information about the status of protected areas by utilising kernel-based spatial reclassification. This automatised method suggests a generalisation approach, which is able to generate delineation of Special Areas of Conservation (SAC) of the European biodiversity Natura 2000 network. Furthermore, it is able to transfer generalisation rules between areas surveyed with varying acquisition methods in different countries by taking into account automated inference of the underlying semantics. The generalisation results were compared with the manual delineation of terrestrial monitoring. For the different habitats in the two sites an accuracy of above 70% was detected. However, it has to be highlighted that the delineation of the ground-truth data inherits a high degree of uncertainty, which is discussed in this study.
45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... code sets inherent to a transaction, and not related to the format of the transaction. Data elements... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...
45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... sets inherent to a transaction, and not related to the format of the transaction. Data elements that... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...
45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... sets inherent to a transaction, and not related to the format of the transaction. Data elements that... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...
45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... sets inherent to a transaction, and not related to the format of the transaction. Data elements that... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...
45 CFR 162.103 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... definitions apply: Code set means any set of codes used to encode data elements, such as tables of terms... code sets inherent to a transaction, and not related to the format of the transaction. Data elements... information in a transaction. Data set means a semantically meaningful unit of information exchanged between...
Parallel State Space Construction for a Model Checking Based on Maximality Semantics

NASA Astrophysics Data System (ADS)

El Abidine Bouneb, Zine; Saīdouni, Djamel Eddine

2009-03-01

The main limiting factor of the model checker integrated in the concurrency verification environment FOCOVE [1, 2], which use the maximality based labeled transition system (noted MLTS) as a true concurrency model[3, 4], is currently the amount of available physical memory. Many techniques have been developed to reduce the size of a state space. An interesting technique among them is the alpha equivalence reduction. Distributed memory execution environment offers yet another choice. The main contribution of the paper is to show that the parallel state space construction algorithm proposed in [5], which is based on interleaving semantics using LTS as semantic model, may be adapted easily to the distributed implementation of the alpha equivalence reduction for the maximality based labeled transition systems.
Mapping Agricultural Fields in Sub-Saharan Africa with a Computer Vision Approach

NASA Astrophysics Data System (ADS)

Debats, S. R.; Luo, D.; Estes, L. D.; Fuchs, T.; Caylor, K. K.

2014-12-01

Sub-Saharan Africa is an important focus for food security research, because it is experiencing unprecedented population growth, agricultural activities are largely dominated by smallholder production, and the region is already home to 25% of the world's undernourished. One of the greatest challenges to monitoring and improving food security in this region is obtaining an accurate accounting of the spatial distribution of agriculture. Households are the primary units of agricultural production in smallholder communities and typically rely on small fields of less than 2 hectares. Field sizes are directly related to household crop productivity, management choices, and adoption of new technologies. As population and agriculture expand, it becomes increasingly important to understand both the distribution of field sizes as well as how agricultural communities are spatially embedded in the landscape. In addition, household surveys, a common tool for tracking agricultural productivity in Sub-Saharan Africa, would greatly benefit from spatially explicit accounting of fields. Current gridded land cover data sets do not provide information on individual agricultural fields or the distribution of field sizes. Therefore, we employ cutting edge approaches from the field of computer vision to map fields across Sub-Saharan Africa, including semantic segmentation, discriminative classifiers, and automatic feature selection. Our approach aims to not only improve the binary classification accuracy of cropland, but also to isolate distinct fields, thereby capturing crucial information on size and geometry. Our research focuses on the development of descriptive features across scales to increase the accuracy and geographic range of our computer vision algorithm. Relevant data sets include high-resolution remote sensing imagery and Landsat (30-m) multi-spectral imagery. Training data for field boundaries is derived from hand-digitized data sets as well as crowdsourcing.
The modulatory influence of the functional COMT Val158Met polymorphism on lexical decisions and semantic priming.

PubMed

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

2009-01-01

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

PubMed

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

2006-01-01

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

ERIC Educational Resources Information Center

Ronnlund, Michael; Nilsson, Lars-Goran.

2009-01-01

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

PubMed

Cruz Morales, John Alexander; Zilber, Boris

2015-08-06

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

ERIC Educational Resources Information Center

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

2012-01-01

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

PubMed Central

Recchia, Gabriel; Jones, Michael N.

2012-01-01

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

NASA Astrophysics Data System (ADS)

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

2010-01-01

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

NASA Technical Reports Server (NTRS)

Berndt, Sarah Ann

2011-01-01

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

Spatiotemporal-Thematic Data Processing for the Semantic Web

NASA Astrophysics Data System (ADS)

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

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

PubMed Central

González-Castro, Lorena; Carta, Claudio; van der Horst, Eelke; Lopes, Pedro; Kaliyaperumal, Rajaram; Thompson, Mark; Thompson, Rachel; Queralt-Rosinach, Núria; Lopez, Estrella; Wood, Libby; Robertson, Agata; Lamanna, Claudia; Gilling, Mette; Orth, Michael; Merino-Martinez, Roxana; Taruscio, Domenica; Lochmüller, Hanns

2017-01-01

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries. PMID:29214177
Categorizing words through semantic memory navigation

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, J.; Arenas, A.

2010-03-01

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

PubMed

Sernadela, Pedro; González-Castro, Lorena; Carta, Claudio; van der Horst, Eelke; Lopes, Pedro; Kaliyaperumal, Rajaram; Thompson, Mark; Thompson, Rachel; Queralt-Rosinach, Núria; Lopez, Estrella; Wood, Libby; Robertson, Agata; Lamanna, Claudia; Gilling, Mette; Orth, Michael; Merino-Martinez, Roxana; Posada, Manuel; Taruscio, Domenica; Lochmüller, Hanns; Robinson, Peter; Roos, Marco; Oliveira, José Luís

2017-01-01

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.
Mind-Sets for a Happier Life.

ERIC Educational Resources Information Center

Webber, Jo

1997-01-01

Discusses how irrational beliefs can be disputed and replaced with alternative, healthier mind-sets. Focuses on cognitive restructuring theory, changing unhealthy thoughts, disputation, semantics, and adopting alternative mind-sets, such as life is bizarre and funny. Claims that using words that indicate preferences and possibilities can help…
How to Constrain and Maintain a Lexicon for the Treatment of Progressive Semantic Naming Deficits: Principles of Item Selection for Formal Semantic Therapy

PubMed Central

Reilly, Jamie

2015-01-01

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

PubMed Central

Danguecan, Ashley N.; Buchanan, Lori

2016-01-01

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

PubMed

Danguecan, Ashley N; Buchanan, Lori

2016-01-01

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

PubMed

Chow, Michael; Macnamara, Brooke N; Conway, Andrew R A

2016-08-01

In a series of four experiments, we explored what conditions are sufficient to produce a phonological similarity facilitation effect in working memory span tasks. By using the same set of memoranda, but differing the secondary-task requirements across experiments, we showed that a phonological similarity facilitation effect is dependent upon the semantic relationship between the memoranda and the secondary-task stimuli, and is robust to changes in the representation, ordering, and pool size of the secondary-task stimuli. These findings are consistent with interference accounts of memory (Brown, Neath, & Chater, Psychological Review, 114, 539-576, 2007; Oberauer, Lewandowsky, Farrell, Jarrold, & Greaves, Psychonomic Bulletin & Review, 19, 779-819, 2012), whereby rhyming stimuli provide a form of categorical similarity that allows distractors to be excluded from retrieval at recall.
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.
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.
Parafoveal processing in silent and oral reading: Reading mode influences the relative weighting of phonological and semantic information in Chinese.

PubMed

Pan, Jinger; Laubrock, Jochen; Yan, Ming

2016-08-01

We examined how reading mode (i.e., silent vs. oral reading) influences parafoveal semantic and phonological processing during the reading of Chinese sentences, using the gaze-contingent boundary paradigm. In silent reading, we found in 2 experiments that reading times on target words were shortened with semantic previews in early and late processing, whereas phonological preview effects mainly occurred in gaze duration or second-pass reading. In contrast, results showed that phonological preview information is obtained early on in oral reading. Strikingly, in oral reading, we observed a semantic preview cost on the target word in Experiment 1 and a decrease in the effect size of preview benefit from first- to second-pass measures in Experiment 2, which we hypothesize to result from increased preview duration. Taken together, our results indicate that parafoveal semantic information can be obtained irrespective of reading mode, whereas readers more efficiently process parafoveal phonological information in oral reading. We discuss implications for notions of information processing priority and saccade generation during silent and oral reading. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
vSPARQL: a view definition language for the semantic web.

PubMed

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

2011-02-01

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

NASA Astrophysics Data System (ADS)

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

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

PubMed

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

2016-02-03

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

PubMed Central

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

2016-01-01

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

PubMed Central

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

2009-01-01

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

PubMed

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

2013-01-01

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

PubMed Central

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

2013-01-01

Mappings established between Knowledge Organization Systems (KOS) increase semantic interoperability between biomedical information systems. However, biomedical knowledge is highly dynamic and changes affecting KOS entities can potentially invalidate part or the totality of existing mappings. Understanding how mappings evolve and what the impacts of KOS evolution on mappings are is therefore crucial for the definition of an automatic approach to maintain mappings valid and up-to-date over time. In this article, we study variations of a specific KOS complex change (split) for two biomedical KOS (SNOMED CT and ICD-9-CM) through a rigorous method of investigation for identifying and refining complex changes, and for selecting representative cases. We empirically analyze and explain their influence on the evolution of associated mappings. Results point out the importance of considering various dimensions of the information described in KOS, like the semantic structure of concepts, the set of relevant information used to define the mappings and the change operations interfering with this set of information. PMID:24551341
Chemical markup, XML, and the world wide web. 6. CMLReact, an XML vocabulary for chemical reactions.

PubMed

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

2006-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

PubMed

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

2018-01-01

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

PubMed Central

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

2018-01-01

ABSTRACT The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover). PMID:29387171
Is Semantic Processing During Sentence Reading Autonomous or Controlled? Evidence from the N400 Component in a Dual Task Paradigm

PubMed Central

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

2015-01-01

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

NASA Astrophysics Data System (ADS)

Piprani, Baba

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

PubMed

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

1998-07-01

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

NASA Astrophysics Data System (ADS)

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

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

PubMed

Jing, X; Cimino, J J

2014-01-01

Graphical displays can make data more understandable; however, large graphs can challenge human comprehension. We have previously described a filtering method to provide high-level summary views of large data sets. In this paper we demonstrate our method for setting and selecting thresholds to limit graph size while retaining important information by applying it to large single and paired data sets, taken from patient and bibliographic databases. Four case studies are used to illustrate our method. The data are either patient discharge diagnoses (coded using the International Classification of Diseases, Clinical Modifications [ICD9-CM]) or Medline citations (coded using the Medical Subject Headings [MeSH]). We use combinations of different thresholds to obtain filtered graphs for detailed analysis. The thresholds setting and selection, such as thresholds for node counts, class counts, ratio values, p values (for diff data sets), and percentiles of selected class count thresholds, are demonstrated with details in case studies. The main steps include: data preparation, data manipulation, computation, and threshold selection and visualization. We also describe the data models for different types of thresholds and the considerations for thresholds selection. The filtered graphs are 1%-3% of the size of the original graphs. For our case studies, the graphs provide 1) the most heavily used ICD9-CM codes, 2) the codes with most patients in a research hospital in 2011, 3) a profile of publications on "heavily represented topics" in MEDLINE in 2011, and 4) validated knowledge about adverse effects of the medication of rosiglitazone and new interesting areas in the ICD9-CM hierarchy associated with patients taking the medication of pioglitazone. Our filtering method reduces large graphs to a manageable size by removing relatively unimportant nodes. The graphical method provides summary views based on computation of usage frequency and semantic context of hierarchical terminology. The method is applicable to large data sets (such as a hundred thousand records or more) and can be used to generate new hypotheses from data sets coded with hierarchical terminologies.
What exactly do numbers mean?

PubMed Central

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

2014-01-01

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

DOE PAGES

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

2014-10-25

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

PubMed Central

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

2016-01-01

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

PubMed

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

2016-12-23

Measures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem. We propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms. The proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/ .
CUILESS2016: a clinical corpus applying compositional normalization of text mentions.

PubMed

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

2018-01-10

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

PubMed

Krueger, Robert; Thom, Dennis; Ertl, Thomas

2015-08-01

In recent years, many approaches have been developed that efficiently and effectively visualize movement data, e.g., by providing suitable aggregation strategies to reduce visual clutter. Analysts can use them to identify distinct movement patterns, such as trajectories with similar direction, form, length, and speed. However, less effort has been spent on finding the semantics behind movements, i.e. why somebody or something is moving. This can be of great value for different applications, such as product usage and consumer analysis, to better understand urban dynamics, and to improve situational awareness. Unfortunately, semantic information often gets lost when data is recorded. Thus, we suggest to enrich trajectory data with POI information using social media services and show how semantic insights can be gained. Furthermore, we show how to handle semantic uncertainties in time and space, which result from noisy, unprecise, and missing data, by introducing a POI decision model in combination with highly interactive visualizations. Finally, we evaluate our approach with two case studies on a large electric scooter data set and test our model on data with known ground truth.
A suffix arrays based approach to semantic search in P2P systems

NASA Astrophysics Data System (ADS)

Shi, Qingwei; Zhao, Zheng; Bao, Hu

2007-09-01

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

PubMed

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

2014-01-01

The Semantic Web is an effort to add semantics, or "meaning," to empower automated searching and processing of Web-based information. The overarching goal of the Semantic Web is to enable users to more easily find, share, and combine information. Critical to this vision are knowledge models called ontologies, which define a set of concepts and formalize the relations between them. Ontologies have been developed to manage and exploit the large and rapidly growing volume of information in biomedical domains. In diagnostic radiology, lists of differential diagnoses of imaging observations, called gamuts, provide an important source of knowledge. The Radiology Gamuts Ontology (RGO) is a formal knowledge model of differential diagnoses in radiology that includes 1674 differential diagnoses, 19,017 terms, and 52,976 links between terms. Its knowledge is used to provide an interactive, freely available online reference of radiology gamuts ( www.gamuts.net ). A Web service allows its content to be discovered and consumed by other information systems. The RGO integrates radiologic knowledge with other biomedical ontologies as part of the Semantic Web. © RSNA, 2014.
To ontologise or not to ontologise: An information model for a geospatial knowledge infrastructure

NASA Astrophysics Data System (ADS)

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

2012-08-01

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

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

Morari, Alessandro; Castellana, Vito G.; Villa, Oreste

In this paper we present SGEM, a full software system for accelerating large-scale semantic graph databases on commodity clusters. Unlike current approaches, SGEM addresses semantic graph databases by only employing graph methods at all the levels of the stack. On one hand, this allows exploiting the space efficiency of graph data structures and the inherent parallelism of graph algorithms. These features adapt well to the increasing system memory and core counts of modern commodity clusters. On the other hand, however, these systems are optimized for regular computation and batched data transfers, while graph methods usually are irregular and generate fine-grainedmore » data accesses with poor spatial and temporal locality. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods and a custom, multithreaded runtime system. We introduce our stack, motivate its advantages with respect to other solutions and show how we solved the challenges posed by irregular behaviors. We present the result of our software stack on the Berlin SPARQL benchmarks with datasets up to 10 billion triples (a triple corresponds to a graph edge), demonstrating scaling in dataset size and in performance as more nodes are added to the cluster.« less
Re-examination of Chinese semantic processing and syntactic processing: evidence from conventional ERPs and reconstructed ERPs by residue iteration decomposition (RIDE).

PubMed

Wang, Fang; Ouyang, Guang; Zhou, Changsong; Wang, Suiping

2015-01-01

A number of studies have explored the time course of Chinese semantic and syntactic processing. However, whether syntactic processing occurs earlier than semantics during Chinese sentence reading is still under debate. To further explore this issue, an event-related potentials (ERPs) experiment was conducted on 21 native Chinese speakers who read individually-presented Chinese simple sentences (NP1+VP+NP2) word-by-word for comprehension and made semantic plausibility judgments. The transitivity of the verbs was manipulated to form three types of stimuli: congruent sentences (CON), sentences with a semantically violated NP2 following a transitive verb (semantic violation, SEM), and sentences with a semantically violated NP2 following an intransitive verb (combined semantic and syntactic violation, SEM+SYN). The ERPs evoked from the target NP2 were analyzed by using the Residue Iteration Decomposition (RIDE) method to reconstruct the ERP waveform blurred by trial-to-trial variability, as well as by using the conventional ERP method based on stimulus-locked averaging. The conventional ERP analysis showed that, compared with the critical words in CON, those in SEM and SEM+SYN elicited an N400-P600 biphasic pattern. The N400 effects in both violation conditions were of similar size and distribution, but the P600 in SEM+SYN was bigger than that in SEM. Compared with the conventional ERP analysis, RIDE analysis revealed a larger N400 effect and an earlier P600 effect (in the time window of 500-800 ms instead of 570-810ms). Overall, the combination of conventional ERP analysis and the RIDE method for compensating for trial-to-trial variability confirmed the non-significant difference between SEM and SEM+SYN in the earlier N400 time window. Converging with previous findings on other Chinese structures, the current study provides further precise evidence that syntactic processing in Chinese does not occur earlier than semantic processing.

The role of the left anterior temporal lobe in semantic composition vs. semantic memory.

PubMed

Westerlund, Masha; Pylkkänen, Liina

2014-05-01

The left anterior temporal lobe (LATL) is robustly implicated in semantic processing by a growing body of literature. However, these results have emerged from two distinct bodies of work, addressing two different processing levels. On the one hand, the LATL has been characterized as a 'semantic hub׳ that binds features of concepts across a distributed network, based on results from semantic dementia and hemodynamic findings on the categorization of specific compared to basic exemplars. On the other, the LATL has been implicated in combinatorial operations in language, as shown by increased activity in this region associated with the processing of sentences and of basic phrases. The present work aimed to reconcile these two literatures by independently manipulating combination and concept specificity within a minimal MEG paradigm. Participants viewed simple nouns that denoted either low specificity (fish) or high specificity categories (trout) presented in either combinatorial (spotted fish/trout) or non-combinatorial contexts (xhsl fish/trout). By combining these paradigms from the two literatures, we directly compared the engagement of the LATL in semantic memory vs. semantic composition. Our results indicate that although noun specificity subtly modulates the LATL activity elicited by single nouns, it most robustly affects the size of the composition effect when these nouns are adjectivally modified, with low specificity nouns eliciting a much larger effect. We conclude that these findings are compatible with an account in which the specificity and composition effects arise from a shared mechanism of meaning specification. Copyright © 2014 Elsevier Ltd. All rights reserved.
Remembering the Important Things: Semantic Importance in Stream Reasoning

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

Yan, Rui; Greaves, Mark T.; Smith, William P.

Reasoning and querying over data streams rely on the abil- ity to deliver a sequence of stream snapshots to the processing algo- rithms. These snapshots are typically provided using windows as views into streams and associated window management strategies. Generally, the goal of any window management strategy is to preserve the most im- portant data in the current window and preferentially evict the rest, so that the retained data can continue to be exploited. A simple timestamp- based strategy is rst-in-rst-out (FIFO), in which items are replaced in strict order of arrival. All timestamp-based strategies implicitly assume that a temporalmore » ordering reliably re ects importance to the processing task at hand, and thus that window management using timestamps will maximize the ability of the processing algorithms to deliver accurate interpretations of the stream. In this work, we explore a general no- tion of semantic importance that can be used for window management for streams of RDF data using semantically-aware processing algorithms like deduction or semantic query. Semantic importance exploits the infor- mation carried in RDF and surrounding ontologies for ranking window data in terms of its likely contribution to the processing algorithms. We explore the general semantic categories of query contribution, prove- nance, and trustworthiness, as well as the contribution of domain-specic ontologies. We describe how these categories behave using several con- crete examples. Finally, we consider how a stream window management strategy based on semantic importance could improve overall processing performance, especially as available window sizes decrease.« less
ResearchEHR: use of semantic web technologies and archetypes for the description of EHRs.

PubMed

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

2010-01-01

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

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

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

2010-01-01

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

DTIC Science & Technology

1985-09-01

Minsky , Marvin and Seymour Papert, [1969], Perceptrons, MIT Press, Cam- bridge, Ma. Mitchell, T. M., [1978], "Version spaces: A candidiate elimination...with respect to a suitable set of affine transformations. This is one area in which classic perceptrons fall short [ Minsky and Papert 19691. The third...Quillian, M. Ross, [1968], "Semantic Memory" (PhD Thesis), in Semantic Infor- mation Processing, M. Minsky (ed.), MIT Press, Cambridge MA. Schlesinger, G
Semantic Web Services Challenge, Results from the First Year. Series: Semantic Web And Beyond, Volume 8.

NASA Astrophysics Data System (ADS)

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

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

PubMed

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

2017-08-01

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

PubMed

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

2013-09-01

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

PubMed Central

Prasicek, Günther; Otto, Jan-Christoph; Montgomery, David R.; Schrott, Lothar

2015-01-01

In recent years, remote sensing, morphometric analysis, and other computational concepts and tools have invigorated the field of geomorphological mapping. Automated interpretation of digital terrain data based on impartial rules holds substantial promise for large dataset processing and objective landscape classification. However, the geomorphological realm presents tremendous complexity and challenges in the translation of qualitative descriptions into geomorphometric semantics. Here, the simple, conventional distinction of V-shaped fluvial and U-shaped glacial valleys was analyzed quantitatively using multi-scale curvature and a novel morphometric variable termed Difference of Minimum Curvature (DMC). We used this automated terrain analysis approach to produce a raster map at a scale of 1:6,000,000 showing the distribution of glaciated valleys across Europe and western Asia. The data set has a cell size of 3 arc seconds and consists of more than 40 billion grid cells. Glaciated U-shaped valleys commonly associated with erosion by warm-based glaciers are abundant in the alpine regions of mid Europe and western Asia but also occur at the margins of mountain ice sheets in Scandinavia. The high-level correspondence with field mapping and the fully transferable semantics validate this approach for automated analysis of yet unexplored terrain around the globe and qualify for potential applications on other planetary bodies like Mars. PMID:27019665
ERPs, semantic processing and age.

PubMed

Miyamoto, T; Katayama, J; Koyama, T

1998-06-01

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

PubMed

Baumgaertner, Annette; Hartwigsen, Gesa; Roman Siebner, Hartwig

2013-06-01

Verbal stimuli often induce right-hemispheric activation in patients with aphasia after left-hemispheric stroke. This right-hemispheric activation is commonly attributed to functional reorganization within the language system. Yet previous evidence suggests that functional activation in right-hemispheric homologues of classic left-hemispheric language areas may partly be due to processing nonlinguistic perceptual features of verbal stimuli. We used functional MRI (fMRI) to clarify the role of the right hemisphere in the perception of nonlinguistic word features in healthy individuals. Participants made perceptual, semantic, or phonological decisions on the same set of auditorily and visually presented word stimuli. Perceptual decisions required judgements about stimulus-inherent changes in font size (visual modality) or fundamental frequency contour (auditory modality). The semantic judgement required subjects to decide whether a stimulus is natural or man-made; the phonologic decision required a decision on whether a stimulus contains two or three syllables. Compared to phonologic or semantic decision, nonlinguistic perceptual decisions resulted in a stronger right-hemispheric activation. Specifically, the right inferior frontal gyrus (IFG), an area previously suggested to support language recovery after left-hemispheric stroke, displayed modality-independent activation during perceptual processing of word stimuli. Our findings indicate that activation of the right hemisphere during language tasks may, in some instances, be driven by a "nonlinguistic perceptual processing" mode that focuses on nonlinguistic word features. This raises the possibility that stronger activation of right inferior frontal areas during language tasks in aphasic patients with left-hemispheric stroke may at least partially reflect increased attentional focus on nonlinguistic perceptual aspects of language. Copyright © 2012 Wiley Periodicals, Inc.
Content analysis of physical examination templates in electronic health records using SNOMED CT.

PubMed

Gøeg, Kirstine Rosenbeck; Chen, Rong; Højen, Anne Randorff; Elberg, Pia

2014-10-01

Most electronic health record (EHR) systems are built on proprietary information models and terminology, which makes achieving semantic interoperability a challenge. Solving interoperability problems requires well-defined standards. In contrast, the need to support clinical work practice requires a local customization of EHR systems. Consequently, contrasting goals may be evident in EHR template design because customization means that local EHR organizations can define their own templates, whereas standardization implies consensus at some level. To explore the complexity of balancing these two goals, this study analyzes the differences and similarities between templates in use today. A similarity analysis was developed on the basis of SNOMED CT. The analysis was performed on four physical examination templates from Denmark and Sweden. The semantic relationships in SNOMED CT were used to quantify similarities and differences. Moreover, the analysis used these identified similarities to investigate the common content of a physical examination template. The analysis showed that there were both similarities and differences in physical examination templates, and the size of the templates varied from 18 to 49 fields. In the SNOMED CT analysis, exact matches and terminology similarities were represented in all template pairs. The number of exact matches ranged from 7 to 24. Moreover, the number of unrelated fields differed a lot from 1/18 to 22/35. Cross-country comparisons tended to have more unrelated content than within-country comparisons. On the basis of identified similarities, it was possible to define the common content of a physical examination. Nevertheless, a complete view on the physical examination required the inclusion of both exact matches and terminology similarities. This study revealed that a core set of items representing the physical examination templates can be generated when the analysis takes into account not only exact matches but also terminology similarities. This core set of items could be a starting point for standardization and semantic interoperability. However, both unmatched terms and terminology matched terms pose a challenge for standardization. Future work will include using local templates as a point of departure in standardization to see if local requirements can be maintained in a standardized framework. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Unambiguous UML Composite Structures: The OMEGA2 Experience

NASA Astrophysics Data System (ADS)

Ober, Iulian; Dragomir, Iulia

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

PubMed

Cuzzola, John; Jovanović, Jelena; Bagheri, Ebrahim

2017-07-01

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

PubMed Central

2011-01-01

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

PubMed

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

2013-01-01

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

NASA Astrophysics Data System (ADS)

Yang, Beibei

Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the Brown corpus, or more recently, Wikipedia. This dissertation proposes two approaches to applying semantic relatedness to the problem of suggesting transfer course equivalencies. Two course descriptions are given as input to feed the proposed algorithms, which output a value that can be used to help determine if the courses are equivalent. The first proposed approach uses traditional knowledge sources such as WordNet and corpora for courses from multiple fields of study. The second approach uses Wikipedia, the openly-editable encyclopedia, and it focuses on courses from a technical field such as Computer Science. This work shows that it is promising to adapt semantic relatedness to the education field for matching equivalencies between transfer courses. A semantic relatedness measure using traditional knowledge sources such as WordNet performs relatively well on non-technical courses. However, due to the "knowledge acquisition bottleneck," such a resource is not ideal for technical courses, which use an extensive and growing set of technical terms. To address the problem, this work proposes a Wikipedia-based approach which is later shown to be more correlated to human judgment compared to previous work.
Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

PubMed Central

Waagmeester, Andra; Pico, Alexander R.

2016-01-01

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

PubMed Central

Luciano, Joanne S; Stevens, Robert D

2007-01-01

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

PubMed

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

2016-06-01

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

Towards a Framework for Developing Semantic Relatedness Reference Standards

PubMed Central

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

2010-01-01

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

PubMed

Carlesimo, Giovanni A; Bonanni, Rita; Caltagirone, Carlo

2003-05-01

This study investigated the hypothesis that brain damaged patients with memory disorder are poorer at remembering the semantic than the perceptual attributes of information. Eight patients with memory impairment of different etiology and 24 patients with chronic consequences of severe closed-head injury were compared to similarly sized age- and literacy-matched normal control groups on recognition tests for the physical aspect and the semantic identity of words and pictures lists. In order to avoid interpretative problems deriving from different absolute levels of performance, study conditions were manipulated across subjects to obtain comparable accuracy on the perceptual recognition tests in the memory disordered and control groups. The results of the Picture Recognition test were consistent with the hypothesis. Indeed, having more time for the stimulus encoding, the two memory disordered groups performed at the same level as the normal subjects on the perceptual test but significantly lower on the semantic test. Instead, on the Word Recognition test, following study condition manipulation, patients and controls performed similarly on both the perceptual and the semantic tests. These data only partially support the hypothesis of the study; rather they suggest that in memory disordered patients there is a reduction of the advantage, exhibited by normal controls, of retrieving pictures over words (picture superiority effect).
Semantic transcoding of video based on regions of interest

NASA Astrophysics Data System (ADS)

Lim, Jeongyeon; Kim, Munchurl; Kim, Jong-Nam; Kim, Kyeongsoo

2003-06-01

Traditional transcoding on multimedia has been performed from the perspectives of user terminal capabilities such as display sizes and decoding processing power, and network resources such as available network bandwidth and quality of services (QoS) etc. The adaptation (or transcoding) of multimedia contents to given such constraints has been made by frame dropping and resizing of audiovisual, as well as reduction of SNR (Signal-to-Noise Ratio) values by saving the resulting bitrates. Not only such traditional transcoding is performed from the perspective of user"s environment, but also we incorporate a method of semantic transcoding of audiovisual based on region of interest (ROI) from user"s perspective. Users can designate their interested parts in images or video so that the corresponding video contents can be adapted focused on the user"s ROI. We incorporate the MPEG-21 DIA (Digital Item Adaptation) framework in which such semantic information of the user"s ROI is represented and delivered to the content provider side as XDI (context digital item). Representation schema of our semantic information of the user"s ROI has been adopted in MPEG-21 DIA Adaptation Model. In this paper, we present the usage of semantic information of user"s ROI for transcoding and show our system implementation with experimental results.
Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

PubMed Central

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

2015-01-01

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

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

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

USGS Publications Warehouse

Varanka, Dalia E.; Caro, Holly K.

2013-01-01

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

NASA Technical Reports Server (NTRS)

Cornish, J. Robert M.; Gange, Graeme; Navas, Jorge A.; Schachte, Peter; Sondergaard, Harald; Stuckey, Peter J.

2014-01-01

We explore a transformational approach to the problem of verifying simple array-manipulating programs. Traditionally, verification of such programs requires intricate analysis machinery to reason with universally quantified statements about symbolic array segments, such as "every data item stored in the segment A[i] to A[j] is equal to the corresponding item stored in the segment B[i] to B[j]." We define a simple abstract machine which allows for set-valued variables and we show how to translate programs with array operations to array-free code for this machine. For the purpose of program analysis, the translated program remains faithful to the semantics of array manipulation. Based on our implementation in LLVM, we evaluate the approach with respect to its ability to extract useful invariants and the cost in terms of code size.
Combining semantic technologies with a content-based image retrieval system - Preliminary considerations

NASA Astrophysics Data System (ADS)

Chmiel, P.; Ganzha, M.; Jaworska, T.; Paprzycki, M.

2017-10-01

Nowadays, as a part of systematic growth of volume, and variety, of information that can be found on the Internet, we observe also dramatic increase in sizes of available image collections. There are many ways to help users browsing / selecting images of interest. One of popular approaches are Content-Based Image Retrieval (CBIR) systems, which allow users to search for images that match their interests, expressed in the form of images (query by example). However, we believe that image search and retrieval could take advantage of semantic technologies. We have decided to test this hypothesis. Specifically, on the basis of knowledge captured in the CBIR, we have developed a domain ontology of residential real estate (detached houses, in particular). This allows us to semantically represent each image (and its constitutive architectural elements) represented within the CBIR. The proposed ontology was extended to capture not only the elements resulting from image segmentation, but also "spatial relations" between them. As a result, a new approach to querying the image database (semantic querying) has materialized, thus extending capabilities of the developed system.
Toward a Geoscientific Semantic Web Based on How Geoscientists Talk Across Disciplines

NASA Astrophysics Data System (ADS)

Peckham, S. D.

2015-12-01

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

PubMed

Oberkampf, Heiner; Zillner, Sonja; Overton, James A; Bauer, Bernhard; Cavallaro, Alexander; Uder, Michael; Hammon, Matthias

2016-01-22

In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements.
Visual noise disrupts conceptual integration in reading.

PubMed

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

2011-02-01

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

PubMed

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

2012-01-01

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

NASA Astrophysics Data System (ADS)

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

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

NASA Astrophysics Data System (ADS)

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

2011-03-01

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

PubMed Central

Li, Qian; Balagurunathan, Yoganand; Liu, Ying; Qi, Jin; Schabath, Matthew B.; Ye, Zhaoxiang; Gillies, Robert

2017-01-01

Rationale Lung-RADS is proposed for the Low-dose computed tomography (LDCT) interpretation in lung cancer screening, but its performance needs to be further evaluated. Objectives To compare the value of radiological semantic features and lung-RADS in predicting nodule malignancy risk at different screening rounds, and to investigate whether the predictive power of lung-RADS could be improved by incorporating semantic features. Methods A training cohort of 199 patients (139 benign and 60 cancerous nodules diagnosed at the third screening round), and a testing cohort of 80 patients (40 benign and 40 malignant nodules) were obtained from the National Lung Screening Trial dataset. A multivariate linear predictor model was built based on the 24 systematically scored semantic features, and the performances were compared to lung-RADS (scale 3 or above called positive). Measurements and Main Results Among the semantic features, contour and border definition were the top individual predictors. The average area under the receiver-operating characteristic curve (AUC) of border definition at baseline (T0) was 0.724. The average AUC of contour at first (T1) and second follow-up (T2) were 0.843 and 0.878, respectively. Other significant features included size, location, vessel attachment, solidity, focal emphysema and focal fibrosis. In comparison, the average AUC of lung-RADS at T0, T1 and T2 were 0.600, 0.760 and 0.867, respectively, and could be improved to 0.743, 0.887 and 0.968 by adding semantic features. Conclusion The semantic features performed similar to lung-RADS at follow-ups, outperformed lung-RADS at baseline, and could improve the performance of lung-RADS for all screening rounds. PMID:29137847
Discovering semantic features in the literature: a foundation for building functional associations

PubMed Central

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

2006-01-01

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

NASA Astrophysics Data System (ADS)

Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

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

PubMed

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

2015-11-01

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

PubMed

Intraub, H; Nicklos, S

1985-04-01

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

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

PubMed Central

Weis, Susanne; Kircher, Tilo

2012-01-01

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

PubMed Central

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

2011-01-01

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

PubMed

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

2015-01-01

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

PubMed

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

2014-02-01

The present study examined the transfer of higher-order cognitive strategy training to inhibitory control. Middle school students enrolled in a comprehension- and reasoning-focused cognitive strategy training program and passive controls participated. The training program taught students a set of steps for inferring essential gist or themes from materials. Both before and after training or a comparable duration in the case of the passive controls, participants completed a semantically cued Go/No-Go task that was designed to assess the effects of depth of semantic processing on response inhibition and components of event-related potentials (ERP) related to response inhibition. Depth of semantic processing was manipulated by varying the level of semantic categorization required for response selection and inhibition. The SMART-trained group showed inhibitory control gains and changes in fronto-central P3 ERP amplitudes on inhibition trials; whereas, the control group did not. The results provide evidence of the transfer of higher-order cognitive strategy training to inhibitory control and modulation of ERPs associated with semantically cued inhibitory control. The findings are discussed in terms of implications for cognitive strategy training, models of cognitive abilities, and education. Published by Elsevier Inc.
Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

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

PubMed Central

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

2016-01-01

Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F1 measure of 71.41% achieved by the Berkeley parser. PMID:28269926
The N400 as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects

PubMed Central

Laszlo, Sarah; Federmeier, Kara D.

2010-01-01

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

PubMed

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

2016-06-15

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

PubMed Central

2010-01-01

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

PubMed Central

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

2009-01-01

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

PubMed

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

2008-01-01

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

PubMed

Hao, T; Weng, C

2015-01-01

To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts. We develop a "plug-n-play" framework that integrates replaceable unsupervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach's recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach's adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts. Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the base- line ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed. This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.
Semantic Integration for Marine Science Interoperability Using Web Technologies

NASA Astrophysics Data System (ADS)

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

2008-12-01

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

PubMed

Sepulcre, Jorge; Peraita, Herminia; Goni, Joaquin; Arrondo, Gonzalo; Martincorena, Inigo; Duque, Beatriz; Velez de Mendizabal, Nieves; Masdeu, Joseph C; Villoslada, Pablo

2011-02-01

The aim of the study was to analyze lexical access strategies in patients with multiple sclerosis (MS) and their changes over time. We studied lexical access strategies during semantic and phonemic verbal fluency tests and also confrontation naming in a 2-year prospective cohort of 45 MS patients and 20 healthy controls. At baseline, switching lexical access strategy (both in semantic and in phonemic verbal fluency tests) and confrontation naming were significantly impaired in MS patients compared with controls. After 2 years follow-up, switching score decreased, and cluster size increased over time in semantic verbal fluency tasks, suggesting a failure in the retrieval of lexical information rather than an impairment of the lexical pool. In conclusion, these findings underline the significant presence of lexical access problems in patients with MS and could point out their key role in the alterations of high-level communications abilities in MS.
BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.

PubMed

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

2016-01-01

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

PubMed

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

2011-04-01

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

NASA Technical Reports Server (NTRS)

Gounder, Ramasamy S.; Esterline, Albert C.

1998-01-01

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

PubMed

Liu, Hong; Zhang, Gaoyan; Liu, Baolin

2017-04-01

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

PubMed

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

2016-10-01

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

PubMed

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

2017-12-01

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

Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.

PubMed

Lahrmann, Bernd; Valous, Nektarios A; Eisenmann, Urs; Wentzensen, Nicolas; Grabe, Niels

2013-01-01

Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide) itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%). Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.
Processing Code-Switching in Algerian Bilinguals: Effects of Language Use and Semantic Expectancy

PubMed Central

Kheder, Souad; Kaan, Edith

2016-01-01

Using a cross-modal naming paradigm this study investigated the effect of sentence constraint and language use on the expectancy of a language switch during listening comprehension. Sixty-five Algerian bilinguals who habitually code-switch between Algerian Arabic and French (AA-FR) but not between Standard Arabic and French (SA-FR) listened to sentence fragments and named a visually presented French target NP out loud. Participants’ speech onset times were recorded. The sentence context was either highly semantically constraining toward the French NP or not. The language of the sentence context was either in Algerian Arabic or in Standard Arabic, but the target NP was always in French, thus creating two code-switching contexts: a typical and recurrent code-switching context (AA-FR) and a non-typical code-switching context (SA-FR). Results revealed a semantic constraint effect indicating that the French switches were easier to process in the high compared to the low-constraint context. In addition, the effect size of semantic constraint was significant in the more typical code-switching context (AA-FR) suggesting that language use influences the processing of switching between languages. The effect of semantic constraint was also modulated by code-switching habits and the proficiency of L2 French. Semantic constraint was reduced in bilinguals who frequently code-switch and in bilinguals with high proficiency in French. Results are discussed with regards to the bilingual interactive activation model (Dijkstra and Van Heuven, 2002) and the control process model of code-switching (Green and Wei, 2014). PMID:26973559
Gestural cue analysis in automated semantic miscommunication annotation

PubMed Central

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

2011-01-01

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

PubMed

Gutierrez, Fernando

2017-01-01

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

PubMed Central

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

2012-01-01

In designing informatics infrastructure to support comparative effectiveness research (CER), it is necessary to implement approaches for integrating heterogeneous data sources such as clinical data typically stored in clinical data warehouses and those that are normally stored in separate research databases. One strategy to support this integration is the use of a concept-oriented data dictionary with a set of semantic terminology models. The aim of this paper is to illustrate the use of the semantic structure of Clinical LOINC (Logical Observation Identifiers, Names, and Codes) in integrating community-based survey items into the Medical Entities Dictionary (MED) to support the integration of survey data with clinical data for CER studies. PMID:24199059
Spanish semantic feature production norms for 400 concrete concepts.

PubMed

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

2017-06-01

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

NASA Astrophysics Data System (ADS)

Elag, M.; Kumar, P.

2016-12-01

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

ERIC Educational Resources Information Center

Conradi, Kristin; Amendum, Steven J.; Liebfreund, Meghan D.

2016-01-01

This study examined the contributions of decoding, language, spelling, and motivation to the reading comprehension of elementary school readers in a high-poverty setting. Specifically, the research questions addressed whether and how the influences of word reading efficiency, semantic knowledge, reading self-concept, and spelling on reading…
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.
Ground Operations Aerospace Language (GOAL) textbook

NASA Technical Reports Server (NTRS)

Dickison, L. R.

1973-01-01

The textbook provides a semantical explanation accompanying a complete set of GOAL syntax diagrams, system concepts, language component interaction, and general language concepts necessary for efficient language implementation/execution.
Vision-based gait impairment analysis for aided diagnosis.

PubMed

Ortells, Javier; Herrero-Ezquerro, María Trinidad; Mollineda, Ramón A

2018-02-12

Gait is a firsthand reflection of health condition. This belief has inspired recent research efforts to automate the analysis of pathological gait, in order to assist physicians in decision-making. However, most of these efforts rely on gait descriptions which are difficult to understand by humans, or on sensing technologies hardly available in ambulatory services. This paper proposes a number of semantic and normalized gait features computed from a single video acquired by a low-cost sensor. Far from being conventional spatio-temporal descriptors, features are aimed at quantifying gait impairment, such as gait asymmetry from several perspectives or falling risk. They were designed to be invariant to frame rate and image size, allowing cross-platform comparisons. Experiments were formulated in terms of two databases. A well-known general-purpose gait dataset is used to establish normal references for features, while a new database, introduced in this work, provides samples under eight different walking styles: one normal and seven impaired patterns. A number of statistical studies were carried out to prove the sensitivity of features at measuring the expected pathologies, providing enough evidence about their accuracy. Graphical Abstract Graphical abstract reflecting main contributions of the manuscript: at the top, a robust, semantic and easy-to-interpret feature set to describe impaired gait patterns; at the bottom, a new dataset consisting of video-recordings of a number of volunteers simulating different patterns of pathological gait, where features were statistically assessed.
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
WebAlchemist: a Web transcoding system for mobile Web access in handheld devices

NASA Astrophysics Data System (ADS)

Whang, Yonghyun; Jung, Changwoo; Kim, Jihong; Chung, Sungkwon

2001-11-01

In this paper, we describe the design and implementation of WebAlchemist, a prototype web transcoding system, which automatically converts a given HTML page into a sequence of equivalent HTML pages that can be properly displayed on a hand-held device. The Web/Alchemist system is based on a set of HTML transcoding heuristics managed by the Transcoding Manager (TM) module. In order to tackle difficult-to-transcode pages such as ones with large or complex table structures, we have developed several new transcoding heuristics that extract partial semantics from syntactic information such as the table width, font size and cascading style sheet. Subjective evaluation results using popular HTML pages (such as the CNN home page) show that WebAlchemist generates readable, structure-preserving transcoded pages, which can be properly displayed on hand-held devices.
Sculpting the UMLS Refined Semantic Network.

PubMed

He, Zhe; Morrey, C Paul; Perl, Yehoshua; Elhanan, Gai; Chen, Ling; Chen, Yan; Geller, James

2014-01-01

The Refined Semantic Network (RSN) for the UMLS was previously introduced to complement the UMLS Semantic Network (SN). The RSN partitions the UMLS Metathesaurus (META) into disjoint groups of concepts. Each such group is semantically uniform. However, the RSN was initially an order of magnitude larger than the SN, which is undesirable since to be useful, a semantic network should be compact. Most semantic types in the RSN represent combinations of semantic types in the UMLS SN. Such a "combination semantic type" is called Intersection Semantic Type (IST). Many ISTs are assigned to very few concepts. Moreover, when reviewing those concepts, many semantic type assignment inconsistencies were found. After correcting those inconsistencies many ISTs, among them some that contradicted UMLS rules, disappeared, which made the RSN smaller. The authors performed a longitudinal study with the goal of reducing the size of the RSN to become compact. This goal was achieved by correcting inconsistencies and errors in the IST assignments in the UMLS, which additionally helped identify and correct ambiguities, inconsistencies, and errors in source terminologies widely used in the realm of public health. In this paper, we discuss the process and steps employed in this longitudinal study and the intermediate results for different stages. The sculpting process includes removing redundant semantic type assignments, expanding semantic type assignments, and removing illegitimate ISTs by auditing ISTs of small extents. However, the emphasis of this paper is not on the auditing methodologies employed during the process, since they were introduced in earlier publications, but on the strategy of employing them in order to transform the RSN into a compact network. For this paper we also performed a comprehensive audit of 168 "small ISTs" in the 2013AA version of the UMLS to finalize the longitudinal study. Over the years it was found that the editors of the UMLS introduced some new inconsistencies that resulted in the reintroduction of unwarranted ISTs that had already been eliminated as a result of their previous corrections. Because of that, the transformation of the RSN into a compact network covering all necessary categories for the UMLS was slowed down. The corrections suggested by an audit of the 2013AA version of the UMLS achieve a compact RSN of equal magnitude as the UMLS SN. The number of ISTs has been reduced to 336. We also demonstrate how auditing the semantic type assignments of UMLS concepts can expose other modeling errors in the UMLS source terminologies, e.g., SNOMED CT, LOINC, and RxNORM that are important for health informatics. Such errors would otherwise stay hidden. It is hoped that the UMLS curators will implement all required corrections and use the RSN along with the SN when maintaining and extending the UMLS. When used correctly, the RSN will support the prevention of the accidental introduction of inconsistent semantic type assignments into the UMLS. Furthermore, this way the RSN will support the exposure of other hidden errors and inconsistencies in health informatics terminologies, which are sources of the UMLS. Notably, the development of the RSN materializes the deeper, more refined Semantic Network for the UMLS that its designers envisioned originally but had not implemented.
DeepMeSH: deep semantic representation for improving large-scale MeSH indexing

PubMed Central

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

2016-01-01

Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the ‘learning to rank’ framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. Availability and Implementation: The software is available upon request. Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307646
Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.
Unitary vs multiple semantics: PET studies of word and picture processing.

PubMed

Bright, P; Moss, H; Tyler, L K

2004-06-01

In this paper we examine a central issue in cognitive neuroscience: are there separate conceptual representations associated with different input modalities (e.g., Paivio, 1971, 1986; Warrington & Shallice, 1984) or do inputs from different modalities converge on to the same set of representations (e.g., Caramazza, Hillis, Rapp, & Romani, 1990; Lambon Ralph, Graham, Patterson, & Hodges, 1999; Rapp, Hillis, & Caramazza, 1993)? We present an analysis of four PET studies (three semantic categorisation tasks and one lexical decision task), two of which employ words as stimuli and two of which employ pictures. Using conjunction analyses, we found robust semantic activation, common to both input modalities in anterior and medial aspects of the left fusiform gyrus, left parahippocampal and perirhinal cortices, and left inferior frontal gyrus (BA 47). There were modality-specific activations in both temporal poles (words) and occipitotemporal cortices (pictures). We propose that the temporal poles are involved in processing both words and pictures, but their engagement might be primarily determined by the level of specificity at which an object is processed. Activation in posterior temporal regions associated with picture processing most likely reflects intermediate, pre-semantic stages of visual processing. Our data are most consistent with a hierarchically structured, unitary system of semantic representations for both verbal and visual modalities, subserved by anterior regions of the inferior temporal cortex.

A common type system for clinical natural language processing

PubMed Central

2013-01-01

Background One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. Results We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. Conclusions We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types. PMID:23286462
Event Congruency Enhances Episodic Memory Encoding through Semantic Elaboration and Relational Binding

PubMed Central

Staresina, Bernhard P.; Gray, James C.

2009-01-01

Behavioral research consistently shows that congruous events, that is, events whose constituent elements match along some specific dimension, are better remembered than incongruous events. Although it has been speculated that this “congruency subsequent memory effect” (cSME) results from enhanced semantic elaboration, empirical evidence for this account is lacking. Here, we report a set of behavioral and neuroimaging experiments demonstrating that congruous events engage regions along the left inferior frontal gyrus (LIFG)—consistently related to semantic elaboration—to a significantly greater degree than incongruous events, providing evidence in favor of this hypothesis. Critically, we additionally report 3 novel findings in relation to event congruency: First, congruous events yield superior memory not only for a given study item but also for associated source details. Second, the cSME is evident not only for events that matched a semantic context but also for those that matched a subjective aesthetic schema. Finally, functional magnetic resonance imaging brain/behavior correlation analysis reveals a strong link between 1) across-subject variation in the magnitude of the cSME and 2) differential right hippocampal activation, suggesting that episodic memory for congruous events is effectively bolstered by the extent to which semantic associations are generated and relationally integrated via LIFG-hippocampal–encoding mechanisms. PMID:18820289
Joint Attributes and Event Analysis for Multimedia Event Detection.

PubMed

Ma, Zhigang; Chang, Xiaojun; Xu, Zhongwen; Sebe, Nicu; Hauptmann, Alexander G

2017-06-15

Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos. To address this issue, we propose to learn semantic attributes from external videos using their semantic labels. We name them video attributes in this paper. In contrast with multimedia event videos, these external videos depict lower level contents such as objects, scenes, and actions. To harness video attributes, we propose an algorithm established on a correlation vector that correlates them to a target event. Consequently, we could incorporate video attributes latently as extra information into the event detector learnt from multimedia event videos in a joint framework. To validate our method, we perform experiments on the real-world large-scale TRECVID MED 2013 and 2014 data sets and compare our method with several state-of-the-art algorithms. The experiments show that our method is advantageous for MED.
Three more semantic serial position functions and a SIMPLE explanation.

PubMed

Kelley, Matthew R; Neath, Ian; Surprenant, Aimée M

2013-05-01

There are innumerable demonstrations of serial position functions-with characteristic primacy and recency effects-in episodic tasks, but there are only a handful of such demonstrations in semantic memory tasks, and those demonstrations have used only two types of stimuli. Here, we provide three more examples of serial position functions when recalling from semantic memory. Participants were asked to reconstruct the order of (1) two cartoon theme song lyrics, (2) the seven Harry Potter books, and (3) two sets of movies, and all three demonstrations yielded conventional-looking serial position functions with primacy and recency effects. The data were well-fit by SIMPLE, a local distinctiveness model of memory that was originally designed to account for serial position effects in short- and long-term episodic memory. According to SIMPLE, serial position functions in both episodic and semantic memory tasks arise from the same type of processing: Items that are more separated from their close neighbors in psychological space at the time of recall will be better remembered. We argue that currently available evidence suggests that serial position functions observed when recalling items that are presumably in semantic memory arise because of the same processes as those observed when recalling items that are presumably in episodic memory.
Leveraging Pattern Semantics for Extracting Entities in Enterprises

PubMed Central

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-01-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises (“Blue” refers to “Windows Blue”), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach. PMID:26705540
Leveraging Pattern Semantics for Extracting Entities in Enterprises.

PubMed

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-05-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises ("Blue" refers to "Windows Blue"), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach.
Semantic attributes for people's appearance description: an appearance modality for video surveillance applications

NASA Astrophysics Data System (ADS)

Frikha, Mayssa; Fendri, Emna; Hammami, Mohamed

2017-09-01

Using semantic attributes such as gender, clothes, and accessories to describe people's appearance is an appealing modeling method for video surveillance applications. We proposed a midlevel appearance signature based on extracting a list of nameable semantic attributes describing the body in uncontrolled acquisition conditions. Conventional approaches extract the same set of low-level features to learn the semantic classifiers uniformly. Their critical limitation is the inability to capture the dominant visual characteristics for each trait separately. The proposed approach consists of extracting low-level features in an attribute-adaptive way by automatically selecting the most relevant features for each attribute separately. Furthermore, relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations. We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions. These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes. Various experiments prove that carefully designed features for learning the visual characteristics for an attribute provide an improvement of the correct classification accuracy and a reduction of both spatial and temporal complexities against state-of-the-art approaches.
Tableau Calculus for the Logic of Comparative Similarity over Arbitrary Distance Spaces

NASA Astrophysics Data System (ADS)

Alenda, Régis; Olivetti, Nicola

The logic CSL (first introduced by Sheremet, Tishkovsky, Wolter and Zakharyaschev in 2005) allows one to reason about distance comparison and similarity comparison within a modal language. The logic can express assertions of the kind "A is closer/more similar to B than to C" and has a natural application to spatial reasoning, as well as to reasoning about concept similarity in ontologies. The semantics of CSL is defined in terms of models based on different classes of distance spaces and it generalizes the logic S4 u of topological spaces. In this paper we consider CSL defined over arbitrary distance spaces. The logic comprises a binary modality to represent comparative similarity and a unary modality to express the existence of the minimum of a set of distances. We first show that the semantics of CSL can be equivalently defined in terms of preferential models. As a consequence we obtain the finite model property of the logic with respect to its preferential semantic, a property that does not hold with respect to the original distance-space semantics. Next we present an analytic tableau calculus based on its preferential semantics. The calculus provides a decision procedure for the logic, its termination is obtained by imposing suitable blocking restrictions.
MPEG-7-based description infrastructure for an audiovisual content analysis and retrieval system

NASA Astrophysics Data System (ADS)

Bailer, Werner; Schallauer, Peter; Hausenblas, Michael; Thallinger, Georg

2005-01-01

We present a case study of establishing a description infrastructure for an audiovisual content-analysis and retrieval system. The description infrastructure consists of an internal metadata model and access tool for using it. Based on an analysis of requirements, we have selected, out of a set of candidates, MPEG-7 as the basis of our metadata model. The openness and generality of MPEG-7 allow using it in broad range of applications, but increase complexity and hinder interoperability. Profiling has been proposed as a solution, with the focus on selecting and constraining description tools. Semantic constraints are currently only described in textual form. Conformance in terms of semantics can thus not be evaluated automatically and mappings between different profiles can only be defined manually. As a solution, we propose an approach to formalize the semantic constraints of an MPEG-7 profile using a formal vocabulary expressed in OWL, which allows automated processing of semantic constraints. We have defined the Detailed Audiovisual Profile as the profile to be used in our metadata model and we show how some of the semantic constraints of this profile can be formulated using ontologies. To work practically with the metadata model, we have implemented a MPEG-7 library and a client/server document access infrastructure.
A common type system for clinical natural language processing.

PubMed

Wu, Stephen T; Kaggal, Vinod C; Dligach, Dmitriy; Masanz, James J; Chen, Pei; Becker, Lee; Chapman, Wendy W; Savova, Guergana K; Liu, Hongfang; Chute, Christopher G

2013-01-03

One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.
Biotea: RDFizing PubMed Central in support for the paper as an interface to the Web of Data

PubMed Central

2013-01-01

Background The World Wide Web has become a dissemination platform for scientific and non-scientific publications. However, most of the information remains locked up in discrete documents that are not always interconnected or machine-readable. The connectivity tissue provided by RDF technology has not yet been widely used to support the generation of self-describing, machine-readable documents. Results In this paper, we present our approach to the generation of self-describing machine-readable scholarly documents. We understand the scientific document as an entry point and interface to the Web of Data. We have semantically processed the full-text, open-access subset of PubMed Central. Our RDF model and resulting dataset make extensive use of existing ontologies and semantic enrichment services. We expose our model, services, prototype, and datasets at http://biotea.idiginfo.org/ Conclusions The semantic processing of biomedical literature presented in this paper embeds documents within the Web of Data and facilitates the execution of concept-based queries against the entire digital library. Our approach delivers a flexible and adaptable set of tools for metadata enrichment and semantic processing of biomedical documents. Our model delivers a semantically rich and highly interconnected dataset with self-describing content so that software can make effective use of it. PMID:23734622
When Phonology Fails: Orthographic Neighbourhood Effects in Dyslexia

ERIC Educational Resources Information Center

Lavidor, Michal; Johnston, Rhona; Snowling, Margaret J.

2006-01-01

Both cerebral hemispheres contain phonological, orthographic and semantic representations of words, however there are between-hemisphere differences in the relative engagement and specialization of the different representations. Taking orthographic processing for example, previous studies suggest that orthographic neighbourhood size (N) has…
Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.

PubMed

Velupillai, S; Mowery, D; South, B R; Kvist, M; Dalianis, H

2015-08-13

We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis. We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers. Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications. There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.
Lexical selection in the semantically blocked cyclic naming task: the role of cognitive control and learning

PubMed Central

Crowther, Jason E.; Martin, Randi C.

2014-01-01

Studies of semantic interference in language production have provided evidence for a role of cognitive control mechanisms in regulating the activation of semantic competitors during naming. The present study investigated the relationship between individual differences in cognitive control abilities, for both younger and older adults, and the degree of semantic interference in a blocked cyclic naming task. We predicted that individuals with lower working memory capacity (as measured by word span), lesser ability to inhibit distracting responses (as measured by Stroop interference), and a lesser ability to resolve proactive interference (as measured by a recent negatives task) would show a greater increase in semantic interference in naming, with effects being larger for older adults. Instead, measures of cognitive control were found to relate to specific indices of semantic interference in the naming task, rather than overall degree of semantic interference, and few interactions with age were found, with younger and older adults performing similarly. The increase in naming latencies across naming trials within a cycle was negatively correlated with word span for both related and unrelated conditions, suggesting a strategy of narrowing response alternatives based upon memory for the set of item names. Evidence for a role of inhibition in response selection was obtained, as Stroop interference correlated positively with the change in naming latencies across cycles for the related, but not unrelated, condition. In contrast, recent negatives interference correlated negatively with the change in naming latencies across unrelated cycles, suggesting that individual differences in this tap the degree of strengthening of links in a lexical network based upon prior exposure. Results are discussed in terms of current models of lexical selection and consequences for word retrieval in more naturalistic production. PMID:24478675
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis

PubMed Central

Mowery, D.; South, B. R.; Kvist, M.; Dalianis, H.

2015-01-01

Summary Objectives We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis. Methods We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers. Results Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications. Conclusions There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices. PMID:26293867
Sound iconicity of abstract concepts: Place of articulation is implicitly associated with abstract concepts of size and social dominance.

PubMed

Auracher, Jan

2017-01-01

The concept of sound iconicity implies that phonemes are intrinsically associated with non-acoustic phenomena, such as emotional expression, object size or shape, or other perceptual features. In this respect, sound iconicity is related to other forms of cross-modal associations in which stimuli from different sensory modalities are associated with each other due to the implicitly perceived correspondence of their primal features. One prominent example is the association between vowels, categorized according to their place of articulation, and size, with back vowels being associated with bigness and front vowels with smallness. However, to date the relative influence of perceptual and conceptual cognitive processing on this association is not clear. To bridge this gap, three experiments were conducted in which associations between nonsense words and pictures of animals or emotional body postures were tested. In these experiments participants had to infer the relation between visual stimuli and the notion of size from the content of the pictures, while directly perceivable features did not support-or even contradicted-the predicted association. Results show that implicit associations between articulatory-acoustic characteristics of phonemes and pictures are mainly influenced by semantic features, i.e., the content of a picture, whereas the influence of perceivable features, i.e., size or shape, is overridden. This suggests that abstract semantic concepts can function as an interface between different sensory modalities, facilitating cross-modal associations.
Towards a Semantic Web of Community, Content and Interactions

DTIC Science & Technology

2005-09-01

importance of setting goals and deadlines as a means to achieving progress on the nebulous road to a dissertation. Jim Herbsleb sparked my interest in...RDF, such as Turtle [Bec04], a text syntax for RDF, and N-Triples [GDB04]. 45 </dc:creator> <dc:title>The Semantic Web: An Introduction</dc:title...2):22–41, 1990. 2.2.2 [Bec04] Dave Beckett. Turtle –terse rdf triple language. http://www.ilrt.bris.ac.uk/discovery/2004/01/ turtle /, January 2004. 4
Next generation data harmonization

NASA Astrophysics Data System (ADS)

Armstrong, Chandler; Brown, Ryan M.; Chaves, Jillian; Czerniejewski, Adam; Del Vecchio, Justin; Perkins, Timothy K.; Rudnicki, Ron; Tauer, Greg

2015-05-01

Analysts are presented with a never ending stream of data sources. Often, subsets of data sources to solve problems are easily identified but the process to align data sets is time consuming. However, many semantic technologies do allow for fast harmonization of data to overcome these problems. These include ontologies that serve as alignment targets, visual tools and natural language processing that generate semantic graphs in terms of the ontologies, and analytics that leverage these graphs. This research reviews a developed prototype that employs all these approaches to perform analysis across disparate data sources documenting violent, extremist events.
The semantics of Chemical Markup Language (CML): dictionaries and conventions.

PubMed

Murray-Rust, Peter; Townsend, Joe A; Adams, Sam E; Phadungsukanan, Weerapong; Thomas, Jens

2011-10-14

The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs.

The semantics of Chemical Markup Language (CML): dictionaries and conventions

PubMed Central

2011-01-01

The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs. PMID:21999509
Morphological Family Size Effects in Young First and Second Language Learners: Evidence of Cross-Language Semantic Activation in Visual Word Recognition

ERIC Educational Resources Information Center

de Zeeuw, Marlies; Verhoeven, Ludo; Schreuder, Robert

2012-01-01

This study examined to what extent young second language (L2) learners showed morphological family size effects in L2 word recognition and whether the effects were grade-level related. Turkish-Dutch bilingual children (L2) and Dutch (first language, L1) children from second, fourth, and sixth grade performed a Dutch lexical decision task on words…
The Anatomy of the Role of Morphological Awareness in Chinese Character Learning: The Mediation of Vocabulary and Semantic Radical Knowledge and the Moderation of Morpheme Family Size

ERIC Educational Resources Information Center

Liu, Duo; Li, Hong; Wong, Kwok Shing Richard

2017-01-01

In the present study, the mediating roles of syllable awareness, orthographic knowledge, and vocabulary skills and the moderating role of morpheme family size in the association between morphological awareness and Chinese character reading were investigated with 176 second-grade Hong Kong Chinese children. In the path analyses, the results…
Argument structure and the representation of abstract semantics.

PubMed

Rodríguez-Ferreiro, Javier; Andreu, Llorenç; Sanz-Torrent, Mònica

2014-01-01

According to the dual coding theory, differences in the ease of retrieval between concrete and abstract words are related to the exclusive dependence of abstract semantics on linguistic information. Argument structure can be considered a measure of the complexity of the linguistic contexts that accompany a verb. If the retrieval of abstract verbs relies more on the linguistic codes they are associated to, we could expect a larger effect of argument structure for the processing of abstract verbs. In this study, sets of length- and frequency-matched verbs including 40 intransitive verbs, 40 transitive verbs taking simple complements, and 40 transitive verbs taking sentential complements were presented in separate lexical and grammatical decision tasks. Half of the verbs were concrete and half were abstract. Similar results were obtained in the two tasks, with significant effects of imageability and transitivity. However, the interaction between these two variables was not significant. These results conflict with hypotheses assuming a stronger reliance of abstract semantics on linguistic codes. In contrast, our data are in line with theories that link the ease of retrieval with availability and robustness of semantic information.
Semantic Service Matchmaking in the ATM Domain Considering Infrastructure Capability Constraints

NASA Astrophysics Data System (ADS)

Moser, Thomas; Mordinyi, Richard; Sunindyo, Wikan Danar; Biffl, Stefan

In a service-oriented environment business processes flexibly build on software services provided by systems in a network. A key design challenge is the semantic matchmaking of business processes and software services in two steps: 1. Find for one business process the software services that meet or exceed the BP requirements; 2. Find for all business processes the software services that can be implemented within the capability constraints of the underlying network, which poses a major problem since even for small scenarios the solution space is typically very large. In this chapter we analyze requirements from mission-critical business processes in the Air Traffic Management (ATM) domain and introduce an approach for semi-automatic semantic matchmaking for software services, the “System-Wide Information Sharing” (SWIS) business process integration framework. A tool-supported semantic matchmaking process like SWIS can provide system designers and integrators with a set of promising software service candidates and therefore strongly reduces the human matching effort by focusing on a much smaller space of matchmaking candidates. We evaluate the feasibility of the SWIS approach in an industry use case from the ATM domain.
Neural correlates of three cognitive processes involved in theory of mind and discourse comprehension.

PubMed

Lin, Nan; Yang, Xiaohong; Li, Jing; Wang, Shaonan; Hua, Huimin; Ma, Yujun; Li, Xingshan

2018-04-01

Neuroimaging studies have found that theory of mind (ToM) and discourse comprehension involve similar brain regions. These brain regions may be associated with three cognitive components that are necessarily or frequently involved in ToM and discourse comprehension, including social concept representation and retrieval, domain-general semantic integration, and domain-specific integration of social semantic contents. Using fMRI, we investigated the neural correlates of these three cognitive components by exploring how discourse topic (social/nonsocial) and discourse processing period (ending/beginning) modulate brain activation in a discourse comprehension (and also ToM) task. Different sets of brain areas showed sensitivity to discourse topic, discourse processing period, and the interaction between them, respectively. The most novel finding was that the right temporoparietal junction and middle temporal gyrus showed sensitivity to discourse processing period only during social discourse comprehension, indicating that they selectively contribute to domain-specific semantic integration. Our finding indicates how different domains of semantic information are processed and integrated in the brain and provides new insights into the neural correlates of ToM and discourse comprehension.
Standard biological parts knowledgebase.

PubMed

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

2011-02-24

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

PubMed

Nygaard, Lynne C; Herold, Debora S; Namy, Laura L

2009-01-01

This investigation examined whether speakers produce reliable prosodic correlates to meaning across semantic domains and whether listeners use these cues to derive word meaning from novel words. Speakers were asked to produce phrases in infant-directed speech in which novel words were used to convey one of two meanings from a set of antonym pairs (e.g., big/small). Acoustic analyses revealed that some acoustic features were correlated with overall valence of the meaning. However, each word meaning also displayed a unique acoustic signature, and semantically related meanings elicited similar acoustic profiles. In two perceptual tests, listeners either attempted to identify the novel words with a matching meaning dimension (picture pair) or with mismatched meaning dimensions. Listeners inferred the meaning of the novel words significantly more often when prosody matched the word meaning choices than when prosody mismatched. These findings suggest that speech contains reliable prosodic markers to word meaning and that listeners use these prosodic cues to differentiate meanings. That prosody is semantic suggests a reconceptualization of traditional distinctions between linguistic and nonlinguistic properties of spoken language. Copyright © 2009 Cognitive Science Society, Inc.
Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis.

PubMed

Ye, Zheng; Doñamayor, Nuria; Münte, Thomas F

2014-02-01

A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context. To look into this issue, we conducted a functional magnetic resonance imaging (fMRI) study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis (ICA) and graph theoretical analysis (GTA). The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area (SMA), left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending. Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs. congruent endings. Copyright © 2012 Wiley Periodicals, Inc.
BioUSeR: a semantic-based tool for retrieving Life Science web resources driven by text-rich user requirements

PubMed Central

2013-01-01

Background Open metadata registries are a fundamental tool for researchers in the Life Sciences trying to locate resources. While most current registries assume that resources are annotated with well-structured metadata, evidence shows that most of the resource annotations simply consists of informal free text. This reality must be taken into account in order to develop effective techniques for resource discovery in Life Sciences. Results BioUSeR is a semantic-based tool aimed at retrieving Life Sciences resources described in free text. The retrieval process is driven by the user requirements, which consist of a target task and a set of facets of interest, both expressed in free text. BioUSeR is able to effectively exploit the available textual descriptions to find relevant resources by using semantic-aware techniques. Conclusions BioUSeR overcomes the limitations of the current registries thanks to: (i) rich specification of user information needs, (ii) use of semantics to manage textual descriptions, (iii) retrieval and ranking of resources based on user requirements. PMID:23635042
MESUR: USAGE-BASED METRICS OF SCHOLARLY IMPACT

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

BOLLEN, JOHAN; RODRIGUEZ, MARKO A.; VAN DE SOMPEL, HERBERT

2007-01-30

The evaluation of scholarly communication items is now largely a matter of expert opinion or metrics derived from citation data. Both approaches can fail to take into account the myriad of factors that shape scholarly impact. Usage data has emerged as a promising complement to existing methods o fassessment but the formal groundwork to reliably and validly apply usage-based metrics of schlolarly impact is lacking. The Andrew W. Mellon Foundation funded MESUR project constitutes a systematic effort to define, validate and cross-validate a range of usage-based metrics of schlolarly impact by creating a semantic model of the scholarly communication process.more » The constructed model will serve as the basis of a creating a large-scale semantic network that seamlessly relates citation, bibliographic and usage data from a variety of sources. A subsequent program that uses the established semantic network as a reference data set will determine the characteristics and semantics of a variety of usage-based metrics of schlolarly impact. This paper outlines the architecture and methodology adopted by the MESUR project and its future direction.« less
The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation

PubMed Central

2011-01-01

Background The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. Description SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. Conclusions SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies. PMID:22024447
The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation.

PubMed

Wilkinson, Mark D; Vandervalk, Benjamin; McCarthy, Luke

2011-10-24

The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies.
Emotional modelling and classification of a large-scale collection of scene images in a cluster environment

PubMed Central

Li, Yanfei; Tian, Yun

2018-01-01

The development of network technology and the popularization of image capturing devices have led to a rapid increase in the number of digital images available, and it is becoming increasingly difficult to identify a desired image from among the massive number of possible images. Images usually contain rich semantic information, and people usually understand images at a high semantic level. Therefore, achieving the ability to use advanced technology to identify the emotional semantics contained in images to enable emotional semantic image classification remains an urgent issue in various industries. To this end, this study proposes an improved OCC emotion model that integrates personality and mood factors for emotional modelling to describe the emotional semantic information contained in an image. The proposed classification system integrates the k-Nearest Neighbour (KNN) algorithm with the Support Vector Machine (SVM) algorithm. The MapReduce parallel programming model was used to adapt the KNN-SVM algorithm for parallel implementation in the Hadoop cluster environment, thereby achieving emotional semantic understanding for the classification of a massive collection of images. For training and testing, 70,000 scene images were randomly selected from the SUN Database. The experimental results indicate that users with different personalities show overall consistency in their emotional understanding of the same image. For a training sample size of 50,000, the classification accuracies for different emotional categories targeted at users with different personalities were approximately 95%, and the training time was only 1/5 of that required for the corresponding algorithm with a single-node architecture. Furthermore, the speedup of the system also showed a linearly increasing tendency. Thus, the experiments achieved a good classification effect and can lay a foundation for classification in terms of additional types of emotional image semantics, thereby demonstrating the practical significance of the proposed model. PMID:29320579
Emotional modelling and classification of a large-scale collection of scene images in a cluster environment.

PubMed

Cao, Jianfang; Li, Yanfei; Tian, Yun

2018-01-01

The development of network technology and the popularization of image capturing devices have led to a rapid increase in the number of digital images available, and it is becoming increasingly difficult to identify a desired image from among the massive number of possible images. Images usually contain rich semantic information, and people usually understand images at a high semantic level. Therefore, achieving the ability to use advanced technology to identify the emotional semantics contained in images to enable emotional semantic image classification remains an urgent issue in various industries. To this end, this study proposes an improved OCC emotion model that integrates personality and mood factors for emotional modelling to describe the emotional semantic information contained in an image. The proposed classification system integrates the k-Nearest Neighbour (KNN) algorithm with the Support Vector Machine (SVM) algorithm. The MapReduce parallel programming model was used to adapt the KNN-SVM algorithm for parallel implementation in the Hadoop cluster environment, thereby achieving emotional semantic understanding for the classification of a massive collection of images. For training and testing, 70,000 scene images were randomly selected from the SUN Database. The experimental results indicate that users with different personalities show overall consistency in their emotional understanding of the same image. For a training sample size of 50,000, the classification accuracies for different emotional categories targeted at users with different personalities were approximately 95%, and the training time was only 1/5 of that required for the corresponding algorithm with a single-node architecture. Furthermore, the speedup of the system also showed a linearly increasing tendency. Thus, the experiments achieved a good classification effect and can lay a foundation for classification in terms of additional types of emotional image semantics, thereby demonstrating the practical significance of the proposed model.
Tracking neural coding of perceptual and semantic features of concrete nouns

PubMed Central

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

2015-01-01

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

NASA Technical Reports Server (NTRS)

Lynnes, Chris; Leptoukh, Greg; Berrick, Steve; Shen, Suhung; Prados, Ana; Fox, Peter; Yang, Wenli; Min, Min; Holloway, Dan; Enloe, Yonsook

2008-01-01

As our inventory of Earth science data sets grows, the ability to compare, merge and fuse multiple datasets grows in importance. This requires a deeper data interoperability than we have now. Efforts such as Open Geospatial Consortium and OPeNDAP (Open-source Project for a Network Data Access Protocol) have broken down format barriers to interoperability; the next challenge is the semantic aspects of the data. Consider the issues when satellite data are merged, cross-calibrated, validated, inter-compared and fused. We must match up data sets that are related, yet different in significant ways: the phenomenon being measured, measurement technique, location in space-time or quality of the measurements. If subtle distinctions between similar measurements are not clear to the user, results can be meaningless or lead to an incorrect interpretation of the data. Most of these distinctions trace to how the data came to be: sensors, processing and quality assessment. For example, monthly averages of satellite-based aerosol measurements often show significant discrepancies, which might be due to differences in spatio- temporal aggregation, sampling issues, sensor biases, algorithm differences or calibration issues. Provenance information must be captured in a semantic framework that allows data inter-use tools to incorporate it and aid in the intervention of comparison or merged products. Semantic web technology allows us to encode our knowledge of measurement characteristics, phenomena measured, space-time representation, and data quality attributes in a well-structured, machine-readable ontology and rulesets. An analysis tool can use this knowledge to show users the provenance-related distrintions between two variables, advising on options for further data processing and analysis. An additional problem for workflows distributed across heterogeneous systems is retrieval and transport of provenance. Provenance may be either embedded within the data payload, or transmitted from server to client in an out-of-band mechanism. The out of band mechanism is more flexible in the richness of provenance information that can be accomodated, but it relies on a persistent framework and can be difficult for legacy clients to use. We are prototyping the embedded model, incorporating provenance within metadata objects in the data payload. Thus, it always remains with the data. The downside is a limit to the size of provenance metadata that we can include, an issue that will eventually need resolution to encompass the richness of provenance information required for daata intercomparison and merging.
Spatial coding of object typical size: evidence for a SNARC-like effect.

PubMed

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

2015-11-01

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

PubMed Central

Névéol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

Objective This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. Materials and methods The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Results Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. Conclusions The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis. PMID:17238409
Besides precision & recall: exploring alternative approaches to evaluating an automatic indexing tool for MEDLINE.

PubMed

Neveol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis.

Acute effects of exergames on cognitive function of institutionalized older persons: a single-blinded, randomized and controlled pilot study.

PubMed

Monteiro-Junior, Renato Sobral; da Silva Figueiredo, Luiz Felipe; Maciel-Pinheiro, Paulo de Tarso; Abud, Erick Lohan Rodrigues; Braga, Ana Elisa Mendes Montalvão; Barca, Maria Lage; Engedal, Knut; Nascimento, Osvaldo José M; Deslandes, Andrea Camaz; Laks, Jerson

2017-06-01

Improvements on balance, gait and cognition are some of the benefits of exergames. Few studies have investigated the cognitive effects of exergames in institutionalized older persons. To assess the acute effect of a single session of exergames on cognition of institutionalized older persons. Nineteen institutionalized older persons were randomly allocated to Wii (WG, n = 10, 86 ± 7 year, two males) or control groups (CG, n = 9, 86 ± 5 year, one male). The WG performed six exercises with virtual reality, whereas CG performed six exercises without virtual reality. Verbal fluency test (VFT), digit span forward and digit span backward were used to evaluate semantic memory/executive function, short-term memory and work memory, respectively, before and after exergames and Δ post- to pre-session (absolute) and Δ % (relative) were calculated. Parametric (t independent test) and nonparametric (Mann-Whitney test) statistics and effect size were applied to tests for efficacy. VFT was statistically significant within WG (-3.07, df = 9, p = 0.013). We found no statistically significant differences between the two groups (p > 0.05). Effect size between groups of Δ % (median = 21 %) showed moderate effect for WG (0.63). Our data show moderate improvement of semantic memory/executive function due to exergames session. It is possible that cognitive brain areas are activated during exergames, increasing clinical response. A single session of exergames showed no significant improvement in short-term memory, working memory and semantic memory/executive function. The effect size for verbal fluency was promising, and future studies on this issue should be developed. RBR-6rytw2.
Using Neural Networks to Generate Inferential Roles for Natural Language

PubMed Central

Blouw, Peter; Eliasmith, Chris

2018-01-01

Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences are likely to follow from a given sentence; these define the sentence's “inferential role.” We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition. PMID:29387031
Semantic and perceptual processing of number symbols: evidence from a cross-linguistic fMRI adaptation study.

PubMed

Holloway, Ian D; Battista, Christian; Vogel, Stephan E; Ansari, Daniel

2013-03-01

The ability to process the numerical magnitude of sets of items has been characterized in many animal species. Neuroimaging data have associated this ability to represent nonsymbolic numerical magnitudes (e.g., arrays of dots) with activity in the bilateral parietal lobes. Yet the quantitative abilities of humans are not limited to processing the numerical magnitude of nonsymbolic sets. Humans have used this quantitative sense as the foundation for symbolic systems for the representation of numerical magnitude. Although numerical symbol use is widespread in human cultures, the brain regions involved in processing of numerical symbols are just beginning to be understood. Here, we investigated the brain regions underlying the semantic and perceptual processing of numerical symbols. Specifically, we used an fMRI adaptation paradigm to examine the neural response to Hindu-Arabic numerals and Chinese numerical ideographs in a group of Chinese readers who could read both symbol types and a control group who could read only the numerals. Across groups, the Hindu-Arabic numerals exhibited ratio-dependent modulation in the left IPS. In contrast, numerical ideographs were associated with activation in the right IPS, exclusively in the Chinese readers. Furthermore, processing of the visual similarity of both digits and ideographs was associated with activation of the left fusiform gyrus. Using culture as an independent variable, we provide clear evidence for differences in the brain regions associated with the semantic and perceptual processing of numerical symbols. Additionally, we reveal a striking difference in the laterality of parietal activation between the semantic processing of the two symbols types.
Metadata management and semantics in microarray repositories.

PubMed

Kocabaş, F; Can, T; Baykal, N

2011-12-01

The number of microarray and other high-throughput experiments on primary repositories keeps increasing as do the size and complexity of the results in response to biomedical investigations. Initiatives have been started on standardization of content, object model, exchange format and ontology. However, there are backlogs and inability to exchange data between microarray repositories, which indicate that there is a great need for a standard format and data management. We have introduced a metadata framework that includes a metadata card and semantic nets that make experimental results visible, understandable and usable. These are encoded in syntax encoding schemes and represented in RDF (Resource Description Frame-word), can be integrated with other metadata cards and semantic nets, and can be exchanged, shared and queried. We demonstrated the performance and potential benefits through a case study on a selected microarray repository. We concluded that the backlogs can be reduced and that exchange of information and asking of knowledge discovery questions can become possible with the use of this metadata framework.
Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.
Mining large heterogeneous data sets in drug discovery.

PubMed

Wild, David J

2009-10-01

Increasingly, effective drug discovery involves the searching and data mining of large volumes of information from many sources covering the domains of chemistry, biology and pharmacology amongst others. This has led to a proliferation of databases and data sources relevant to drug discovery. This paper provides a review of the publicly-available large-scale databases relevant to drug discovery, describes the kinds of data mining approaches that can be applied to them and discusses recent work in integrative data mining that looks for associations that pan multiple sources, including the use of Semantic Web techniques. The future of mining large data sets for drug discovery requires intelligent, semantic aggregation of information from all of the data sources described in this review, along with the application of advanced methods such as intelligent agents and inference engines in client applications.
Subliminal number priming within and across the visual and auditory modalities.

PubMed

Kouider, Sid; Dehaene, Stanislas

2009-01-01

Whether masked number priming involves a low-level sensorimotor route or an amodal semantic level of processing remains highly debated. Several alternative interpretations have been put forward, proposing either that masked number priming is solely a byproduct of practice with numbers, or that stimulus awareness was underestimated. In a series of four experiments, we studied whether repetition and congruity priming for numbers reliably extend to novel (i.e., unpracticed) stimuli and whether priming transfers from a visual prime to an auditory target, even when carefully controlling for stimulus awareness. While we consistently observed cross-modal priming, the generalization to novel stimuli was weaker and reached significance only when considering the whole set of experiments. We conclude that number priming does involve an amodal, semantic level of processing, but is also modulated by task settings.
Semantic Approaches Applied to Scientific Ocean Drilling Data

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

NASA Astrophysics Data System (ADS)

Wan, Lin; Ren, Rongrong

2015-12-01

This paper aims at the geographic data integration and sharing method for multiple online VGI data sets. We propose a semantic-enabled framework for online VGI sources cooperative application environment to solve a target class of geospatial problems. Based on linked data technologies - which is one of core components of semantic web, we can construct the relationship link among geographic features distributed in diverse VGI platform by using linked data modeling methods, then deploy these semantic-enabled entities on the web, and eventually form an interconnected geographic data network to support geospatial information cooperative application across multiple VGI data sources. The mapping and transformation from VGI sources to RDF linked data model is presented to guarantee the unique data represent model among different online social geographic data sources. We propose a mixed strategy which combined spatial distance similarity and feature name attribute similarity as the measure standard to compare and match different geographic features in various VGI data sets. And our work focuses on how to apply Markov logic networks to achieve interlinks of the same linked data in different VGI-based linked data sets. In our method, the automatic generating method of co-reference object identification model according to geographic linked data is discussed in more detail. It finally built a huge geographic linked data network across loosely-coupled VGI web sites. The results of the experiment built on our framework and the evaluation of our method shows the framework is reasonable and practicable.
Sculpting the UMLS Refined Semantic Network

PubMed Central

Morrey, C. Paul; Perl, Yehoshua; Elhanan, Gai; Chen, Ling; Chen, Yan; Geller, James

2014-01-01

Background The Refined Semantic Network (RSN) for the UMLS was previously introduced to complement the UMLS Semantic Network (SN). The RSN partitions the UMLS Metathesaurus (META) into disjoint groups of concepts. Each such group is semantically uniform. However, the RSN was initially an order of magnitude larger than the SN, which is undesirable since to be useful, a semantic network should be compact. Most semantic types in the RSN represent combinations of semantic types in the UMLS SN. Such a “combination semantic type” is called Intersection Semantic Type (IST). Many ISTs are assigned to very few concepts. Moreover, when reviewing those concepts, many semantic type assignment inconsistencies were found. After correcting those inconsistencies many ISTs, among them some that contradicted UMLS rules, disappeared, which made the RSN smaller. Objective The authors performed a longitudinal study with the goal of reducing the size of the RSN to become compact. This goal was achieved by correcting inconsistencies and errors in the IST assignments in the UMLS, which additionally helped identify and correct ambiguities, inconsistencies, and errors in source terminologies widely used in the realm of public health. Methods In this paper, we discuss the process and steps employed in this longitudinal study and the intermediate results for different stages. The sculpting process includes removing redundant semantic type assignments, expanding semantic type assignments, and removing illegitimate ISTs by auditing ISTs of small extents. However, the emphasis of this paper is not on the auditing methodologies employed during the process, since they were introduced in earlier publications, but on the strategy of employing them in order to transform the RSN into a compact network. For this paper we also performed a comprehensive audit of 168 “small ISTs” in the 2013AA version of the UMLS to finalize the longitudinal study. Results Over the years it was found that the editors of the UMLS introduced some new inconsistencies that resulted in the reintroduction of unwarranted ISTs that had already been eliminated as a result of their previous corrections. Because of that, the transformation of the RSN into a compact network covering all necessary categories for the UMLS was slowed down. The corrections suggested by an audit of the 2013AA version of the UMLS achieve a compact RSN of equal magnitude as the UMLS SN. The number of ISTs has been reduced to 336. We also demonstrate how auditing the semantic type assignments of UMLS concepts can expose other modeling errors in the UMLS source terminologies, e.g., SNOMED CT, LOINC, and RxNORM that are important for health informatics. Such errors would otherwise stay hidden. Conclusions It is hoped that the UMLS curators will implement all required corrections and use the RSN along with the SN when maintaining and extending the UMLS. When used correctly, the RSN will support the prevention of the accidental introduction of inconsistent semantic type assignments into the UMLS. Furthermore, this way the RSN will support the exposure of other hidden errors and inconsistencies in health informatics terminologies, which are sources of the UMLS. Notably, the development of the RSN materializes the deeper, more refined Semantic Network for the UMLS that its designers envisioned originally but had not implemented. PMID:25422719
Semantic processing in connected speech at a uniformly early stage of autopsy-confirmed Alzheimer's disease.

PubMed

Ahmed, Samrah; de Jager, Celeste A; Haigh, Anne-Marie; Garrard, Peter

2013-01-01

The aim of the present study was to quantify the semantic content of connected speech produced by patients at a uniformly early stage of pathologically proven Alzheimer's disease (AD). A secondary aim was to establish whether semantic units were reduced globally, or whether there was a disproportionate reduction of specific classes of information. Discourse samples were obtained from 18 AD patients and 18 matched controls, all pathologically confirmed. Semantic unit identification was scored overall and for four subclasses: subjects, locations, objects, and actions. Idea density and efficiency were calculated. AD transcripts showed significantly reduced units overall, particularly actions and subjects, as well as reduced efficiency. Total semantic units and a combination of subject-, location-, and object-related units ("noun" units) correlated with the Expression subscore on the Cambridge Cognitive Examination (CAMCOG). Subject related units correlated with the CAMCOG Abstract Thinking scale. Logistic regression analyses confirmed that all measures that were lower in AD than controls were predictive of group membership. An exploratory comparison between units expressed mainly using nouns and those mainly using verbs showed that the latter was the stronger of these two predictors. The present study adds a lexico-semantic dimension to the linguistic profile based on discourse analysis in typical AD, recently described by the same authors. 2012, 83(11): 1056-1062). The suggestion of differential importance of verb and noun use in the present study may be related to the reduction in syntactic complexity that was reported, using the same set of discourse samples, in the earlier study.
Inhibitory mechanism of the matching heuristic in syllogistic reasoning.

PubMed

Tse, Ping Ping; Moreno Ríos, Sergio; García-Madruga, Juan Antonio; Bajo Molina, María Teresa

2014-11-01

A number of heuristic-based hypotheses have been proposed to explain how people solve syllogisms with automatic processes. In particular, the matching heuristic employs the congruency of the quantifiers in a syllogism—by matching the quantifier of the conclusion with those of the two premises. When the heuristic leads to an invalid conclusion, successful solving of these conflict problems requires the inhibition of automatic heuristic processing. Accordingly, if the automatic processing were based on processing the set of quantifiers, no semantic contents would be inhibited. The mental model theory, however, suggests that people reason using mental models, which always involves semantic processing. Therefore, whatever inhibition occurs in the processing implies the inhibition of the semantic contents. We manipulated the validity of the syllogism and the congruency of the quantifier of its conclusion with those of the two premises according to the matching heuristic. A subsequent lexical decision task (LDT) with related words in the conclusion was used to test any inhibition of the semantic contents after each syllogistic evaluation trial. In the LDT, the facilitation effect of semantic priming diminished after correctly solved conflict syllogisms (match-invalid or mismatch-valid), but was intact after no-conflict syllogisms. The results suggest the involvement of an inhibitory mechanism of semantic contents in syllogistic reasoning when there is a conflict between the output of the syntactic heuristic and actual validity. Our results do not support a uniquely syntactic process of syllogistic reasoning but fit with the predictions based on mental model theory. Copyright © 2014 Elsevier B.V. All rights reserved.
Rapid parallel semantic processing of numbers without awareness.

PubMed

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

2011-07-01

In this study, we investigate whether multiple digits can be processed at a semantic level without awareness, either serially or in parallel. In two experiments, we presented participants with two successive sets of four simultaneous Arabic digits. The first set was masked and served as a subliminal prime for the second, visible target set. According to the instructions, participants had to extract from the target set either the mean or the sum of the digits, and to compare it with a reference value. Results showed that participants applied the requested instruction to the entire set of digits that was presented below the threshold of conscious perception, because their magnitudes jointly affected the participant's decision. Indeed, response decision could be accurately modeled as a sigmoid logistic function that pooled together the evidence provided by the four targets and, with lower weights, the four primes. In less than 800ms, participants successfully approximated the addition and mean tasks, although they tended to overweight the large numbers, particularly in the sum task. These findings extend previous observations on ensemble coding by showing that set statistics can be extracted from abstract symbolic stimuli rather than low-level perceptual stimuli, and that an ensemble code can be represented without awareness. Copyright © 2011 Elsevier B.V. All rights reserved.
Relative Weighting of Semantic and Syntactic Cues in Native and Non-Native Listeners' Recognition of English Sentences.

PubMed

Shi, Lu-Feng; Koenig, Laura L

2016-01-01

Non-native listeners do not recognize English sentences as effectively as native listeners, especially in noise. It is not entirely clear to what extent such group differences arise from differences in relative weight of semantic versus syntactic cues. This study quantified the use and weighting of these contextual cues via Boothroyd and Nittrouer's j and k factors. The j represents the probability of recognizing sentences with or without context, whereas the k represents the degree to which context improves recognition performance. Four groups of 13 normal-hearing young adult listeners participated. One group consisted of native English monolingual (EMN) listeners, whereas the other three consisted of non-native listeners contrasting in their language dominance and first language: English-dominant Russian-English, Russian-dominant Russian-English, and Spanish-dominant Spanish-English bilinguals. All listeners were presented three sets of four-word sentences: high-predictability sentences included both semantic and syntactic cues, low-predictability sentences included syntactic cues only, and zero-predictability sentences included neither semantic nor syntactic cues. Sentences were presented at 65 dB SPL binaurally in the presence of speech-spectrum noise at +3 dB SNR. Listeners orally repeated each sentence and recognition was calculated for individual words as well as the sentence as a whole. Comparable j values across groups for high-predictability, low-predictability, and zero-predictability sentences suggested that all listeners, native and non-native, utilized contextual cues to recognize English sentences. Analysis of the k factor indicated that non-native listeners took advantage of syntax as effectively as EMN listeners. However, only English-dominant bilinguals utilized semantics to the same extent as EMN listeners; semantics did not provide a significant benefit for the two non-English-dominant groups. When combined, semantics and syntax benefitted EMN listeners significantly more than all three non-native groups of listeners. Language background influenced the use and weighting of semantic and syntactic cues in a complex manner. A native language advantage existed in the effective use of both cues combined. A language-dominance effect was seen in the use of semantics. No first-language effect was present for the use of either or both cues. For all non-native listeners, syntax contributed significantly more to sentence recognition than semantics, possibly due to the fact that semantics develops more gradually than syntax in second-language acquisition. The present study provides evidence that Boothroyd and Nittrouer's j and k factors can be successfully used to quantify the effectiveness of contextual cue use in clinically relevant, linguistically diverse populations.
Atypical neurophysiology underlying episodic and semantic memory in adults with autism spectrum disorder.

PubMed

Massand, Esha; Bowler, Dermot M

2015-02-01

Individuals with autism spectrum disorder (ASD) show atypicalities in episodic memory (Boucher et al. in Psychological Bulletin, 138 (3), 458-496, 2012). We asked participants to recall the colours of a set of studied line drawings (episodic judgement), or to recognize line drawings alone (semantic judgement). Cycowicz et al. (Journal of Experimental Child Psychology, 65, 171-237, 2001) found early (300 ms onset) posterior old-new event-related potential effects for semantic judgements in typically developing (TD) individuals, and occipitally focused negativity (800 ms onset) for episodic judgements. Our results replicated findings in TD individuals and demonstrate attenuated early old-new effects in ASD. Late posterior negativity was present in the ASD group, but was not specific to this time window. This non-specificity may contribute to the atypical episodic memory judgements characteristic of individuals with ASD.
Ontology based decision system for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.
Determinants of translation ambiguity

PubMed Central

Degani, Tamar; Prior, Anat; Eddington, Chelsea M.; Arêas da Luz Fontes, Ana B.; Tokowicz, Natasha

2016-01-01

Ambiguity in translation is highly prevalent, and has consequences for second-language learning and for bilingual lexical processing. To better understand this phenomenon, the current study compared the determinants of translation ambiguity across four sets of translation norms from English to Spanish, Dutch, German and Hebrew. The number of translations an English word received was correlated across these different languages, and was also correlated with the number of senses the word has in English, demonstrating that translation ambiguity is partially determined by within-language semantic ambiguity. For semantically-ambiguous English words, the probability of the different translations in Spanish and Hebrew was predicted by the meaning-dominance structure in English, beyond the influence of other lexical and semantic factors, for bilinguals translating from their L1, and translating from their L2. These findings are consistent with models postulating direct access to meaning from L2 words for moderately-proficient bilinguals. PMID:27882188
A Sieving ANN for Emotion-Based Movie Clip Classification

NASA Astrophysics Data System (ADS)

Watanapa, Saowaluk C.; Thipakorn, Bundit; Charoenkitkarn, Nipon

Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.
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.
SEMPATH Ontology: modeling multidisciplinary treatment schemes utilizing semantics.

PubMed

Alexandrou, Dimitrios Al; Pardalis, Konstantinos V; Bouras, Thanassis D; Karakitsos, Petros; Mentzas, Gregoris N

2012-03-01

A dramatic increase of demand for provided treatment quality has occurred during last decades. The main challenge to be confronted, so as to increase treatment quality, is the personalization of treatment, since each patient constitutes a unique case. Healthcare provision encloses a complex environment since healthcare provision organizations are highly multidisciplinary. In this paper, we present the conceptualization of the domain of clinical pathways (CP). The SEMPATH (SEMantic PATHways) Oontology comprises three main parts: 1) the CP part; 2) the business and finance part; and 3) the quality assurance part. Our implementation achieves the conceptualization of the multidisciplinary domain of healthcare provision, in order to be further utilized for the implementation of a Semantic Web Rules (SWRL rules) repository. Finally, SEMPATH Ontology is utilized for the definition of a set of SWRL rules for the human papillomavirus) disease and its treatment scheme. © 2012 IEEE

Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

NASA Technical Reports Server (NTRS)

Anastasiadis, Stergios

1991-01-01

Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.
OMOGENIA: A Semantically Driven Collaborative Environment

NASA Astrophysics Data System (ADS)

Liapis, Aggelos

Ontology creation can be thought of as a social procedure. Indeed the concepts involved in general need to be elicited from communities of domain experts and end-users by teams of knowledge engineers. Many problems in ontology creation appear to resemble certain problems in software design, particularly with respect to the setup of collaborative systems. For instance, the resolution of conceptual conflicts between formalized ontologies is a major engineering problem as ontologies move into widespread use on the semantic web. Such conflict resolution often requires human collaboration and cannot be achieved by automated methods with the exception of simple cases. In this chapter we discuss research in the field of computer-supported cooperative work (CSCW) that focuses on classification and which throws light on ontology building. Furthermore, we present a semantically driven collaborative environment called OMOGENIA as a natural way to display and examine the structure of an evolving ontology in a collaborative setting.
Next-generation applications in healthcare digital libraries using semantic service composition and coordination.

PubMed

Möller, Thorsten; Schuldt, Heiko; Gerber, Andreas; Klusch, Matthias

2006-06-01

Healthcare digital libraries (DLs) increasingly make use of dedicated services to access functionality and/or data. Semantic (web) services enhance single services and facilitate compound services, thereby supporting advanced applications on top of a DL. The traditional process management approach tends to focus on process definition at build time rather than on actual service events in run time, and to anticipate failures in order to define appropriate strategies. This paper presents a novel approach where service coordination is distributed among a set of agents. A dedicated component plans compound semantic services on demand for a particular application. In failure, the planner is reinvoked to define contin- gency strategies. Finally, matchmaking is effected at runtime by choosing the appropriate service provider. These combined technologies will provide key support for highly flexible next-generation DL applications. Such technologies are under development within CASCOM.
Ontology-aided Data Fusion (Invited)

NASA Astrophysics Data System (ADS)

Raskin, R.

2009-12-01

An ontology provides semantic descriptions that are analogous to those in a dictionary, but are readable by both computers and humans. A data or service is semantically annotated when it is formally associated with elements of an ontology. The ESIP Federation Semantic Web Cluster has developed a set of ontologies to describe datatypes and data services that can be used to support automated data fusion. The service ontology includes descriptors of the service function, its inputs/outputs, and its invocation method. The datatype descriptors resemble typical metadata fields (data format, data model, data structure, originator, etc.) augmented with descriptions of the meaning of the data. These ontologies, in combination with the SWEET science ontology, enable a registered data fusion service to be chained together and implemented that is scientifically meaningful based on machine understanding of the associated data and services. This presentation describes initial results and experiences in automated data fusion.
Semantic integration to identify overlapping functional modules in protein interaction networks

PubMed Central

Cho, Young-Rae; Hwang, Woochang; Ramanathan, Murali; Zhang, Aidong

2007-01-01

Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification. PMID:17650343
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.
Forced to remember: when memory is biased by salient information.

PubMed

Santangelo, Valerio

2015-04-15

The last decades have seen a rapid growing in the attempt to understand the key factors involved in the internal memory representation of the external world. Visual salience have been found to provide a major contribution in predicting the probability for an item/object embedded in a complex setting (i.e., a natural scene) to be encoded and then remembered later on. Here I review the existing literature highlighting the impact of perceptual- (based on low-level sensory features) and semantics-related salience (based on high-level knowledge) on short-term memory representation, along with the neural mechanisms underpinning the interplay between these factors. The available evidence reveal that both perceptual- and semantics-related factors affect attention selection mechanisms during the encoding of natural scenes. Biasing internal memory representation, both perceptual and semantics factors increase the probability to remember high- to the detriment of low-saliency items. The available evidence also highlight an interplay between these factors, with a reduced impact of perceptual-related salience in biasing memory representation as a function of the increasing availability of semantics-related salient information. The neural mechanisms underpinning this interplay involve the activation of different portions of the frontoparietal attention control network. Ventral regions support the assignment of selection/encoding priorities based on high-level semantics, while the involvement of dorsal regions reflects priorities assignment based on low-level sensory features. Copyright © 2015 Elsevier B.V. All rights reserved.
Cross-cultural adaptation of instruments assessing breastfeeding determinants: a multi-step approach

PubMed Central

2014-01-01

Background Cross-cultural adaptation is a necessary process to effectively use existing instruments in other cultural and language settings. The process of cross-culturally adapting, including translation, of existing instruments is considered a critical set to establishing a meaningful instrument for use in another setting. Using a multi-step approach is considered best practice in achieving cultural and semantic equivalence of the adapted version. We aimed to ensure the content validity of our instruments in the cultural context of KwaZulu-Natal, South Africa. Methods The Iowa Infant Feeding Attitudes Scale, Breastfeeding Self-Efficacy Scale-Short Form and additional items comprise our consolidated instrument, which was cross-culturally adapted utilizing a multi-step approach during August 2012. Cross-cultural adaptation was achieved through steps to maintain content validity and attain semantic equivalence in the target version. Specifically, Lynn’s recommendation to apply an item-level content validity index score was followed. The revised instrument was translated and back-translated. To ensure semantic equivalence, Brislin’s back-translation approach was utilized followed by the committee review to address any discrepancies that emerged from translation. Results Our consolidated instrument was adapted to be culturally relevant and translated to yield more reliable and valid results for use in our larger research study to measure infant feeding determinants effectively in our target cultural context. Conclusions Undertaking rigorous steps to effectively ensure cross-cultural adaptation increases our confidence that the conclusions we make based on our self-report instrument(s) will be stronger. In this way, our aim to achieve strong cross-cultural adaptation of our consolidated instruments was achieved while also providing a clear framework for other researchers choosing to utilize existing instruments for work in other cultural, geographic and population settings. PMID:25285151
Linguistic contributions to speech-on-speech masking for native and non-native listeners: language familiarity and semantic content.

PubMed

Brouwer, Susanne; Van Engen, Kristin J; Calandruccio, Lauren; Bradlow, Ann R

2012-02-01

This study examined whether speech-on-speech masking is sensitive to variation in the degree of similarity between the target and the masker speech. Three experiments investigated whether speech-in-speech recognition varies across different background speech languages (English vs Dutch) for both English and Dutch targets, as well as across variation in the semantic content of the background speech (meaningful vs semantically anomalous sentences), and across variation in listener status vis-à-vis the target and masker languages (native, non-native, or unfamiliar). The results showed that the more similar the target speech is to the masker speech (e.g., same vs different language, same vs different levels of semantic content), the greater the interference on speech recognition accuracy. Moreover, the listener's knowledge of the target and the background language modulate the size of the release from masking. These factors had an especially strong effect on masking effectiveness in highly unfavorable listening conditions. Overall this research provided evidence that that the degree of target-masker similarity plays a significant role in speech-in-speech recognition. The results also give insight into how listeners assign their resources differently depending on whether they are listening to their first or second language. © 2012 Acoustical Society of America
Linguistic contributions to speech-on-speech masking for native and non-native listeners: Language familiarity and semantic content

PubMed Central

Brouwer, Susanne; Van Engen, Kristin J.; Calandruccio, Lauren; Bradlow, Ann R.

2012-01-01

This study examined whether speech-on-speech masking is sensitive to variation in the degree of similarity between the target and the masker speech. Three experiments investigated whether speech-in-speech recognition varies across different background speech languages (English vs Dutch) for both English and Dutch targets, as well as across variation in the semantic content of the background speech (meaningful vs semantically anomalous sentences), and across variation in listener status vis-à-vis the target and masker languages (native, non-native, or unfamiliar). The results showed that the more similar the target speech is to the masker speech (e.g., same vs different language, same vs different levels of semantic content), the greater the interference on speech recognition accuracy. Moreover, the listener’s knowledge of the target and the background language modulate the size of the release from masking. These factors had an especially strong effect on masking effectiveness in highly unfavorable listening conditions. Overall this research provided evidence that that the degree of target-masker similarity plays a significant role in speech-in-speech recognition. The results also give insight into how listeners assign their resources differently depending on whether they are listening to their first or second language. PMID:22352516
Linking Disparate Datasets of the Earth Sciences with the SemantEco Annotator

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

NASA Technical Reports Server (NTRS)

Kacprzak, M.; Lomuscio, A.; Lasica, T.; Penczek, W.; Szreter, M.

2004-01-01

We present an approach to the problem of verification of epistemic properties in multi-agent systems by means of symbolic model checking. In particular, it is shown how to extend the technique of unbounded model checking from a purely temporal setting to a temporal-epistemic one. In order to achieve this, we base our discussion on interpreted systems semantics, a popular semantics used in multi-agent systems literature. We give details of the technique and show how it can be applied to the well known train, gate and controller problem. Keywords: model checking, unbounded model checking, multi-agent systems
Semantically induced memories of love and helping behavior.

PubMed

Lamy, Lubomir; Fischer-Lokou, Jacques; Guéguen, Nicolas

2008-04-01

This study tested the effect of semantically induced thoughts of love on helping behavior. In a natural setting, 253 participants were interviewed and asked to retrieve the memory of a love episode or, in the control condition, a piece of music they loved. They then met another confederate who asked for money. Analysis showed that inducing the idea of love had a significant positive effect on compliance to a request by a male passerby who was asked for help by a female confederate, but not by a female passerby. Theoretical explanations are presented, based on a gender-role expectation hypothesis.
Semantics Enabled Queries in EuroGEOSS: a Discovery Augmentation Approach

NASA Astrophysics Data System (ADS)

Santoro, M.; Mazzetti, P.; Fugazza, C.; Nativi, S.; Craglia, M.

2010-12-01

One of the main challenges in Earth Science Informatics is to build interoperability frameworks which allow users to discover, evaluate, and use information from different scientific domains. This needs to address multidisciplinary interoperability challenges concerning both technological and scientific aspects. From the technological point of view, it is necessary to provide a set of special interoperability arrangement in order to develop flexible frameworks that allow a variety of loosely-coupled services to interact with each other. From a scientific point of view, it is necessary to document clearly the theoretical and methodological assumptions underpinning applications in different scientific domains, and develop cross-domain ontologies to facilitate interdisciplinary dialogue and understanding. In this presentation we discuss a brokering approach that extends the traditional Service Oriented Architecture (SOA) adopted by most Spatial Data Infrastructures (SDIs) to provide the necessary special interoperability arrangements. In the EC-funded EuroGEOSS (A European approach to GEOSS) project, we distinguish among three possible functional brokering components: discovery, access and semantics brokers. This presentation focuses on the semantics broker, the Discovery Augmentation Component (DAC), which was specifically developed to address the three thematic areas covered by the EuroGEOSS project: biodiversity, forestry and drought. The EuroGEOSS DAC federates both semantics (e.g. SKOS repositories) and ISO-compliant geospatial catalog services. The DAC can be queried using common geospatial constraints (i.e. what, where, when, etc.). Two different augmented discovery styles are supported: a) automatic query expansion; b) user assisted query expansion. In the first case, the main discovery steps are: i. the query keywords (the what constraint) are “expanded” with related concepts/terms retrieved from the set of federated semantic services. A default expansion regards the multilinguality relationship; ii. The resulting queries are submitted to the federated catalog services; iii. The DAC performs a “smart” aggregation of the queries results and provides them back to the client. In the second case, the main discovery steps are: i. the user browses the federated semantic repositories and selects the concepts/terms-of-interest; ii. The DAC creates the set of geospatial queries based on the selected concepts/terms and submits them to the federated catalog services; iii. The DAC performs a “smart” aggregation of the queries results and provides them back to the client. A Graphical User Interface (GUI) was also developed for testing and interacting with the DAC. The entire brokering framework is deployed in the context of EuroGEOSS infrastructure and it is used in a couple of GEOSS AIP-3 use scenarios: the “e-Habitat Use Scenario” for the Biodiversity and Climate Change topic, and the “Comprehensive Drought Index Use Scenario” for Water/Drought topic
Extending Primitive Spatial Data Models to Include Semantics

NASA Astrophysics Data System (ADS)

Reitsma, F.; Batcheller, J.

2009-04-01

Our traditional geospatial data model involves associating some measurable quality, such as temperature, or observable feature, such as a tree, with a point or region in space and time. When capturing data we implicitly subscribe to some kind of conceptualisation. If we can make this explicit in an ontology and associate it with the captured data, we can leverage formal semantics to reason with the concepts represented in our spatial data sets. To do so, we extend our fundamental representation of geospatial data in a data model by including a URI in our basic data model that links it to our ontology defining our conceptualisation, We thus extend Goodchild et al's geo-atom [1] with the addition of a URI: (x, Z, z(x), URI) . This provides us with pixel or feature level knowledge and the ability to create layers of data from a set of pixels or features that might be drawn from a database based on their semantics. Using open source tools, we present a prototype that involves simple reasoning as a proof of concept. References [1] M.F. Goodchild, M. Yuan, and T.J. Cova. Towards a general theory of geographic representation in gis. International Journal of Geographical Information Science, 21(3):239-260, 2007.
Spatio-Temporal Data Model for Integrating Evolving Nation-Level Datasets

NASA Astrophysics Data System (ADS)

Sorokine, A.; Stewart, R. N.

2017-10-01

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

PubMed

Rönnlund, Michael; Nilsson, Lars-Göran

2009-09-01

The study examined the extent to which time-related gains in cognitive performance, so-called Flynn effects, generalize across sub-factors of episodic memory (recall and recognition) and semantic memory (knowledge and fluency). We conducted time-sequential analyses of data drawn from the Betula prospective cohort study, involving four age-matched samples (35-80 years; N=2996) tested on the same battery of memory tasks on either of four occasions (1989, 1995, 1999, and 2004). The results demonstrate substantial time-related improvements on recall and recognition as well as on fluency and knowledge, with a trend of larger gains on semantic as compared with episodic memory [Rönnlund, M., & Nilsson, L. -G. (2008). The magnitude, generality, and determinants of Flynn effects on forms of declarative memory: Time-sequential analyses of data from a Swedish cohort study. Intelligence], but highly similar gains across the sub-factors. Finally, the association with markers of environmental change was similar, with evidence that historical increases in quantity of schooling was a main driving force behind the gains, both on the episodic and semantic sub-factors. The results obtained are discussed in terms of brain regions involved.
Standard Biological Parts Knowledgebase

PubMed Central

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

2011-01-01

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

PubMed

Dehdarbehbahani, Iman; Shakery, Azadeh; Faili, Heshaam

2014-10-01

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

NASA Astrophysics Data System (ADS)

Hwang, Chyi-Wen

2015-07-01

When facing the "overabundant" of semantic web information, in this paper, the researcher proposes the hierarchical classification and visualizing RIA (Rich Internet Application) navigation system: Concept Map (CM) + Semantic Structure (SS) + the Knowledge on Demand (KOD) service. The aim of the Multimedia processing and empirical applications testing, was to investigating the utility and usability of this visualizing navigation strategy in web communication design, into whether it enables the user to retrieve and construct their personal knowledge or not. Furthermore, based on the segment markets theory in the Marketing model, to propose a User Interface (UI) classification strategy and formulate a set of hypermedia design principles for further UI strategy and e-learning resources in semantic web communication. These research findings: (1) Irrespective of whether the simple declarative knowledge or the complex declarative knowledge model is used, the "CM + SS + KOD navigation system" has a better cognition effect than the "Non CM + SS + KOD navigation system". However, for the" No web design experience user", the navigation system does not have an obvious cognition effect. (2) The essential of classification in semantic web communication design: Different groups of user have a diversity of preference needs and different cognitive styles in the CM + SS + KOD navigation system.

Positive and negative emotion enhances the processing of famous faces in a semantic judgment task.

PubMed

Bate, Sarah; Haslam, Catherine; Hodgson, Timothy L; Jansari, Ashok; Gregory, Nicola; Kay, Janice

2010-01-01

Previous work has consistently reported a facilitatory influence of positive emotion in face recognition (e.g., D'Argembeau, Van der Linden, Comblain, & Etienne, 2003). However, these reports asked participants to make recognition judgments in response to faces, and it is unknown whether emotional valence may influence other stages of processing, such as at the level of semantics. Furthermore, other evidence suggests that negative rather than positive emotion facilitates higher level judgments when processing nonfacial stimuli (e.g., Mickley & Kensinger, 2008), and it is possible that negative emotion also influences latter stages of face processing. The present study addressed this issue, examining the influence of emotional valence while participants made semantic judgments in response to a set of famous faces. Eye movements were monitored while participants performed this task, and analyses revealed a reduction in information extraction for the faces of liked and disliked celebrities compared with those of emotionally neutral celebrities. Thus, in contrast to work using familiarity judgments, both positive and negative emotion facilitated processing in this semantic-based task. This pattern of findings is discussed in relation to current models of face processing. Copyright 2009 APA, all rights reserved.
Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

PubMed Central

Cao, Jianfang; Chen, Lichao

2015-01-01

With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818
Changes in task-based effective connectivity in language networks following rehabilitation in post-stroke patients with aphasia

PubMed Central

Kiran, Swathi; Meier, Erin L.; Kapse, Kushal J.; Glynn, Peter A.

2015-01-01

In this study, we examined regions in the left and right hemisphere language network that were altered in terms of the underlying neural activation and effective connectivity subsequent to language rehabilitation. Eight persons with chronic post-stroke aphasia and eight normal controls participated in the current study. Patients received a 10 week semantic feature-based rehabilitation program to improve their skills. Therapy was provided on atypical examples of one trained category while two control categories were monitored; the categories were counterbalanced across patients. In each fMRI session, two experimental tasks were conducted: (a) picture naming and (b) semantic feature verification of trained and untrained categories. Analysis of treatment effect sizes revealed that all patients showed greater improvements on the trained category relative to untrained categories. Results from this study show remarkable patterns of consistency despite the inherent variability in lesion size and activation patterns across patients. Across patients, activation that emerged as a function of rehabilitation on the trained category included bilateral IFG, bilateral SFG, LMFG, and LPCG for picture naming; and bilateral IFG, bilateral MFG, LSFG, and bilateral MTG for semantic feature verification. Analysis of effective connectivity using Dynamic Causal Modeling (DCM) indicated that LIFG was the consistently significantly modulated region after rehabilitation across participants. These results indicate that language networks in patients with aphasia resemble normal language control networks and that this similarity is accentuated by rehabilitation. PMID:26106314
EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

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

2015-01-16

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution,more » diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less
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.
Temporally graded semantic memory loss in amnesia and semantic dementia: Further evidence for opposite gradients.

PubMed

Estmacott, Robyn W; Moscovitch, Morris

2002-03-01

The consolidation theory of long-term memory (e.g., Squire, 1992) predicts that damage to the medial temporal lobes will result in temporally graded retrograde memory loss, with a disproportionate impairment of recent relative to remote knowledge; in contrast, severe atrophy of the temporal neocortex is predicted to result in the reverse temporally graded pattern, with a selective sparing of recent memory (K.S. Graham & Hodges, 1997). Previously, we reported evidence that autobiographical episodic memory does not follow this temporal pattern (Westmacott, Leach, Freedman, & Moscovitch, 2001). In the present study, we found evidence suggesting that semantic memory loss does follow the predicted temporal pattern. We used a set of tasks that tap implicit and explicit memory for famous names and English vocabulary terms from across the 20th century. KC, a person with medial temporal amnesia, consistently demonstrated across tasks a selective deficit for famous names and vocabulary terms from the 5-year period just prior to injury; this deficit was particularly profound for elaborated semantic knowledge (e.g., word definitions, occupation of famous person). However, when asked to guess on unfamiliar items, KC's performance for names and words from this 5-year time period increased substantially, suggesting that he retains some of this knowledge at an implicit or rudimentary level. Conversely, EL, a semantic dementia patient with temporal neocortical atrophy and relative sparing of the medial temporal lobe, demonstrated a selective sparing of names and words from the most recent time period. However, this selective sparing of recent semantic memory was demonstrated in the implicit tasks only; performance on explicit tasks suggested an equally severe impairment of semantics across all time periods. Unlike the data from our previous study of autobiographical episodic memory, these findings are consistent with the predictions both of consolidation theory (Hodges & Graham, 1998; Squire, 1992) and multiple trace theory (Nadel & Moscovitch, 1999) that the hippocampus plays a timelimited role in the acquisition and representation of long-term semantic memories. Moreover, our findings suggest that tasks requiring minimal verbal production and explicit recall may provide a more sensitive and comprehensive assessment of intact memory capacity in brain-damaged individuals.
A journey to Semantic Web query federation in the life sciences.

PubMed

Cheung, Kei-Hoi; Frost, H Robert; Marshall, M Scott; Prud'hommeaux, Eric; Samwald, Matthias; Zhao, Jun; Paschke, Adrian

2009-10-01

As interest in adopting the Semantic Web in the biomedical domain continues to grow, Semantic Web technology has been evolving and maturing. A variety of technological approaches including triplestore technologies, SPARQL endpoints, Linked Data, and Vocabulary of Interlinked Datasets have emerged in recent years. In addition to the data warehouse construction, these technological approaches can be used to support dynamic query federation. As a community effort, the BioRDF task force, within the Semantic Web for Health Care and Life Sciences Interest Group, is exploring how these emerging approaches can be utilized to execute distributed queries across different neuroscience data sources. We have created two health care and life science knowledge bases. We have explored a variety of Semantic Web approaches to describe, map, and dynamically query multiple datasets. We have demonstrated several federation approaches that integrate diverse types of information about neurons and receptors that play an important role in basic, clinical, and translational neuroscience research. Particularly, we have created a prototype receptor explorer which uses OWL mappings to provide an integrated list of receptors and executes individual queries against different SPARQL endpoints. We have also employed the AIDA Toolkit, which is directed at groups of knowledge workers who cooperatively search, annotate, interpret, and enrich large collections of heterogeneous documents from diverse locations. We have explored a tool called "FeDeRate", which enables a global SPARQL query to be decomposed into subqueries against the remote databases offering either SPARQL or SQL query interfaces. Finally, we have explored how to use the vocabulary of interlinked Datasets (voiD) to create metadata for describing datasets exposed as Linked Data URIs or SPARQL endpoints. We have demonstrated the use of a set of novel and state-of-the-art Semantic Web technologies in support of a neuroscience query federation scenario. We have identified both the strengths and weaknesses of these technologies. While Semantic Web offers a global data model including the use of Uniform Resource Identifiers (URI's), the proliferation of semantically-equivalent URI's hinders large scale data integration. Our work helps direct research and tool development, which will be of benefit to this community.
A journey to Semantic Web query federation in the life sciences

PubMed Central

Cheung, Kei-Hoi; Frost, H Robert; Marshall, M Scott; Prud'hommeaux, Eric; Samwald, Matthias; Zhao, Jun; Paschke, Adrian

2009-01-01

Background As interest in adopting the Semantic Web in the biomedical domain continues to grow, Semantic Web technology has been evolving and maturing. A variety of technological approaches including triplestore technologies, SPARQL endpoints, Linked Data, and Vocabulary of Interlinked Datasets have emerged in recent years. In addition to the data warehouse construction, these technological approaches can be used to support dynamic query federation. As a community effort, the BioRDF task force, within the Semantic Web for Health Care and Life Sciences Interest Group, is exploring how these emerging approaches can be utilized to execute distributed queries across different neuroscience data sources. Methods and results We have created two health care and life science knowledge bases. We have explored a variety of Semantic Web approaches to describe, map, and dynamically query multiple datasets. We have demonstrated several federation approaches that integrate diverse types of information about neurons and receptors that play an important role in basic, clinical, and translational neuroscience research. Particularly, we have created a prototype receptor explorer which uses OWL mappings to provide an integrated list of receptors and executes individual queries against different SPARQL endpoints. We have also employed the AIDA Toolkit, which is directed at groups of knowledge workers who cooperatively search, annotate, interpret, and enrich large collections of heterogeneous documents from diverse locations. We have explored a tool called "FeDeRate", which enables a global SPARQL query to be decomposed into subqueries against the remote databases offering either SPARQL or SQL query interfaces. Finally, we have explored how to use the vocabulary of interlinked Datasets (voiD) to create metadata for describing datasets exposed as Linked Data URIs or SPARQL endpoints. Conclusion We have demonstrated the use of a set of novel and state-of-the-art Semantic Web technologies in support of a neuroscience query federation scenario. We have identified both the strengths and weaknesses of these technologies. While Semantic Web offers a global data model including the use of Uniform Resource Identifiers (URI's), the proliferation of semantically-equivalent URI's hinders large scale data integration. Our work helps direct research and tool development, which will be of benefit to this community. PMID:19796394
Improvements to the Ontology-based Metadata Portal for Unified Semantics (OlyMPUS)

NASA Astrophysics Data System (ADS)

Linsinbigler, M. A.; Gleason, J. L.; Huffer, E.

2016-12-01

The Ontology-based Metadata Portal for Unified Semantics (OlyMPUS), funded by the NASA Earth Science Technology Office Advanced Information Systems Technology program, is an end-to-end system designed to support Earth Science data consumers and data providers, enabling the latter to register data sets and provision them with the semantically rich metadata that drives the Ontology-Driven Interactive Search Environment for Earth Sciences (ODISEES). OlyMPUS complements the ODISEES' data discovery system with an intelligent tool to enable data producers to auto-generate semantically enhanced metadata and upload it to the metadata repository that drives ODISEES. Like ODISEES, the OlyMPUS metadata provisioning tool leverages robust semantics, a NoSQL database and query engine, an automated reasoning engine that performs first- and second-order deductive inferencing, and uses a controlled vocabulary to support data interoperability and automated analytics. The ODISEES data discovery portal leverages this metadata to provide a seamless data discovery and access experience for data consumers who are interested in comparing and contrasting the multiple Earth science data products available across NASA data centers. Olympus will support scientists' services and tools for performing complex analyses and identifying correlations and non-obvious relationships across all types of Earth System phenomena using the full spectrum of NASA Earth Science data available. By providing an intelligent discovery portal that supplies users - both human users and machines - with detailed information about data products, their contents and their structure, ODISEES will reduce the level of effort required to identify and prepare large volumes of data for analysis. This poster will explain how OlyMPUS leverages deductive reasoning and other technologies to create an integrated environment for generating and exploiting semantically rich metadata.
Enrichment and Ranking of the YouTube Tag Space and Integration with the Linked Data Cloud

NASA Astrophysics Data System (ADS)

Choudhury, Smitashree; Breslin, John G.; Passant, Alexandre

The increase of personal digital cameras with video functionality and video-enabled camera phones has increased the amount of user-generated videos on the Web. People are spending more and more time viewing online videos as a major source of entertainment and "infotainment". Social websites allow users to assign shared free-form tags to user-generated multimedia resources, thus generating annotations for objects with a minimum amount of effort. Tagging allows communities to organise their multimedia items into browseable sets, but these tags may be poorly chosen and related tags may be omitted. Current techniques to retrieve, integrate and present this media to users are deficient and could do with improvement. In this paper, we describe a framework for semantic enrichment, ranking and integration of web video tags using Semantic Web technologies. Semantic enrichment of folksonomies can bridge the gap between the uncontrolled and flat structures typically found in user-generated content and structures provided by the Semantic Web. The enhancement of tag spaces with semantics has been accomplished through two major tasks: (1) a tag space expansion and ranking step; and (2) through concept matching and integration with the Linked Data cloud. We have explored social, temporal and spatial contexts to enrich and extend the existing tag space. The resulting semantic tag space is modelled via a local graph based on co-occurrence distances for ranking. A ranked tag list is mapped and integrated with the Linked Data cloud through the DBpedia resource repository. Multi-dimensional context filtering for tag expansion means that tag ranking is much easier and it provides less ambiguous tag to concept matching.
You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes.

PubMed

Sadeghi, Zahra; McClelland, James L; Hoffman, Paul

2015-09-01

An influential position in lexical semantics holds that semantic representations for words can be derived through analysis of patterns of lexical co-occurrence in large language corpora. Firth (1957) famously summarised this principle as "you shall know a word by the company it keeps". We explored whether the same principle could be applied to non-verbal patterns of object co-occurrence in natural scenes. We performed latent semantic analysis (LSA) on a set of photographed scenes in which all of the objects present had been manually labelled. This resulted in a representation of objects in a high-dimensional space in which similarity between two objects indicated the degree to which they appeared in similar scenes. These representations revealed similarities among objects belonging to the same taxonomic category (e.g., items of clothing) as well as cross-category associations (e.g., between fruits and kitchen utensils). We also compared representations generated from this scene dataset with two established methods for elucidating semantic representations: (a) a published database of semantic features generated verbally by participants and (b) LSA applied to a linguistic corpus in the usual fashion. Statistical comparisons of the three methods indicated significant association between the structures revealed by each method, with the scene dataset displaying greater convergence with feature-based representations than did LSA applied to linguistic data. The results indicate that information about the conceptual significance of objects can be extracted from their patterns of co-occurrence in natural environments, opening the possibility for such data to be incorporated into existing models of conceptual representation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
Toward Semantic Interoperability in Home Health Care: Formally Representing OASIS Items for Integration into a Concept-oriented Terminology

PubMed Central

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

2005-01-01

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

PubMed

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

2005-01-01

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

PubMed

Fargier, Raphaël; Laganaro, Marina

2017-03-01

Picture naming tasks are largely used to elicit the production of specific words and sentences in psycholinguistic and neuroimaging research. However, the generation of lexical concepts from a visual input is clearly not the exclusive way speech production is triggered. In inferential speech encoding, the concept is not provided from a visual input, but is elaborated though semantic and/or episodic associations. It is therefore likely that the cognitive operations leading to lexical selection and word encoding are different in inferential and referential expressive language. In particular, in picture naming lexical selection might ensue from a simple association between a perceptual visual representation and a word with minimal semantic processes, whereas richer semantic associations are involved in lexical retrieval in inferential situations. Here we address this hypothesis by analyzing ERP correlates during word production in a referential and an inferential task. The participants produced the same words elicited from pictures or from short written definitions. The two tasks displayed similar electrophysiological patterns only in the time-period preceding the verbal response. In the stimulus-locked ERPs waveform amplitudes and periods of stable global electrophysiological patterns differed across tasks after the P100 component and until 400-500 ms, suggesting the involvement of different, task-specific neural networks. Based on the analysis of the time-windows affected by specific semantic and lexical variables in each task, we conclude that lexical selection is underpinned by a different set of conceptual and brain processes, with semantic processes clearly preceding word retrieval in naming from definition whereas the semantic information is enriched in parallel with word retrieval in picture naming.
The ClearEarth Project: Preliminary Findings from Experiments in Applying the CLEARTK NLP Pipeline and Annotation Tools Developed for Biomedicine to the Earth Sciences

NASA Astrophysics Data System (ADS)

Duerr, R.; Thessen, A.; Jenkins, C. J.; Palmer, M.; Myers, S.; Ramdeen, S.

2016-12-01

The ability to quickly find, easily use and effortlessly integrate data from a variety of sources is a grand challenge in Earth sciences, one around which entire research programs have been built. A myriad of approaches to tackling components of this challenge have been demonstrated, often with some success. Yet finding, assessing, accessing, using and integrating data remains a major challenge for many researchers. A technology that has shown promise in nearly every aspect of the challenge is semantics. Semantics has been shown to improve data discovery, facilitate assessment of a data set, and through adoption of the W3C's Linked Data Platform to have improved data integration and use at least for data amenable to that paradigm. Yet the creation of semantic resources has been slow. Why? Amongst a plethora of other reasons, it is because semantic expertise is rare in the Earth and Space sciences; the creation of semantic resources for even a single discipline is labor intensive and requires agreement within the discipline; best practices, methods and tools for supporting the creation and maintenance of the resources generated are in flux; and the human and financial capital needed are rarely available in the Earth sciences. However, other fields, such as biomedicine, have made considerable progress in these areas. The NSF-funded ClearEarth project is adapting the methods and tools from these communities for the Earth sciences in the expectation that doing so will enhance progress and the rate at which the needed semantic resources are created. We discuss progress and results to date, lessons learned from this adaptation process, and describe our upcoming efforts to extend this knowledge to the next generation of Earth and data scientists.
Towards Semantic Web Services on Large, Multi-Dimensional Coverages

NASA Astrophysics Data System (ADS)

Baumann, P.

2009-04-01

Observed and simulated data in the Earth Sciences often come as coverages, the general term for space-time varying phenomena as set forth by standardization bodies like the Open GeoSpatial Consortium (OGC) and ISO. Among such data are 1-d time series, 2-D surface data, 3-D surface data time series as well as x/y/z geophysical and oceanographic data, and 4-D metocean simulation results. With increasing dimensionality the data sizes grow exponentially, up to Petabyte object sizes. Open standards for exploiting coverage archives over the Web are available to a varying extent. The OGC Web Coverage Service (WCS) standard defines basic extraction operations: spatio-temporal and band subsetting, scaling, reprojection, and data format encoding of the result - a simple interoperable interface for coverage access. More processing functionality is available with products like Matlab, Grid-type interfaces, and the OGC Web Processing Service (WPS). However, these often lack properties known as advantageous from databases: declarativeness (describe results rather than the algorithms), safe in evaluation (no request can keep a server busy infinitely), and optimizable (enable the server to rearrange the request so as to produce the same result faster). WPS defines a geo-enabled SOAP interface for remote procedure calls. This allows to webify any program, but does not allow for semantic interoperability: a function is identified only by its function name and parameters while the semantics is encoded in the (only human readable) title and abstract. Hence, another desirable property is missing, namely an explicit semantics which allows for machine-machine communication and reasoning a la Semantic Web. The OGC Web Coverage Processing Service (WCPS) language, which has been adopted as an international standard by OGC in December 2008, defines a flexible interface for the navigation, extraction, and ad-hoc analysis of large, multi-dimensional raster coverages. It is abstract in that it does not anticipate any particular protocol. One such protocol is given by the OGC Web Coverage Service (WCS) Processing Extension standard which ties WCPS into WCS. Another protocol which makes WCPS an OGC Web Processing Service (WPS) Profile is under preparation. Thereby, WCPS bridges WCS and WPS. The conceptual model of WCPS relies on the coverage model of WCS, which in turn is based on ISO 19123. WCS currently addresses raster-type coverages where a coverage is seen as a function mapping points from a spatio-temporal extent (its domain) into values of some cell type (its range). A retrievable coverage has an identifier associated, further the CRSs supported and, for each range field (aka band, channel), the interpolation methods applicable. The WCPS language offers access to one or several such coverages via a functional, side-effect free language. The following example, which derives the NDVI (Normalized Difference Vegetation Index) from given coverages C1, C2, and C3 within the regions identified by the binary mask R, illustrates the language concept: for c in ( C1, C2, C3 ), r in ( R ) return encode( (char) (c.nir - c.red) / (c.nir + c.red), H˜DF-EOS\\~ ) The result is a list of three HDF-EOS encoded images containing masked NDVI values. Note that the same request can operate on coverages of any dimensionality. The expressive power of WCPS includes statistics, image, and signal processing up to recursion, to maintain safe evaluation. As both syntax and semantics of any WCPS expression is well known the language is Semantic Web ready: clients can construct WCPS requests on the fly, servers can optimize such requests (this has been investigated extensively with the rasdaman raster database system) and automatically distribute them for processing in a WCPS-enabled computing cloud. The WCPS Reference Implementation is being finalized now that the standard is stable; it will be released in open source once ready. Among the future tasks is to extend WCPS to general meshes, in synchronization with the WCS standard. In this talk WCPS is presented in the context of OGC standardization. The author is co-chair of OGC's WCS Working Group (WG) and Coverages WG.
Cross-language activation of morphological relatives in cognates: the role of orthographic overlap and task-related processing

PubMed Central

Mulder, Kimberley; Dijkstra, Ton; Baayen, R. Harald

2015-01-01

We considered the role of orthography and task-related processing mechanisms in the activation of morphologically related complex words during bilingual word processing. So far, it has only been shown that such morphologically related words (i.e., morphological family members) are activated through the semantic and morphological overlap they share with the target word. In this study, we investigated family size effects in Dutch-English identical cognates (e.g., tent in both languages), non-identical cognates (e.g., pil and pill, in English and Dutch, respectively), and non-cognates (e.g., chicken in English). Because of their cross-linguistic overlap in orthography, reading a cognate can result in activation of family members both languages. Cognates are therefore well-suited for studying mechanisms underlying bilingual activation of morphologically complex words. We investigated family size effects in an English lexical decision task and a Dutch-English language decision task, both performed by Dutch-English bilinguals. English lexical decision showed a facilitatory effect of English and Dutch family size on the processing of English-Dutch cognates relative to English non-cognates. These family size effects were not dependent on cognate type. In contrast, for language decision, in which a bilingual context is created, Dutch and English family size effects were inhibitory. Here, the combined family size of both languages turned out to better predict reaction time than the separate family size in Dutch or English. Moreover, the combined family size interacted with cognate type: the response to identical cognates was slowed by morphological family members in both languages. We conclude that (1) family size effects are sensitive to the task performed on the lexical items, and (2) depend on both semantic and formal aspects of bilingual word processing. We discuss various mechanisms that can explain the observed family size effects in a spreading activation framework. PMID:25698953
Headgear Accessories Classification Using an Overhead Depth Sensor

PubMed Central

Luna, Carlos A.; Marron-Romera, Marta; Mazo, Manuel; Luengo-Sanchez, Sara; Macho-Pedroso, Roberto

2017-01-01

In this paper, we address the generation of semantic labels describing the headgear accessories carried out by people in a scene under surveillance, only using depth information obtained from a Time-of-Flight (ToF) camera placed in an overhead position. We propose a new method for headgear accessories classification based on the design of a robust processing strategy that includes the estimation of a meaningful feature vector that provides the relevant information about the people’s head and shoulder areas. This paper includes a detailed description of the proposed algorithmic approach, and the results obtained in tests with persons with and without headgear accessories, and with different types of hats and caps. In order to evaluate the proposal, a wide experimental validation has been carried out on a fully labeled database (that has been made available to the scientific community), including a broad variety of people and headgear accessories. For the validation, three different levels of detail have been defined, considering a different number of classes: the first level only includes two classes (hat/cap, and no hat/cap), the second one considers three classes (hat, cap and no hat/cap), and the last one includes the full class set with the five classes (no hat/cap, cap, small size hat, medium size hat, and large size hat). The achieved performance is satisfactory in every case: the average classification rates for the first level reaches 95.25%, for the second one is 92.34%, and for the full class set equals 84.60%. In addition, the online stage processing time is 5.75 ms per frame in a standard PC, thus allowing for real-time operation. PMID:28796177
Discovering discovery patterns with Predication-based Semantic Indexing.

PubMed

Cohen, Trevor; Widdows, Dominic; Schvaneveldt, Roger W; Davies, Peter; Rindflesch, Thomas C

2012-12-01

In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as "discovery patterns", such as "drug x INHIBITS substance y, substance y CAUSES disease z" that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues. Copyright © 2012 Elsevier Inc. All rights reserved.
Discovering discovery patterns with predication-based Semantic Indexing

PubMed Central

Cohen, Trevor; Widdows, Dominic; Schvaneveldt, Roger W.; Davies, Peter; Rindflesch, Thomas C.

2012-01-01

In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as “discovery patterns”, such as “drug x INHIBITS substance y, substance y CAUSES disease z” that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues. PMID:22841748

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