Science.gov

Sample records for latent semantic indexing

  1. Indexing by Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Deerwester, Scott; And Others

    1990-01-01

    Describes a new method for automatic indexing and retrieval called latent semantic indexing (LSI). Problems with matching query words with document words in term-based information retrieval systems are discussed, semantic structure is examined, singular value decomposition (SVD) is explained, and the mathematics underlying the SVD model is…

  2. Latent Semantic Indexing of medical diagnoses using UMLS semantic structures.

    PubMed Central

    Chute, C. G.; Yang, Y.; Evans, D. A.

    1991-01-01

    The relational files within the UMLS Metathesaurus contain rich semantic associations to main concepts. We invoked the technique of Latent Semantic Indexing to generate information matrices based on these relationships and created "semantic vectors" using singular value decomposition. Evaluations were made on the complete set and subsets of Metathesaurus main concepts with the semantic type "Disease or Syndrome." Real number matrices were created with main concepts, lexical variants, synonyms, and associated expressions. Ancestors, children, siblings, and related terms were added to alternative matrices, preserving the hierarchical direction of the relation as the imaginary component of a complex number. Preliminary evaluation suggests that this technique is robust. A major advantage is the exploitation of semantic features which derive from a statistical decomposition of UMLS structures, possibly reducing dependence on the tedious construction of semantic frames by humans. PMID:1807584

  3. On updating problems in latent semantic indexing

    SciTech Connect

    Simon, H.D.; Zha, H.

    1997-11-01

    The authors develop new SVD-updating algorithms for three types of updating problems arising from Latent Semantic Indexing (LSI) for information retrieval to deal with rapidly changing text document collections. They also provide theoretical justification for using a reduced-dimension representation of the original document collection in the updating process. Numerical experiments using several standard text document collections show that the new algorithms give higher (interpolated) average precisions than the existing algorithms and the retrieval accuracy is comparable to that obtained using the complete document collection.

  4. On updating problems in latent semantic indexing

    SciTech Connect

    Zha, H.; Simon, H.D.

    1999-10-01

    The authors develop new SVD-updating algorithms for three types of updating problems arising from latent semantic indexing (LSI) for information retrieval to deal with rapidly changing text document collections. They also provide theoretical justification for using a reduced-dimension representation of the original document collection in the updating process. Numerical experiments using several standard text document collections show that the new algorithms give higher (interpolated) average precisions that the existing algorithms, and the retrieval accuracy is comparable to that obtained using the complete document collection.

  5. Analyzing large-scale proteomics projects with latent semantic indexing.

    PubMed

    Klie, Sebastian; Martens, Lennart; Vizcaíno, Juan Antonio; Côté, Richard; Jones, Phil; Apweiler, Rolf; Hinneburg, Alexander; Hermjakob, Henning

    2008-01-01

    Since the advent of public data repositories for proteomics data, readily accessible results from high-throughput experiments have been accumulating steadily. Several large-scale projects in particular have contributed substantially to the amount of identifications available to the community. Despite the considerable body of information amassed, very few successful analyses have been performed and published on this data, leveling off the ultimate value of these projects far below their potential. A prominent reason published proteomics data is seldom reanalyzed lies in the heterogeneous nature of the original sample collection and the subsequent data recording and processing. To illustrate that at least part of this heterogeneity can be compensated for, we here apply a latent semantic analysis to the data contributed by the Human Proteome Organization's Plasma Proteome Project (HUPO PPP). Interestingly, despite the broad spectrum of instruments and methodologies applied in the HUPO PPP, our analysis reveals several obvious patterns that can be used to formulate concrete recommendations for optimizing proteomics project planning as well as the choice of technologies used in future experiments. It is clear from these results that the analysis of large bodies of publicly available proteomics data by noise-tolerant algorithms such as the latent semantic analysis holds great promise and is currently underexploited.

  6. IPSILON: incremental parsing for semantic indexing of latent concepts.

    PubMed

    Bae, Soo Hyun; Juang, Biing-Hwang

    2010-07-01

    A new framework for content-based image retrieval, which takes advantage of the source characterization property of a universal source coding scheme, is investigated. Based upon a new class of multidimensional incremental parsing algorithm, extended from the Lempel-Ziv incremental parsing code, the proposed method captures the occurrence pattern of visual elements from a given image. A linguistic processing technique, namely the latent semantic analysis (LSA) method, is then employed to identify associative ensembles of visual elements, which lay the foundation for intelligent visual information analysis. In 2-D applications, incremental parsing decomposes an image into elementary patches that are different from the conventional fixed square-block type patches. When used in compressive representations, it is amenable in schemes that do not rely on average distortion criteria, a methodology that is a departure from the conventional vector quantization. We call this methodology a parsed representation. In this article, we present our implementations of an image retrieval system, called IPSILON, with parsed representations induced by different perceptual distortion thresholds. We evaluate the effectiveness of the use of the parsed representations by comparing their performance with that of four image retrieval systems, one using the conventional vector quantization for visual information analysis under the same LSA paradigm, another using a method called SIMPLIcity which is based upon an image segmentation and integrated region matching, and the other two based upon query-by-semantic-example and query-by-visual-example. The first two of them were tested with 20,000 images of natural scenes, and the others were tested with a portion of the images. The experimental results show that the proposed parsed representation efficiently captures the salient features in visual images and the IPSILON systems outperform other systems in terms of retrieval precision and distortion

  7. Supervised non-negative matrix factorization based latent semantic image indexing

    NASA Astrophysics Data System (ADS)

    Liang, Dong; Yang, Jie; Chang, Yuchou

    2006-05-01

    A novel latent semantic indexing (LSI) approach for content-based image retrieval is presented in this paper. Firstly, an extension of non-negative matrix factorization (NMF) to supervised initialization is discussed. Then, supervised NMF is used in LSI to find the relationships between low-level features and high-level semantics. The retrieved results are compared with other approaches and a good performance is obtained.

  8. An Explanation of the Effectiveness of Latent Semantic Indexing by Means of a Bayesian Regression Model.

    ERIC Educational Resources Information Center

    Story, Roger E.

    1996-01-01

    Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…

  9. On matrices with low-rank-plus-shift structure: Partial SVD and latent semantic indexing

    SciTech Connect

    Zha, H.; Zhang, Z.

    1998-08-01

    The authors present a detailed analysis of matrices satisfying the so-called low-rank-plus-shift property in connection with the computation of their partial singular value decomposition. The application they have in mind is Latent Semantic Indexing for information retrieval where the term-document matrices generated from a text corpus approximately satisfy this property. The analysis is motivated by developing more efficient methods for computing and updating partial SVD of large term-document matrices and gaining deeper understanding of the behavior of the methods in the presence of noise.

  10. An evaluation of concept based latent semantic indexing for clinical information retrieval.

    PubMed Central

    Chute, C. G.; Yang, Y.

    1992-01-01

    Latent Semantic Indexing (LSI) of surgical case report text using ICD-9-CM procedure codes and index terms was evaluated. The precision-recall performance of this two-step matrix retrieval process was compared with the SMART Document retrieval system, surface word matching, and humanly assigned procedure codes. Human coding performed best, two-step LSI did less well than surface matching or SMART. This evaluation suggests that concept-based LSI may be compromised by its two-stage nature and its dependence upon a robust term database linked to main concepts. However, the potential elegance of partial- credit concept matching merits the continued evaluation of LSI for clinical case retrieval. PMID:1482949

  11. A Comparison of SVD, SVR, ADE and IRR for Latent Semantic Indexing

    NASA Astrophysics Data System (ADS)

    Zhang, Wen; Tang, Xijin; Yoshida, Taketoshi

    Recently, singular value decomposition (SVD) and its variants, which are singular value rescaling (SVR), approximation dimension equalization (ADE) and iterative residual rescaling (IRR), were proposed to conduct the job of latent semantic indexing (LSI). Although they are all based on linear algebraic method for tem-document matrix computation, which is SVD, the basic motivations behind them concerning LSI are different from each other. In this paper, a series of experiments are conducted to examine their effectiveness of LSI for the practical application of text mining, including information retrieval, text categorization and similarity measure. The experimental results demonstrate that SVD and SVR have better performances than other proposed LSI methods in the above mentioned applications. Meanwhile, ADE and IRR, because of the too much difference between their approximation matrix and original term-document matrix in Frobenius norm, can not derive good performances for text mining applications using LSI.

  12. A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing.

    PubMed

    Devarajan, Karthik; Wang, Guoli; Ebrahimi, Nader

    2015-04-01

    Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H, such that V ∼ W H. It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H. In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data.

  13. Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications.

    PubMed

    Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Maudsley, Stuart

    2013-01-01

    Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.

  14. Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Dumais, Susan T.

    2004-01-01

    Presents a literature review that covers the following topics related to Latent Semantic Analysis (LSA): (1) LSA overview; (2) applications of LSA, including information retrieval (IR), information filtering, cross-language retrieval, and other IR-related LSA applications; (3) modeling human memory, including the relationship of LSA to other…

  15. Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing

    PubMed Central

    2012-01-01

    Background Identification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the post-genomic era, determining protein localization based on biological experiments can be expensive and time-consuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthew's correlation coefficient (MCC) around 54%~70% and 0.250~0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies. Results In this study, first we propose PSLNuc (Protein Subcellular Localization prediction for Nucleus) for predicting nuclear localization in proteins. First, for feature representation, a protein is represented by gapped-dipeptides and the feature values are weighted by homology information from a smoothed position-specific scoring matrix. After that, we incorporate probabilistic latent semantic indexing (PLSI) for feature reduction. Finally, the reduced features are used as input for a support vector machine (SVM) classifier. In addition to PSLNuc, we further identify gapped-dipeptide signatures for putative NLSs and NESs

  16. Essay Assessment with Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Miller, Tristan

    2003-01-01

    Latent semantic analysis (LSA) is an automated, statistical technique for comparing the semantic similarity of words or documents. In this article, I examine the application of LSA to automated essay scoring. I compare LSA methods to earlier statistical methods for assessing essay quality, and critically review contemporary essay-scoring systems…

  17. Comparing and Combining the Effectiveness of Latent Semantic Indexing and the Ordinary Vector Space Model for Information Retrieval.

    ERIC Educational Resources Information Center

    Lochbaum, Karen E.; Streeter, Lynn A.

    1989-01-01

    Describes experiments that compared a new method for automatically analyzing semantic structures in text by statistical means with the standard vector space model. Findings indicate that combining both methods improved performance over either alone. The effects of other experimental variables on retrieval performance (term weighting, suffix…

  18. An Introduction to Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Landauer, Thomas K; Foltz, Peter W.; Laham, Darrell

    1998-01-01

    Offers an introduction to the theory and implementation of Latent Semantic Analysis (LSA), a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. Gives an overview of applications and modeling of human knowledge to which LSA has been applied. (SR)

  19. From paragraph to graph: Latent semantic analysis for information visualization

    PubMed Central

    Landauer, Thomas K.; Laham, Darrell; Derr, Marcia

    2004-01-01

    Most techniques for relating textual information rely on intellectually created links such as author-chosen keywords and titles, authority indexing terms, or bibliographic citations. Similarity of the semantic content of whole documents, rather than just titles, abstracts, or overlap of keywords, offers an attractive alternative. Latent semantic analysis provides an effective dimension reduction method for the purpose that reflects synonymy and the sense of arbitrary word combinations. However, latent semantic analysis correlations with human text-to-text similarity judgments are often empirically highest at ≈300 dimensions. Thus, two- or three-dimensional visualizations are severely limited in what they can show, and the first and/or second automatically discovered principal component, or any three such for that matter, rarely capture all of the relations that might be of interest. It is our conjecture that linguistic meaning is intrinsically and irreducibly very high dimensional. Thus, some method to explore a high dimensional similarity space is needed. But the 2.7 × 107 projections and infinite rotations of, for example, a 300-dimensional pattern are impossible to examine. We suggest, however, that the use of a high dimensional dynamic viewer with an effective projection pursuit routine and user control, coupled with the exquisite abilities of the human visual system to extract information about objects and from moving patterns, can often succeed in discovering multiple revealing views that are missed by current computational algorithms. We show some examples of the use of latent semantic analysis to support such visualizations and offer views on future needs. PMID:15037748

  20. Transforming Selected Concepts into Dimensions in Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Olmos, Ricardo; Jorge-Botana, Guillermo; León, José Antonio; Escudero, Inmaculada

    2014-01-01

    This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this…

  1. Multimodal visual dictionary learning via heterogeneous latent semantic sparse coding

    NASA Astrophysics Data System (ADS)

    Li, Chenxiao; Ding, Guiguang; Zhou, Jile; Guo, Yuchen; Liu, Qiang

    2014-11-01

    Visual dictionary learning as a crucial task of image representation has gained increasing attention. Specifically, sparse coding is widely used due to its intrinsic advantage. In this paper, we propose a novel heterogeneous latent semantic sparse coding model. The central idea is to bridge heterogeneous modalities by capturing their common sparse latent semantic structure so that the learned visual dictionary is able to describe both the visual and textual properties of training data. Experiments on both image categorization and retrieval tasks demonstrate that our model shows superior performance over several recent methods such as K-means and Sparse Coding.

  2. The Measurement of Textual Coherence with Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Foltz, Peter W.; Kintsch, Walter; Landauer, Thomas K

    1998-01-01

    Illustrates use of Latent Semantic Analysis (LSA) for predicting coherence through reanalyzing two studies that manipulated coherence of texts and assessed readers' comprehension. Finds that LSA predicts effects of text coherence on comprehension. Notes that LSA can be applied as an automated method that produces coherence predictions similar to…

  3. Effectiveness of Automated Chinese Sentence Scoring with Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Liao, Chen-Huei; Kuo, Bor-Chen; Pai, Kai-Chih

    2012-01-01

    Automated scoring by means of Latent Semantic Analysis (LSA) has been introduced lately to improve the traditional human scoring system. The purposes of the present study were to develop a LSA-based assessment system to evaluate children's Chinese sentence construction skills and to examine the effectiveness of LSA-based automated scoring function…

  4. Using Latent Semantic Analysis To Assess Knowledge: Some Technical Considerations.

    ERIC Educational Resources Information Center

    Rehder, Bob; Schreiner, M. E.; Wolfe, Michael B. W.; Laham, Darrell; Kintsch, Walter; Landauer, Thomas K

    1998-01-01

    Provides a technical analysis of the factors involved in the ability of latent semantic analysis to assess student knowledge (grading essays and matching students with appropriate instructional texts). Addresses the role of technical vocabulary, how long the student essays should be, and how one deals with the directionality of knowledge in the…

  5. A combined feature latent semantic model for scene classification

    NASA Astrophysics Data System (ADS)

    Jiang, Yue; Wang, Runsheng

    2009-10-01

    Due to vast growth of image databases, scene image classification methods have become increasingly important in computer vision areas. We propose a new scene image classification framework based on combined feature and a latent semantic model which is based on the Latent Dirichlet Allocation (LDA) in the statistical text literature. Here the model is applied to visual words representation for images. We use Gibbs sampling for parameter estimation and use several different numbers of topics at the same time to obtain the latent topic representation of images. We densely extract multi-scale patches from images and get the combined feature on these patches. Our method is unsupervised. It can also well represent semantic characteristic of images. We demonstrate the effectiveness of our approach by comparing it to those used in previous work in this area. Experiments were conducted on three often used image databases, and our method got better results than the others.

  6. Latent Semantic Analysis of the Languages of Life

    NASA Astrophysics Data System (ADS)

    Rossi, Ryan Anthony

    We use Latent Semantic Analysis as a basis to study the languages of life. Using this approach we derive techniques to discover latent relationships between organisms such as significant motifs and evolutionary features. Doubly Singular Value Decomposition is defined and the significance of this adaptation is demonstrated by finding a phylogeny of twenty prokaryotes. Minimal Killer Words are used to define families of organisms from negative information. The application of these words makes it possible to automatically retrieve the coding frame of a sequence from any organism.

  7. Modeling semantic aspects for cross-media image indexing.

    PubMed

    Monay, Florent; Gatica-Perez, Daniel

    2007-10-01

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

  8. Massively Parallel Latent Semantic Analyzes using a Graphics Processing Unit

    SciTech Connect

    Cavanagh, Joseph M; Cui, Xiaohui

    2009-01-01

    Latent Semantic Indexing (LSA) aims to reduce the dimensions of large Term-Document datasets using Singular Value Decomposition. However, with the ever expanding size of data sets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. The Graphics Processing Unit (GPU) can solve some highly parallel problems much faster than the traditional sequential processor (CPU). Thus, a deployable system using a GPU to speedup large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a computer cluster. Due to the GPU s application-specific architecture, harnessing the GPU s computational prowess for LSA is a great challenge. We present a parallel LSA implementation on the GPU, using NVIDIA Compute Unified Device Architecture and Compute Unified Basic Linear Algebra Subprograms. The performance of this implementation is compared to traditional LSA implementation on CPU using an optimized Basic Linear Algebra Subprograms library. After implementation, we discovered that the GPU version of the algorithm was twice as fast for large matrices (1000x1000 and above) that had dimensions not divisible by 16. For large matrices that did have dimensions divisible by 16, the GPU algorithm ran five to six times faster than the CPU version. The large variation is due to architectural benefits the GPU has for matrices divisible by 16. It should be noted that the overall speeds for the CPU version did not vary from relative normal when the matrix dimensions were divisible by 16. Further research is needed in order to produce a fully implementable version of LSA. With that in mind, the research we presented shows that the GPU is a viable option for increasing the speed of LSA, in terms of cost/performance ratio.

  9. Latent semantic analysis: a new method to measure prose recall.

    PubMed

    Dunn, John C; Almeida, Osvaldo P; Barclay, Lee; Waterreus, Anna; Flicker, Leon

    2002-02-01

    The aim of this study was to compare traditional methods of scoring the Logical Memory test of the Wechsler Memory Scale-III with a new method based on Latent Semantic Analysis (LSA). LSA represents texts as vectors in a high-dimensional semantic space and the similarity of any two texts is measured by the cosine of the angle between their respective vectors. The Logical Memory test was administered to a sample of 72 elderly individuals, 14 of whom were classified as cognitively impaired by the Mini-Mental State Examination (MMSE). The results showed that LSA was at least as valid and sensitive as traditional measures. Partial correlations between prose recall measures and measures of cognitive function indicated that LSA explained all the relationship between Logical Memory and general cognitive function. This suggests that LSA may serve as an improved measure of prose recall.

  10. Probabilistic latent semantic analysis for dynamic textures recognition and localization

    NASA Astrophysics Data System (ADS)

    Wang, Yong; Hu, Shiqiang

    2014-11-01

    We present a framework for dynamic textures (DTs) recognition and localization by using a model developed in the text analysis literature: probabilistic latent semantic analysis (pLSA). The novelty is revealed in three aspects. First, chaotic feature vector is introduced and characterizes each pixel intensity series. Next, the pLSA model is employed to discover the topics by using the bag of words representation. Finally, the spatial layout of DTs can be found. Experimental results are conducted on the well-known DTs datasets. The results show that the proposed method can successfully build DTs models and achieve higher accuracies in DTs recognition and effectively localize DTs.

  11. Randomized Probabilistic Latent Semantic Analysis for Scene Recognition

    NASA Astrophysics Data System (ADS)

    Rodner, Erik; Denzler, Joachim

    The concept of probabilistic Latent Semantic Analysis (pLSA) has gained much interest as a tool for feature transformation in image categorization and scene recognition scenarios. However, a major issue of this technique is overfitting. Therefore, we propose to use an ensemble of pLSA models which are trained using random fractions of the training data. We analyze empirically the influence of the degree of randomization and the size of the ensemble on the overall classification performance of a scene recognition task. A thoughtful evaluation shows the benefits of this approach compared to a single pLSA model.

  12. Improving knowledge management systems with latent semantic analysis

    SciTech Connect

    Sebok, A.; Plott, C.; LaVoie, N.

    2006-07-01

    Latent Semantic Analysis (LSA) offers a technique for improving lessons learned and knowledge management systems. These systems are expected to become more widely used in the nuclear industry, as experienced personnel leave and are replaced by younger, less-experienced workers. LSA is a machine learning technology that allows searching of text based on meaning rather than predefined keywords or categories. Users can enter and retrieve data using their own words, rather than relying on constrained language lists or navigating an artificially structured database. LSA-based tools can greatly enhance the usability and usefulness of knowledge management systems and thus provide a valuable tool to assist nuclear industry personnel in gathering and transferring worker expertise. (authors)

  13. Application of Latent Semantic Analysis for Open-Ended Responses in a Large, Epidemiologic Study

    DTIC Science & Technology

    2011-10-05

    2006 questionnaire cycles were included in this study (n = 108,129). To perform these analyses, Latent Semantic Analysis (LSA) was applied to a broad...of topics, most notably illness/injury, exposure, and exercise. Conclusion: These findings suggest generalized topic areas, as well as identify...the 2001-2003 and 2004-2006 questionnaire cycles were included in this study (n = 108,129). To perform these analyses, Latent Semantic Analysis

  14. Parallel Latent Semantic Analysis using a Graphics Processing Unit

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E; Cavanagh, Joseph M

    2009-01-01

    Latent Semantic Analysis (LSA) can be used to reduce the dimensions of large Term-Document datasets using Singular Value Decomposition. However, with the ever expanding size of data sets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. The Graphics Processing Unit (GPU) can solve some highly parallel problems much faster than the traditional sequential processor (CPU). Thus, a deployable system using a GPU to speedup large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a computer cluster. In this paper, we presented a parallel LSA implementation on the GPU, using NVIDIA Compute Unified Device Architecture (CUDA) and Compute Unified Basic Linear Algebra Subprograms (CUBLAS). The performance of this implementation is compared to traditional LSA implementation on CPU using an optimized Basic Linear Algebra Subprograms library. For large matrices that have dimensions divisible by 16, the GPU algorithm ran five to six times faster than the CPU version.

  15. Enhancing multilingual latent semantic analysis with term alignment information.

    SciTech Connect

    Chew, Peter A.; Bader, Brett William

    2008-08-01

    Latent Semantic Analysis (LSA) is based on the Singular Value Decomposition (SVD) of a term-by-document matrix for identifying relationships among terms and documents from co-occurrence patterns. Among the multiple ways of computing the SVD of a rectangular matrix X, one approach is to compute the eigenvalue decomposition (EVD) of a square 2 x 2 composite matrix consisting of four blocks with X and XT in the off-diagonal blocks and zero matrices in the diagonal blocks. We point out that significant value can be added to LSA by filling in some of the values in the diagonal blocks (corresponding to explicit term-to-term or document-to-document associations) and computing a term-by-concept matrix from the EVD. For the case of multilingual LSA, we incorporate information on cross-language term alignments of the same sort used in Statistical Machine Translation (SMT). Since all elements of the proposed EVD-based approach can rely entirely on lexical statistics, hardly any price is paid for the improved empirical results. In particular, the approach, like LSA or SMT, can still be generalized to virtually any language(s); computation of the EVD takes similar resources to that of the SVD since all the blocks are sparse; and the results of EVD are just as economical as those of SVD.

  16. Assessing Reading Skill with a Think-Aloud Procedure and Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Magliano, Joseph P.; Millis, Keith K.

    2003-01-01

    Two studies examined the viability of assessing reading strategies using a think-aloud protocol combined with latent semantic analysis (LSA). Findings demonstrated that the responses of less-skilled readers semantically overlapped more with focal sentences than with causal antecedent sentences, whereas skilled readers' responses overlapped with…

  17. Measuring Discourse-Level Processes with Verbal Protocols and Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Millis, Keith; Magliano, Joseph; Todaro, Stacey

    2006-01-01

    The present study used latent semantic analysis (LSA) to analyze verbal protocols that were collected while participants read expository passages. In the study, participants were asked to type their thoughts after reading each sentence of 2 scientific texts. The semantic similarity between the protocols and the current sentence and prior causal…

  18. MASSIVELY PARALLEL LATENT SEMANTIC ANALYSES USING A GRAPHICS PROCESSING UNIT

    SciTech Connect

    Cavanagh, J.; Cui, S.

    2009-01-01

    Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using Singular Value Decomposition. However, with the ever-expanding size of datasets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. A graphics processing unit (GPU) can solve some highly parallel problems much faster than a traditional sequential processor or central processing unit (CPU). Thus, a deployable system using a GPU to speed up large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a PC cluster. Due to the GPU’s application-specifi c architecture, harnessing the GPU’s computational prowess for LSA is a great challenge. We presented a parallel LSA implementation on the GPU, using NVIDIA® Compute Unifi ed Device Architecture and Compute Unifi ed Basic Linear Algebra Subprograms software. The performance of this implementation is compared to traditional LSA implementation on a CPU using an optimized Basic Linear Algebra Subprograms library. After implementation, we discovered that the GPU version of the algorithm was twice as fast for large matrices (1 000x1 000 and above) that had dimensions not divisible by 16. For large matrices that did have dimensions divisible by 16, the GPU algorithm ran fi ve to six times faster than the CPU version. The large variation is due to architectural benefi ts of the GPU for matrices divisible by 16. It should be noted that the overall speeds for the CPU version did not vary from relative normal when the matrix dimensions were divisible by 16. Further research is needed in order to produce a fully implementable version of LSA. With that in mind, the research we presented shows that the GPU is a viable option for increasing the speed of LSA, in terms of cost/performance ratio.

  19. 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…

  20. Learning from Text: Matching Readers and Texts by Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Wolfe, Michael B. W.; Schreiner, M. E.; Rehder, Bob; Laham, Darrell; Foltz, Peter W.; Kintsch, Walter; Landauer, Thomas K

    1998-01-01

    Uses Latent Semantic Analysis (LSA) to predict how much readers would learn from texts based on the estimated conceptual match between their topic knowledge and the text information. Shows a nonmonotonic relationship in which learning was greatest for texts that were neither too easy nor too difficult. Finds LSA was as effective at predicting…

  1. Latent Semantic Analysis: A Theory of the Psychology of Language and Mind.

    ERIC Educational Resources Information Center

    Landauer, Thomas K.

    1999-01-01

    Contributes to communication theory and research by adding to a discussion of a computational model called latent semantic analysis (LSA). Argues that LSA does not handle all aspects of language processing, but offers a biologically and psychologically plausible mechanistic explanation of the acquisition, induction, and representation of verbal…

  2. More data trumps smarter algorithms: comparing pointwise mutual information with latent semantic analysis.

    PubMed

    Recchia, Gabriel; Jones, Michael N

    2009-08-01

    Computational models of lexical semantics, such as latent semantic analysis, can automatically generate semantic similarity measures between words from statistical redundancies in text. These measures are useful for experimental stimulus selection and for evaluating a model's cognitive plausibility as a mechanism that people might use to organize meaning in memory. Although humans are exposed to enormous quantities of speech, practical constraints limit the amount of data that many current computational models can learn from. We follow up on previous work evaluating a simple metric of pointwise mutual information. Controlling for confounds in previous work, we demonstrate that this metric benefits from training on extremely large amounts of data and correlates more closely with human semantic similarity ratings than do publicly available implementations of several more complex models. We also present a simple tool for building simple and scalable models from large corpora quickly and efficiently.

  3. Adapting Spectral Co-clustering to Documents and Terms Using Latent Semantic Analysis

    NASA Astrophysics Data System (ADS)

    Park, Laurence A. F.; Leckie, Christopher A.; Ramamohanarao, Kotagiri; Bezdek, James C.

    Spectral co-clustering is a generic method of computing co-clusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. We show that both spectral co-clustering and LSA follow the same process, using different normalisation schemes and metrics. By combining the properties of the two co-clustering methods, we obtain an improved co-clustering method for document-term relational data that provides an increase in the cluster quality of 33.0%.

  4. Latent morpho-semantic analysis : multilingual information retrieval with character n-grams and mutual information.

    SciTech Connect

    Bader, Brett William; Chew, Peter A.; Abdelali, Ahmed

    2008-08-01

    We describe an entirely statistics-based, unsupervised, and language-independent approach to multilingual information retrieval, which we call Latent Morpho-Semantic Analysis (LMSA). LMSA overcomes some of the shortcomings of related previous approaches such as Latent Semantic Analysis (LSA). LMSA has an important theoretical advantage over LSA: it combines well-known techniques in a novel way to break the terms of LSA down into units which correspond more closely to morphemes. Thus, it has a particular appeal for use with morphologically complex languages such as Arabic. We show through empirical results that the theoretical advantages of LMSA can translate into significant gains in precision in multilingual information retrieval tests. These gains are not matched either when a standard stemmer is used with LSA, or when terms are indiscriminately broken down into n-grams.

  5. Latent Semantics of Action Verbs Reflect Phonetic Parameters of Intensity and Emotional Content

    PubMed Central

    Petersen, Michael Kai

    2015-01-01

    Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from term document matrices made up of Wikipedia lines and Shakespeare plots, the resulting latent semantics capture not only the associative links which form concepts, but also spatial dimensions embedded within the surface structure of language. As both the shape and movements of objects have been found to be associated with phonetic contrasts already in toddlers, this study explores whether articulatory and acoustic parameters may likewise differentiate the latent semantics of action verbs. Selecting 3 × 20 emotion-, face-, and hand-related verbs known to activate premotor areas in the brain, their mutual cosine similarities were computed using latent semantic analysis LSA, and the resulting adjacency matrices were compared based on two different large scale text corpora: HAWIK and TASA. Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions. Transforming the verbs into their constituent phonemes, and projecting them into an articulatory space framed by tongue height and formant frequencies, the clustered small and large size movements appear differentiated by front versus back vowels corresponding to increasing levels of arousal. Whereas the clustered emotional verbs seem characterized by sequences of close versus open jaw produced phonemes, generating up- or downwards shifts in formant frequencies that may influence their perceived valence. Suggesting, that the latent semantics of action verbs reflect parameters of intensity and emotional polarity that appear correlated with the articulatory contrasts and acoustic characteristics of phonemes. PMID:25849977

  6. Latent semantics of action verbs reflect phonetic parameters of intensity and emotional content.

    PubMed

    Petersen, Michael Kai

    2015-01-01

    Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from term document matrices made up of Wikipedia lines and Shakespeare plots, the resulting latent semantics capture not only the associative links which form concepts, but also spatial dimensions embedded within the surface structure of language. As both the shape and movements of objects have been found to be associated with phonetic contrasts already in toddlers, this study explores whether articulatory and acoustic parameters may likewise differentiate the latent semantics of action verbs. Selecting 3 × 20 emotion-, face-, and hand-related verbs known to activate premotor areas in the brain, their mutual cosine similarities were computed using latent semantic analysis LSA, and the resulting adjacency matrices were compared based on two different large scale text corpora: HAWIK and TASA. Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions. Transforming the verbs into their constituent phonemes, and projecting them into an articulatory space framed by tongue height and formant frequencies, the clustered small and large size movements appear differentiated by front versus back vowels corresponding to increasing levels of arousal. Whereas the clustered emotional verbs seem characterized by sequences of close versus open jaw produced phonemes, generating up- or downwards shifts in formant frequencies that may influence their perceived valence. Suggesting, that the latent semantics of action verbs reflect parameters of intensity and emotional polarity that appear correlated with the articulatory contrasts and acoustic characteristics of phonemes.

  7. Semantic overlay network for large-scale spatial information indexing

    NASA Astrophysics Data System (ADS)

    Zou, Zhiqiang; Wang, Yue; Cao, Kai; Qu, Tianshan; Wang, Zhongmin

    2013-08-01

    The increased demand for online services of spatial information poses new challenges to the combined filed of Computer Science and Geographic Information Science. Amongst others, these include fast indexing of spatial data in distributed networks. In this paper we propose a novel semantic overlay network for large-scale multi-dimensional spatial information indexing, called SON_LSII, which has a hybrid structure integrating a semantic quad-tree and Chord ring. The SON_LSII is a small world overlay network that achieves a very competitive trade-off between indexing efficiency and maintenance overhead. To create SON_LSII, we use an effective semantic clustering strategy that considers two aspects, i.e., the semantic of spatial information that peer holds in overlay network and physical network performances. Based on SON_LSII, a mapping method is used to reduce the multi-dimensional features into a single dimension and an efficient indexing algorithm is presented to support complex range queries of the spatial information with a massive number of concurrent users. The results from extensive experiments demonstrate that SON_LSII is superior to existing overlay networks in various respects, including scalability, maintenance, rate of indexing hits, indexing logical hops, and adaptability. Thus, the proposed SON_LSII can be used for large-scale spatial information indexing.

  8. Simulating Expert Clinical Comprehension: Adapting Latent Semantic Analysis to Accurately Extract Clinical Concepts from Psychiatric Narrative

    PubMed Central

    Cohen, Trevor; Blatter, Brett; Patel, Vimla

    2008-01-01

    Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested. PMID:18455483

  9. A decision support system for fusion of hard and soft sensor information based on probabilistic latent semantic analysis technique

    NASA Astrophysics Data System (ADS)

    Shirkhodaie, Amir; Elangovan, Vinayak; Alkilani, Amjad; Habibi, Mohammad

    2013-05-01

    This paper presents an ongoing effort towards development of an intelligent Decision-Support System (iDSS) for fusion of information from multiple sources consisting of data from hard (physical sensors) and soft (textural sources. Primarily, this paper defines taxonomy of decision support systems for latent semantic data mining from heterogeneous data sources. A Probabilistic Latent Semantic Analysis (PLSA) approach is proposed for latent semantic concepts search from heterogeneous data sources. An architectural model for generating semantic annotation of multi-modality sensors in a modified Transducer Markup Language (TML) is described. A method for TML messages fusion is discussed for alignment and integration of spatiotemporally correlated and associated physical sensory observations. Lastly, the experimental results which exploit fusion of soft/hard sensor sources with support of iDSS are discussed.

  10. Semantic Lexicon Construction: Learning from Unlabeled Data via Spectral Analysis

    DTIC Science & Technology

    2004-01-01

    SVD). In this paper, we generally call such SVD- based subspace construction spectral analysis. Latent Semantic Indexing (LSI) (Deerwester et al...Richard Harshman. 1990. Indexing by Latent Semantic Analysis. Journal of the Society for Information Science, 41:391–407. A. Dempster, N. Laird, and D...and Knowledge Management. Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, and Santosh Vempala. 2000. Latent Semantic Indexing: A

  11. Computerizing reading training: evaluation of a latent semantic analysis space for science text.

    PubMed

    Kurby, Christopher A; Wiemer-Hastings, Katja; Ganduri, Nagasai; Magliano, Joseph P; Millis, Keith K; McNamara, Danielle S

    2003-05-01

    The effectiveness of a domain-specific latent semantic analysis (LSA) in assessing reading strategies was examined. Students were given self-explanation reading training (SERT) and asked to think aloud after each sentence in a science text. Novice and expert human raters and two LSA spaces (general reading, science) rated the similarity of each think-aloud protocol to benchmarks representing three different reading strategies (minimal, local, and global). The science LSA space correlated highly with human judgments, and more highly than did the general reading space. Also, cosines from the science LSA spaces can distinguish between different levels of semantic similarity, but may have trouble in distinguishing local processing protocols. Thus, a domain-specific LSA space is advantageous regardless of the size of the space. The results are discussed in the context of applying the science LSA to a computer-based version of SERT that gives online feedback based on LSA cosines.

  12. MEDTAG: tag-like semantics for medical document indexing.

    PubMed

    Ruch, P; Wagner, J; Bouillon, P; Baud, R H; Rassinoux, A M; Scherrer, J R

    1999-01-01

    Medical documentation is central in health care, as it constitutes the main means of communication between care providers. However, there is a gap to bridge between storing information and extracting the relevant underlying knowledge. We believe natural language processing (NLP) is the best solution to handle such a large amount of textual information. In this paper we describe the construction of a semantic tagset for medical document indexing purposes. Rather than attempting to produce a home-made tagset, we decided to use, as far as possible, standard medicine resources. This step has led us to choose UMLS hierarchical classes as a basis for our tagset. We also show that semantic tagging is not only providing bases for disambiguisation between senses, but is also useful in the query expansion process of the retrieval system. We finally focus on assessing the results of the semantic tagger.

  13. Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

    PubMed Central

    Gießler, Paul; Ohnesorge-Radtke, Ursula; Spreckelsen, Cord

    2015-01-01

    Background The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM’s semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms. Objective The purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM. Methods In this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged. Results The study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users. Conclusions It is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively. PMID:27731860

  14. Validating Quantitative Measurement Using Qualitative Data: Combining Rasch Scaling and Latent Semantic Analysis in Psychiatry

    NASA Astrophysics Data System (ADS)

    Lange, Rense

    2015-02-01

    An extension of concurrent validity is proposed that uses qualitative data for the purpose of validating quantitative measures. The approach relies on Latent Semantic Analysis (LSA) which places verbal (written) statements in a high dimensional semantic space. Using data from a medical / psychiatric domain as a case study - Near Death Experiences, or NDE - we established concurrent validity by connecting NDErs qualitative (written) experiential accounts with their locations on a Rasch scalable measure of NDE intensity. Concurrent validity received strong empirical support since the variance in the Rasch measures could be predicted reliably from the coordinates of their accounts in the LSA derived semantic space (R2 = 0.33). These coordinates also predicted NDErs age with considerable precision (R2 = 0.25). Both estimates are probably artificially low due to the small available data samples (n = 588). It appears that Rasch scalability of NDE intensity is a prerequisite for these findings, as each intensity level is associated (at least probabilistically) with a well- defined pattern of item endorsements.

  15. Predicting Raters’ Transparency Judgments of English and Chinese Morphological Constituents using Latent Semantic Analysis

    PubMed Central

    Wang, Hsueh-Cheng; Hsu, Li-Chuan; Tien, Yi-Min; Pomplun, Marc

    2013-01-01

    The morphological constituents of English compounds (e.g., “butter” and “fly” for “butterfly”) and two-character Chinese compounds may differ in meaning from the whole word. Subjective differences and ambiguity of transparency make the judgments difficult, and a computational alternative based on a general model may be a way to average across subjective differences. The current study proposes two approaches based on Latent Semantic Analysis (Landauer & Dumais, 1997): Model 1 compares the semantic similarity between a compound word and each of its constituents, and Model 2 derives the dominant meaning of a constituent based on a clustering analysis of morphological family members (e.g., “butterfingers” or “buttermilk” for “butter”). The proposed models successfully predicted participants’ transparency ratings, and we recommend that experimenters use Model 1 for English compounds and Model 2 for Chinese compounds, due to raters’ morphological processing in different writing systems. The dominance of lexical meaning, semantic transparency, and the average similarity between all pairs within a morphological family are provided, and practical applications for future studies are discussed. PMID:23784009

  16. The Use of Latent Semantic Analysis as a Tool for the Quantitative Assessment of Understanding and Knowledge.

    ERIC Educational Resources Information Center

    Shapiro, Amy M.; McNamara, Danielle S.

    2000-01-01

    Discusses latent semantic analysis (LSA), a statistical model for representing word usage in written language, and describes two experiments that were conducted with undergraduates to determine what aspect of knowledge, conceptual or factual, is being reflected in an LSA output from student essays. (Contains 21 references.)u (Author/LRW)

  17. A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.

    ERIC Educational Resources Information Center

    Landauer, Thomas K; Dumais, Susan T.

    1997-01-01

    A theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented to explain how people know as much as they do with the little information they get. LSA suggests that some domains of knowledge contain vast numbers of weak intercorrelations that amplify learning by inference. (SLD)

  18. Applying latent semantic analysis to large-scale medical image databases.

    PubMed

    Stathopoulos, Spyridon; Kalamboukis, Theodore

    2015-01-01

    Latent Semantic Analysis (LSA) although has been used successfully in text retrieval when applied to CBIR induces scalability issues with large image collections. The method so far has been used with small collections due to the high cost of storage and computational time for solving the SVD problem for a large and dense feature matrix. Here we present an effective and efficient approach of applying LSA skipping the SVD solution of the feature matrix and overcoming in this way the deficiencies of the method with large scale datasets. Early and late fusion techniques are tested and their performance is calculated. The study demonstrates that early fusion of several composite descriptors with visual words increase retrieval effectiveness. It also combines well in a late fusion for mixed (textual and visual) ad hoc and modality classification. The results reported are comparable to state of the art algorithms without including additional knowledge from the medical domain.

  19. Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis

    NASA Astrophysics Data System (ADS)

    Müller, Oliver; Schmiedel, Theresa; Gorbacheva, Elena; vom Brocke, Jan

    2016-01-01

    While researchers have analysed the organisational competences that are required for successful Business Process Management (BPM) initiatives, individual BPM competences have not yet been studied in detail. In this study, latent semantic analysis is used to examine a collection of 1507 BPM-related job advertisements in order to develop a typology of BPM professionals. This empirical analysis reveals distinct ideal types and profiles of BPM professionals on several levels of abstraction. A closer look at these ideal types and profiles confirms that BPM is a boundary-spanning field that requires interdisciplinary sets of competence that range from technical competences to business and systems competences. Based on the study's findings, it is posited that individual and organisational alignment with the identified ideal types and profiles is likely to result in high employability and organisational BPM success.

  20. Latent semantic analysis cosines as a cognitive similarity measure: Evidence from priming studies.

    PubMed

    Günther, Fritz; Dudschig, Carolin; Kaup, Barbara

    2016-01-01

    In distributional semantics models (DSMs) such as latent semantic analysis (LSA), words are represented as vectors in a high-dimensional vector space. This allows for computing word similarities as the cosine of the angle between two such vectors. In two experiments, we investigated whether LSA cosine similarities predict priming effects, in that higher cosine similarities are associated with shorter reaction times (RTs). Critically, we applied a pseudo-random procedure in generating the item material to ensure that we directly manipulated LSA cosines as an independent variable. We employed two lexical priming experiments with lexical decision tasks (LDTs). In Experiment 1 we presented participants with 200 different prime words, each paired with one unique target. We found a significant effect of cosine similarities on RTs. The same was true for Experiment 2, where we reversed the prime-target order (primes of Experiment 1 were targets in Experiment 2, and vice versa). The results of these experiments confirm that LSA cosine similarities can predict priming effects, supporting the view that they are psychologically relevant. The present study thereby provides evidence for qualifying LSA cosine similarities not only as a linguistic measure, but also as a cognitive similarity measure. However, it is also shown that other DSMs can outperform LSA as a predictor of priming effects.

  1. Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic analysis

    NASA Astrophysics Data System (ADS)

    Yi, Wenbin; Tang, Hong

    2009-10-01

    In this paper, we present a novel algorithm to classify UAV images through the image annotation which is a semi-supervised method. During the annotation process, we first divide whole image into different sizes of blocks and generate suitable visual words which are the K-means clustering centers or just pixels in small size image block. Then, given a set of image blocks for each semantic concept as training data, learning is based on the Probabilistic Latent Semantic Analysis (PLSA). The probability distributions of visual words in every document can be learned through the PLSA model. The labeling of every document (image block) is done by computing the similarity of its feature distribution to the distribution of the training documents with the Kullback-Leibler (K-L) divergence. Finally, the classification of the UAV images will be done by combining all the image blocks in every block size. The UAV images using in our experiments was acquired during Sichuan earthquake in 2008. The results show that smaller size block image will get better classification results.

  2. Comparison of Human and Latent Semantic Analysis (LSA) Judgements of Pairwise Document Similarities for a News Corpus

    DTIC Science & Technology

    2004-09-01

    documents. Previously, assessments of the similarity of the judgements of these techniques to those of people have been carried out on data sets where...Various local and global weighting schemes are available for LSA. These alter the level of correlation with human judgements as does the choice of the...number of singular values to use in the construction of the latent semantic space. The effects of the settings of these variables on the correlation of

  3. Annotations of Mexican bullfighting videos for semantic index

    NASA Astrophysics Data System (ADS)

    Montoya Obeso, Abraham; Oropesa Morales, Lester Arturo; Fernando Vázquez, Luis; Cocolán Almeda, Sara Ivonne; Stoian, Andrei; García Vázquez, Mireya Saraí; Zamudio Fuentes, Luis Miguel; Montiel Perez, Jesús Yalja; de la O Torres, Saul; Ramírez Acosta, Alejandro Alvaro

    2015-09-01

    The video annotation is important for web indexing and browsing systems. Indeed, in order to evaluate the performance of video query and mining techniques, databases with concept annotations are required. Therefore, it is necessary generate a database with a semantic indexing that represents the digital content of the Mexican bullfighting atmosphere. This paper proposes a scheme to make complex annotations in a video in the frame of multimedia search engine project. Each video is partitioned using our segmentation algorithm that creates shots of different length and different number of frames. In order to make complex annotations about the video, we use ELAN software. The annotations are done in two steps: First, we take note about the whole content in each shot. Second, we describe the actions as parameters of the camera like direction, position and deepness. As a consequence, we obtain a more complete descriptor of every action. In both cases we use the concepts of the TRECVid 2014 dataset. We also propose new concepts. This methodology allows to generate a database with the necessary information to create descriptors and algorithms capable to detect actions to automatically index and classify new bullfighting multimedia content.

  4. Exploring dangerous neighborhoods: Latent Semantic Analysis and computing beyond the bounds of the familiar

    PubMed Central

    Cohen, Trevor; Blatter, Brett; Patel, Vimla

    2005-01-01

    Certain applications require computer systems to approximate intended human meaning. This is achievable in constrained domains with a finite number of concepts. Areas such as psychiatry, however, draw on concepts from the world-at-large. A knowledge structure with broad scope is required to comprehend such domains. Latent Semantic Analysis (LSA) is an unsupervised corpus-based statistical method that derives quantitative estimates of the similarity between words and documents from their contextual usage statistics. The aim of this research was to evaluate the ability of LSA to derive meaningful associations between concepts relevant to the assessment of dangerousness in psychiatry. An expert reference model of dangerousness was used to guide the construction of a relevant corpus. Derived associations between words in the corpus were evaluated qualitatively. A similarity-based scoring function was used to assign dangerousness categories to discharge summaries. LSA was shown to derive intuitive relationships between concepts and correlated significantly better than random with human categorization of psychiatric discharge summaries according to dangerousness. The use of LSA to derive a simulated knowledge structure can extend the scope of computer systems beyond the boundaries of constrained conceptual domains. PMID:16779020

  5. 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.

  6. The semantic pathfinder: using an authoring metaphor for generic multimedia indexing.

    PubMed

    Snoek, Cees G M; Worring, Marcel; Geusebroek, Jan-Mark; Koelma, Dennis C; Seinstra, Frank J; Smeulders, Arnold W M

    2006-10-01

    This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives.

  7. 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.

  8. Context as the Building Blocks of Meaning: A Retrieval Model for the Semantic Representation of Words

    DTIC Science & Technology

    2003-04-01

    the question in a computational model called Latent Semantic Analysis (LSA). A word’s meaning in LSA is a vector describing the frequency with which...References 1. Landauer, T. K. & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of the acquisition...Dumais, S.T. (1994). Latent Semantic Indexing (LSI) and TREC-2. In D. Harnan (Ed.) The second text retrieval conference (TREC2) (National Institute of

  9. Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

    ERIC Educational Resources Information Center

    Leite, Walter L.; Stapleton, Laura M.

    2011-01-01

    In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

  10. Predication-based semantic indexing: permutations as a means to encode predications in semantic space.

    PubMed

    Cohen, Trevor; Schvaneveldt, Roger W; Rindflesch, Thomas C

    2009-11-14

    Corpus-derived distributional models of semantic distance between terms have proved useful in a number of applications. For both theoretical and practical reasons, it is desirable to extend these models to encode discrete concepts and the ways in which they are related to one another. In this paper, we present a novel vector space model that encodes semantic predications derived from MEDLINE by the SemRep system into a compact spatial representation. The associations captured by this method are of a different and complementary nature to those derived by traditional vector space models, and the encoding of predication types presents new possibilities for knowledge discovery and information retrieval.

  11. Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing.

    PubMed

    Fan, Jianping; Luo, Hangzai; Elmagarmid, Ahmed K

    2004-07-01

    Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.

  12. Visualization of semantic indexing similarity over MeSH.

    PubMed

    Du, Haixia; Yoo, Terry S

    2007-10-11

    We present an interactive visualization system for the evaluation of indexing results of the MEDLINE data-base over the Medical Subject Headings (MeSH) structure in a graphical radial-tree layout. It displays indexing similarity measurements with 2D color coding and a 3D height field permitting the evaluation of the automatic Medical Text Indexer (MTI), compared with human indexers.

  13. Semantic Indexing of Terrasar-X and in Situ Data for Urban Analytics

    NASA Astrophysics Data System (ADS)

    Espinoza Molina, D.; Alonso, K.; Datcu, M.

    2015-12-01

    This paper presents the semantic indexing of TerraSAR-X images and in situ data. Image processing together with machine learning methods, relevance feedback techniques, and human expertise are used to annotate the image content into a land use land cover catalogue. All the generated information is stored into a geo-database supporting the link between different types of information and the computation of queries and analytics. We used 11 TerraSAR-X scenes over Germany and LUCAS as in situ data. The semantic index is composed of about 73 land use land cover categories found in TerraSAR-X test dataset and 84 categories found in LUCAS dataset.

  14. 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

  15. Journal descriptor indexing tool for categorizing text according to discipline or semantic type.

    PubMed

    Humphrey, Susanne M; Lu, Chris J; Rogers, Willie J; Browne, Allen C

    2006-01-01

    A JDI (Journal Descriptor Indexing) tool has been developed at NLM that automatically categorizes biomedical text as input, returning a ranked list, with scores between 0-1, of either JDs (Journal Descriptors, corresponding to biomedical disciplines) or STs (UMLS Semantic Types). Possible applications include WSD (Word Sense Disambiguation) and retrieval according to discipline. The Lexical Systems Group plans to distribute an open source JAVA version of this tool.

  16. Semantic extraction and processing of medical records for patient-oriented visual index

    NASA Astrophysics Data System (ADS)

    Zheng, Weilin; Dong, Wenjie; Chen, Xiangjiao; Zhang, Jianguo

    2012-02-01

    To have comprehensive and completed understanding healthcare status of a patient, doctors need to search patient medical records from different healthcare information systems, such as PACS, RIS, HIS, USIS, as a reference of diagnosis and treatment decisions for the patient. However, it is time-consuming and tedious to do these procedures. In order to solve this kind of problems, we developed a patient-oriented visual index system (VIS) to use the visual technology to show health status and to retrieve the patients' examination information stored in each system with a 3D human model. In this presentation, we present a new approach about how to extract the semantic and characteristic information from the medical record systems such as RIS/USIS to create the 3D Visual Index. This approach includes following steps: (1) Building a medical characteristic semantic knowledge base; (2) Developing natural language processing (NLP) engine to perform semantic analysis and logical judgment on text-based medical records; (3) Applying the knowledge base and NLP engine on medical records to extract medical characteristics (e.g., the positive focus information), and then mapping extracted information to related organ/parts of 3D human model to create the visual index. We performed the testing procedures on 559 samples of radiological reports which include 853 focuses, and achieved 828 focuses' information. The successful rate of focus extraction is about 97.1%.

  17. Concept indexing and expansion for social multimedia websites based on semantic processing and graph analysis

    NASA Astrophysics Data System (ADS)

    Lin, Po-Chuan; Chen, Bo-Wei; Chang, Hangbae

    2016-07-01

    This study presents a human-centric technique for social video expansion based on semantic processing and graph analysis. The objective is to increase metadata of an online video and to explore related information, thereby facilitating user browsing activities. To analyze the semantic meaning of a video, shots and scenes are firstly extracted from the video on the server side. Subsequently, this study uses annotations along with ConceptNet to establish the underlying framework. Detailed metadata, including visual objects and audio events among the predefined categories, are indexed by using the proposed method. Furthermore, relevant online media associated with each category are also analyzed to enrich the existing content. With the above-mentioned information, users can easily browse and search the content according to the link analysis and its complementary knowledge. Experiments on a video dataset are conducted for evaluation. The results show that our system can achieve satisfactory performance, thereby demonstrating the feasibility of the proposed idea.

  18. Inferring the semantic relationships of words within an ontology using random indexing: applications to pharmacogenomics.

    PubMed

    Percha, Bethany; Altman, Russ B

    2013-01-01

    The biomedical literature presents a uniquely challenging text mining problem. Sentences are long and complex, the subject matter is highly specialized with a distinct vocabulary, and producing annotated training data for this domain is time consuming and expensive. In this environment, unsupervised text mining methods that do not rely on annotated training data are valuable. Here we investigate the use of random indexing, an automated method for producing vector-space semantic representations of words from large, unlabeled corpora, to address the problem of term normalization in sentences describing drugs and genes. We show that random indexing produces similarity scores that capture some of the structure of PHARE, a manually curated ontology of pharmacogenomics concepts. We further show that random indexing can be used to identify likely word candidates for inclusion in the ontology, and can help localize these new labels among classes and roles within the ontology.

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

    PubMed

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

    2011-07-01

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

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

    ERIC Educational Resources Information Center

    Anderson, James D.; Perez-Carballo, Jose

    2001-01-01

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

  1. Visualizing Semantic Spaces and Author Co-citation Networks in Digital Libraries.

    ERIC Educational Resources Information Center

    Chen, Chaomei

    1999-01-01

    Describes the development and application of visualization techniques for users to access and explore information in digital libraries effectively and intuitively. Salient semantic structures and citation patterns are extracted from several collections of documents using Latent Semantic Indexing and Pathfinder Network Scaling. Author cocitation…

  2. A Semantic Analysis of Abstracts Around an Experiment in Mechanized Indexing.

    ERIC Educational Resources Information Center

    Noel, Jacques

    The first part of this dissertation is a metatheoretical discussion of the needs and means of semantic analysis. This discussion includes sections on metalanguage, deep and surface structure and structural semantics, and procedures for relating the English of abstracts to a classification concordance in the same language. The second part describes…

  3. Nouns, verbs, objects, actions, and abstractions: Local fMRI activity indexes semantics, not lexical categories

    PubMed Central

    Moseley, Rachel L.; Pulvermüller, Friedemann

    2014-01-01

    Noun/verb dissociations in the literature defy interpretation due to the confound between lexical category and semantic meaning; nouns and verbs typically describe concrete objects and actions. Abstract words, pertaining to neither, are a critical test case: dissociations along lexical-grammatical lines would support models purporting lexical category as the principle governing brain organisation, whilst semantic models predict dissociation between concrete words but not abstract items. During fMRI scanning, participants read orthogonalised word categories of nouns and verbs, with or without concrete, sensorimotor meaning. Analysis of inferior frontal/insula, precentral and central areas revealed an interaction between lexical class and semantic factors with clear category differences between concrete nouns and verbs but not abstract ones. Though the brain stores the combinatorial and lexical-grammatical properties of words, our data show that topographical differences in brain activation, especially in the motor system and inferior frontal cortex, are driven by semantics and not by lexical class. PMID:24727103

  4. Nouns, verbs, objects, actions, and abstractions: local fMRI activity indexes semantics, not lexical categories.

    PubMed

    Moseley, Rachel L; Pulvermüller, Friedemann

    2014-05-01

    Noun/verb dissociations in the literature defy interpretation due to the confound between lexical category and semantic meaning; nouns and verbs typically describe concrete objects and actions. Abstract words, pertaining to neither, are a critical test case: dissociations along lexical-grammatical lines would support models purporting lexical category as the principle governing brain organisation, whilst semantic models predict dissociation between concrete words but not abstract items. During fMRI scanning, participants read orthogonalised word categories of nouns and verbs, with or without concrete, sensorimotor meaning. Analysis of inferior frontal/insula, precentral and central areas revealed an interaction between lexical class and semantic factors with clear category differences between concrete nouns and verbs but not abstract ones. Though the brain stores the combinatorial and lexical-grammatical properties of words, our data show that topographical differences in brain activation, especially in the motor system and inferior frontal cortex, are driven by semantics and not by lexical class.

  5. Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

    PubMed

    Uthayan, K R; Mala, G S Anandha

    2015-01-01

    Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

  6. Analysis of semantic search within the domains of uncertainty: using Keyword Effectiveness Indexing as an evaluation tool.

    PubMed

    Lorence, Daniel; Abraham, Joanna

    2006-01-01

    Medical and health-related searches pose a special case of risk when using the web as an information resource. Uninsured consumers, lacking access to a trained provider, will often rely on information from the internet for self-diagnosis and treatment. In areas where treatments are uncertain or controversial, most consumers lack the knowledge to make an informed decision. This exploratory technology assessment examines the use of Keyword Effectiveness Indexing (KEI) analysis as a potential tool for profiling information search and keyword retrieval patterns. Results demonstrate that the KEI methodology can be useful in identifying e-health search patterns, but is limited by semantic or text-based web environments.

  7. VastMM-Tag: Semantic Indexing and Browsing of Videos for E-Learning

    ERIC Educational Resources Information Center

    Morris, Mitchell J.

    2012-01-01

    Quickly accessing the contents of a video is challenging for users, particularly for unstructured video, which contains no intentional shot boundaries, no chapters, and no apparent edited format. We approach this problem in the domain of lecture videos though the use of machine learning, to gather semantic information about the videos; and through…

  8. GeoIRIS: Geospatial Information Retrieval and Indexing System-Content Mining, Semantics Modeling, and Complex Queries.

    PubMed

    Shyu, Chi-Ren; Klaric, Matt; Scott, Grant J; Barb, Adrian S; Davis, Curt H; Palaniappan, Kannappan

    2007-04-01

    Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Using these underpinnings, we have developed techniques for complex queries that merge information from heterogeneous geospatial databases, retrievals of objects based on shape and visual characteristics, analysis of multiobject relationships for the retrieval of objects in specific spatial configurations, and semantic models to link low-level image features with high-level visual descriptors. GeoIRIS brings this diverse set of technologies together into a coherent system with an aim of allowing image analysts to more rapidly identify relevant imagery. GeoIRIS is able to answer analysts' questions in seconds, such as "given a query image, show me database satellite images that have similar objects and spatial relationship that are within a certain radius of a landmark."

  9. Cross-language MeSH indexing using morpho-semantic normalization.

    PubMed

    Markó, Kornél; Daumke, Philipp; Schulz, Stefan; Hahn, Udo

    2003-01-01

    We consider three alternative procedures for the automatic indexing of medical documents using MeSH thesaurus identifiers as target units (document descriptors). Rather than considering complete words as the starting point of the indexing procedure, we here propose morphologically plausible subwords as basic units from which MeSH terms are derived. We describe the morphological segmentation and normalization procedures, as well as the mappings from subwords to MeSH terms, and discuss results from an evaluation carried out on a German-language corpus.

  10. Indexing method of digital audiovisual medical resources with semantic Web integration.

    PubMed

    Cuggia, Marc; Mougin, Fleur; Le Beux, Pierre

    2003-01-01

    Digitalization of audio-visual resources combined with the performances of the networks offer many possibilities which are the subject of intensive work in the scientific and industrial sectors. Indexing such resources is a major challenge. Recently, the Motion Pictures Expert Group (MPEG) has been developing MPEG-7, a standard for describing multimedia content. The good of this standard is to develop a rich set of standardized tools to enable fast efficient retrieval from digital archives or filtering audiovisual broadcasts on the internet. How this kind of technologies could be used in the medical context? In this paper, we propose a simpler indexing system, based on Dublin Core standard and complaint to MPEG-7. We use MeSH and UMLS to introduce conceptual navigation. We also present a video-platform with enables to encode and give access to audio-visual resources in streaming mode.

  11. Indexing method of digital audiovisual medical resources with semantic Web integration.

    PubMed

    Cuggia, Marc; Mougin, Fleur; Le Beux, Pierre

    2005-03-01

    Digitalization of audiovisual resources and network capability offer many possibilities which are the subject of intensive work in scientific and industrial sectors. Indexing such resources is a major challenge. Recently, the Motion Pictures Expert Group (MPEG) has developed MPEG-7, a standard for describing multimedia content. The goal of this standard is to develop a rich set of standardized tools to enable efficient retrieval from digital archives or the filtering of audiovisual broadcasts on the Internet. How could this kind of technology be used in the medical context? In this paper, we propose a simpler indexing system, based on the Dublin Core standard and compliant to MPEG-7. We use MeSH and the UMLS to introduce conceptual navigation. We also present a video-platform which enables encoding and gives access to audiovisual resources in streaming mode.

  12. Once Is Enough: N400 Indexes Semantic Integration of Novel Word Meanings from a Single Exposure in Context

    ERIC Educational Resources Information Center

    Borovsky, Arielle; Elman, Jeffrey L.; Kutas, Marta

    2012-01-01

    We investigated the impact of contextual constraint on the integration of novel word meanings into semantic memory. Adults read strongly or weakly constraining sentences ending in known or unknown (novel) words as scalp-recorded electrical brain activity was recorded. Word knowledge was assessed via a lexical decision task in which recently seen…

  13. Semantic and Social Networks Comparison for the Haiti Earthquake Relief Operations from APAN Data Sources using Lexical Link Analysis (LLA)

    DTIC Science & Technology

    2012-06-01

    Semantic Analysis (LSA; (Dumais, Furnas, Landauer, Deerwester, & Harshman, 1988; Gorman, 2003; Letsche, 1997) and Probabilistic Latent Semantic Analysis...Dumais, S. T., Furnas, G. W., Landauer, T. K., Deerwester, S., & Harshman, R. (1988). Using latent semantic analysis to improve information retrieval...analysis/ Gorman, J. C., Foltz, P. W. Kiekel, P. A., Martin, M. A. & Cooke, N. J. (2003) Evaluation of Latent Semantic Analysis-based measures of

  14. Semantic Tools in Information Retrieval.

    ERIC Educational Resources Information Center

    Rubinoff, Morris; Stone, Don C.

    This report discusses the problem of the meansings of words used in information retrieval systems, and shows how semantic tools can aid in the communication which takes place between indexers and searchers via index terms. After treating the differing use of semantic tools in different types of systems, two tools (classification tables and…

  15. Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

    ERIC Educational Resources Information Center

    Anaya, Leticia H.

    2011-01-01

    In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed.…

  16. Memory for Narrative and Expository Text: Independent Influences of Semantic Associations and Text Organization

    ERIC Educational Resources Information Center

    Wolfe, Michael B. W.

    2005-01-01

    The author examined memory for text in terms of the independent influences of semantic knowledge associations and text organization. Semantic associations were operationalized as the semantic relatedness between individual text concepts and the text as a whole and assessed with latent semantic analysis. The author assessed text organization by…

  17. Semantic Data Matching: Principles and Performance

    NASA Astrophysics Data System (ADS)

    Deaton, Russell; Doan, Thao; Schweiger, Tom

    Automated and real-time management of customer relationships requires robust and intelligent data matching across widespread and diverse data sources. Simple string matching algorithms, such as dynamic programming, can handle typographical errors in the data, but are less able to match records that require contextual and experiential knowledge. Latent Semantic Indexing (LSI) (Berry et al. ; Deerwester et al. is a machine intelligence technique that can match data based upon higher order structure, and is able to handle difficult problems, such as words that have different meanings but the same spelling, are synonymous, or have multiple meanings. Essentially, the technique matches records based upon context, or mathematically quantifying when terms occur in the same record.

  18. Longitudinal associations between body mass index, physical activity, and healthy dietary behaviors in adults: A parallel latent growth curve modeling approach

    PubMed Central

    Kim, Youngdeok; Lee, Jung-Min; Kim, Jungyoon; Dhurandhar, Emily; Soliman, Ghada; Wehbi, Nizar K.; Canedy, James

    2017-01-01

    Background Physical activity (PA) and healthy dietary behaviors (HDB) are two well-documented lifestyle factors influencing body mass index (BMI). This study examined 7-year longitudinal associations between changes in PA, HDB, and BMI among adults using a parallel latent growth curve modeling (LGCM). Methods We used prospective cohort data collected by a private company (SimplyWell LLC, Omaha, NE, USA) implementing a workplace health screening program. Data from a total of 2,579 adults who provided valid BMI, PA, and HDB information for at least 5 out of 7 follow-up years from the time they entered the program were analyzed. PA and HDB were subjectively measured during an annual online health survey. Height and weight measured during an annual onsite health screening were used to calculate BMI (kg·m2). The parallel LGCMs stratified by gender and baseline weight status (normal: BMI<25, overweight BMI 25–29.9, and obese: BMI>30) were fitted to examine the longitudinal associations of changes in PA and HDB with change in BMI over years. Results On average, BMI gradually increased over years, at rates ranging from 0.06 to 0.20 kg·m2·year, with larger increases observed among those of normal baseline weight status across genders. The increases in PA and HDB were independently associated with a smaller increase in BMI for obese males (b = -1.70 and -1.98, respectively), and overweight females (b = -1.85 and -2.46, respectively) and obese females (b = -2.78 and -3.08, respectively). However, no significant associations of baseline PA and HDB with changes in BMI were observed. Conclusions Our study suggests that gradual increases in PA and HDB are independently associated with smaller increases in BMI in overweight and obese adults, but not in normal weight individuals. Further study is warranted to address factors that check increases in BMI in normal weight adults. PMID:28296945

  19. A Semantic Relatedness Approach for Traceability Link Recovery

    SciTech Connect

    Mahmoud, Anas M.; Niu, Nan; Xu, Songhua

    2012-01-01

    Human analysts working with automated tracing tools need to directly vet candidate traceability links in order to determine the true traceability information. Currently, human intervention happens at the end of the traceability process, after candidate traceability links have already been generated. This often leads to a decline in the results accuracy. In this paper, we propose an approach, based on semantic relatedness (SR), which brings human judgment to an earlier stage of the tracing process by integrating it into the underlying retrieval mechanism. SR tries to mimic human mental model of relevance by considering a broad range of semantic relations, hence producing more semantically meaningful results. We evaluated our approach using three datasets from different application domains, and assessed the tracing results via six different performance measures concerning both result quality and browsability. The empirical evaluation results show that our SR approach achieves a significantly better performance in recovering true links than a standard Vector Space Model (VSM) in all datasets. Our approach also achieves a significantly better precision than Latent Semantic Indexing (LSI) in two of our datasets.

  20. Semantic Processing of Mathematical Gestures

    ERIC Educational Resources Information Center

    Lim, Vanessa K.; Wilson, Anna J.; Hamm, Jeff P.; Phillips, Nicola; Iwabuchi, Sarina J.; Corballis, Michael C.; Arzarello, Ferdinando; Thomas, Michael O. J.

    2009-01-01

    Objective: To examine whether or not university mathematics students semantically process gestures depicting mathematical functions (mathematical gestures) similarly to the way they process action gestures and sentences. Semantic processing was indexed by the N400 effect. Results: The N400 effect elicited by words primed with mathematical gestures…

  1. Learning multimodal latent attributes.

    PubMed

    Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

    2014-02-01

    The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular, we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multimodal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we 1) introduce a concept of semilatent attribute space, expressing user-defined and latent attributes in a unified framework, and 2) propose a novel scalable probabilistic topic model for learning multimodal semilatent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multitask learning, learning with label noise, N-shot transfer learning, and importantly zero-shot learning.

  2. Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory

    ERIC Educational Resources Information Center

    Gruenenfelder, Thomas M.; Recchia, Gabriel; Rubin, Tim; Jones, Michael N.

    2016-01-01

    We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network…

  3. Language networks associated with computerized semantic indices.

    PubMed

    Pakhomov, Serguei V S; Jones, David T; Knopman, David S

    2015-01-01

    Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory.

  4. Embedding of Semantic Predications.

    PubMed

    Cohen, Trevor; Widdows, Dominic

    2017-03-08

    This paper concerns the generation of distributed vector representations of biomedical concepts from structured knowledge, in the form of subject-relation-object triplets known as semantic predications. Specifically, we evaluate the extent to which a representational approach we have developed for this purpose previously, known as Predication-based Semantic Indexing (PSI), might benefit from insights gleaned from neural-probabilistic language models, which have enjoyed a surge in popularity in recent years as a means to generate distributed vector representations of terms from free text. To do so, we develop a novel neural-probabilistic approach to encoding predications, called Embedding of Semantic Predications (ESP), by adapting aspects of the Skipgram with Negative Sampling (SGNS) algorithm to this purpose. We compare ESP and PSI across a number of tasks including recovery of encoded information, estimation of semantic similarity and relatedness, and identification of potentially therapeutic and harmful relationships using both analogical retrieval and supervised learning. We find advantages for ESP in some, but not all of these tasks, revealing the contexts in which the additional computational work of neural-probabilistic modeling is justified.

  5. Capturing the Semantics of User Interaction: A Review and Case Study

    NASA Astrophysics Data System (ADS)

    Morrison, Donn; Marchand-Maillet, Stéphane; Bruno, Eric

    In many retrieval domains there exists a problematic gap between what computers can describe and what humans are capable of perceiving. This gap is most evident in the indexing of multimedia data such as images, video and sound where the low-level features are too semantically deficient to be of use from a typical users' perspective. On the other hand, users possess the ability to quickly examine and summarise these documents, even subconsciously. Examples include specifying relevance between a query and results, rating preferences in film databases, purchasing items from online retailers, and even browsing web sites. Data from these interactions, captured and stored in log files, can be interpreted to have semantic meaning, which proves indispensable when used in a collaborative setting where users share similar preferences or goals. In this chapter we summarise techniques for efficiently exploiting user interaction in its many forms for the generation and augmentation of semantic data in large databases. This user interaction can be applied to improve performance in recommender and information retrieval systems. A case study is presented which applies a popular technique, latent semantic analysis, to improve retrieval on an image database.

  6. Generative Semantics

    ERIC Educational Resources Information Center

    Bagha, Karim Nazari

    2011-01-01

    Generative semantics is (or perhaps was) a research program within linguistics, initiated by the work of George Lakoff, John R. Ross, Paul Postal and later McCawley. The approach developed out of transformational generative grammar in the mid 1960s, but stood largely in opposition to work by Noam Chomsky and his students. The nature and genesis of…

  7. Principal Semantic Components of Language and the Measurement of Meaning

    PubMed Central

    Samsonovic, Alexei V.; Ascoli, Giorgio A.

    2010-01-01

    Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of “good/bad” (valence), “calm/excited” (arousal), and “open/closed” (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number (∼4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a

  8. Generalized Latent Trait Models.

    ERIC Educational Resources Information Center

    Moustaki, Irini; Knott, Martin

    2000-01-01

    Discusses a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. Presents a unified maximum likelihood method for estimating the parameters of the generalized latent trait model and discusses the scoring of individuals on the latent dimensions.…

  9. Semantic Information Management Control of Mission Asset State Changes

    DTIC Science & Technology

    2014-06-01

    other being ontology instantiation ( OWL ). OWL can be seen as the semantic web equivalent of schemas to the standardized document object model. The real...unstructured relational fact defined without an OWL counterpart. Applying these observations to an operational, real world use case demonstrates the...semantically expressed via a set of indexers. The result of semantic processing is a semantic RDF/ OWL document that relates values for details

  10. Predicting Novel Human Gene Ontology Annotations Using Semantic Analysis

    PubMed Central

    Done, Bogdan; Khatri, Purvesh; Done, Arina; Draghici, Sorin

    2013-01-01

    The correct interpretation of many molecular biology experiments depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are meant to act as repositories for our biological knowledge as we acquire and refine it. Hence, by definition, they are incomplete at any given time. In this paper, we describe a technique that improves our previous method for predicting novel GO annotations by extracting implicit semantic relationships between genes and functions. In this work, we use a vector space model and a number of weighting schemes in addition to our previous latent semantic indexing approach. The technique described here is able to take into consideration the hierarchical structure of the Gene Ontology (GO) and can weight differently GO terms situated at different depths. The prediction abilities of 15 different weighting schemes are compared and evaluated. Nine such schemes were previously used in other problem domains, while six of them are introduced in this paper. The best weighting scheme was a novel scheme, n2tn. Out of the top 50 functional annotations predicted using this weighting scheme, we found support in the literature for 84 percent of them, while 6 percent of the predictions were contradicted by the existing literature. For the remaining 10 percent, we did not find any relevant publications to confirm or contradict the predictions. The n2tn weighting scheme also outperformed the simple binary scheme used in our previous approach. PMID:20150671

  11. Supervised Semantic Classification for Nuclear Proliferation Monitoring

    SciTech Connect

    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 proliferation monitoring using high resolution remote sensing images.

  12. Complex Semantic Networks

    NASA Astrophysics Data System (ADS)

    Teixeira, G. M.; Aguiar, M. S. F.; Carvalho, C. F.; Dantas, D. R.; Cunha, M. V.; Morais, J. H. M.; Pereira, H. B. B.; Miranda, J. G. V.

    Verbal language is a dynamic mental process. Ideas emerge by means of the selection of words from subjective and individual characteristics throughout the oral discourse. The goal of this work is to characterize the complex network of word associations that emerge from an oral discourse from a discourse topic. Because of that, concepts of associative incidence and fidelity have been elaborated and represented the probability of occurrence of pairs of words in the same sentence in the whole oral discourse. Semantic network of words associations were constructed, where the words are represented as nodes and the edges are created when the incidence-fidelity index between pairs of words exceeds a numerical limit (0.001). Twelve oral discourses were studied. The networks generated from these oral discourses present a typical behavior of complex networks and their indices were calculated and their topologies characterized. The indices of these networks obtained from each incidence-fidelity limit exhibit a critical value in which the semantic network has maximum conceptual information and minimum residual associations. Semantic networks generated by this incidence-fidelity limit depict a pattern of hierarchical classes that represent the different contexts used in the oral discourse.

  13. Latent Period of Relaxation.

    PubMed

    Kobayashi, M; Irisawa, H

    1961-10-27

    The latent period of relaxation of molluscan myocardium due to anodal current is much longer than that of contraction. Although the rate and the grade of relaxation are intimately related to both the stimulus condition and the muscle tension, the latent period of relaxation remains constant, except when the temperature of the bathing fluid is changed.

  14. Preserved Musical Semantic Memory in Semantic Dementia

    PubMed Central

    Weinstein, Jessica; Koenig, Phyllis; Gunawardena, Delani; McMillan, Corey; Bonner, Michael; Grossman, Murray

    2012-01-01

    Objective To understand the scope of semantic impairment in semantic dementia. Design Case study. Setting Academic medical center. Patient A man with semantic dementia, as demonstrated by clinical, neuropsychological, and imaging studies. Main Outcome Measures Music performance and magnetic resonance imaging results. Results Despite profoundly impaired semantic memory for words and objects due to left temporal lobe atrophy, this semiprofessional musician was creative and expressive in demonstrating preserved musical knowledge. Conclusion Long-term representations of words and objects in semantic memory may be dissociated from meaningful knowledge in other domains, such as music. PMID:21320991

  15. Text Influenced Molecular Indexing (TIMI): a literature database mining approach that handles text and chemistry.

    PubMed

    Singh, Suresh B; Hull, Richard D; Fluder, Eugene M

    2003-01-01

    We present an application of a novel methodology called Text Influenced Molecular Indexing (TIMI) to mine the information in the scientific literature. TIMI is an extension of two existing methodologies: (1) Latent Semantic Structure Indexing (LaSSI), a method for calculating chemical similarity using two-dimensional topological descriptors, and (2) Latent Semantic Indexing (LSI), a method for generating correlations between textual terms. The singular value decomposition (SVD) of a feature/object matrix is the fundamental mathematical operation underlying LSI, LaSSI, and TIMI and is used in the identification of associations between textual and chemical descriptors. We present the results of our studies with a database containing 11,571 PubMed/MEDLINE abstracts which show the advantages of merging textual and chemical descriptors over using either text or chemistry alone. Our work demonstrates that searching text-only databases limits retrieved documents to those that explicitly mention compounds by name in the text. Similarly, searching chemistry-only databases can only retrieve those documents that have chemical structures in them. TIMI, however, enables search and retrieval of documents with textual, chemical, and/or text- and chemistry-based queries. Thus, the TIMI system offers a powerful new approach to uncovering the contextual scientific knowledge sought by the medical research community.

  16. Latent fingerprint matching.

    PubMed

    Jain, Anil K; Feng, Jianjiang

    2011-01-01

    Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.

  17. Grounding Collaborative Learning in Semantics-Based Critiquing

    ERIC Educational Resources Information Center

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

    2007-01-01

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

  18. A Study about Placement Support Using Semantic Similarity

    ERIC Educational Resources Information Center

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

    2014-01-01

    This paper discusses Latent Semantic Analysis (LSA) as a method for the assessment of prior learning. The Accreditation of Prior Learning (APL) is a procedure to offer learners an individualized curriculum based on their prior experiences and knowledge. The placement decisions in this process are based on the analysis of student material by domain…

  19. The Software Therapist: Usability Problem Diagnosis Through Latent Semantic Analysis

    DTIC Science & Technology

    2006-06-14

    engineering because of information losses. The causes of these losses can be attributed to a lack of an adequate conceptual framework for organizing...effective inputs to redesign for fixing the problems found during testing. These problems can be exacerbated by inadequate levels of training or...discovery of appropriate and effective solutions to these problems. For this project, the UAF was validated, extended with new material, and embodied in

  20. Semantics via Machine Translation

    ERIC Educational Resources Information Center

    Culhane, P. T.

    1977-01-01

    Recent experiments in machine translation have given the semantic elements of collocation in Russian more objective criteria. Soviet linguists in search of semantic relationships have attempted to devise a semantic synthesis for construction of a basic language for machine translation. One such effort is summarized. (CHK)

  1. SEMANTICS AND CRITICAL READING.

    ERIC Educational Resources Information Center

    FLANIGAN, MICHAEL C.

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

  2. The computational complexity of alternative updating approaches for an SVD-encoded indexing scheme

    SciTech Connect

    Berry, M.W.; O`Brien, G.W.; Dumais, S.T.

    1995-12-01

    Latent Semantic Indexing (LSI) is a conceptual indexing technique which uses the truncated SVD to estimate the underlying latent semantic structure of word to document association. By computing a lower-rank approximation to the original term-document matrix, LSI dampens the effects of word choice variability by representing terms and documents using (orthogonal) left and right singular vectors. Current methods for adding new documents to an LSI database (folding-in documents) can have deteriorating effects on the orthogonality of the vectors used to represent documents in high-dimensional subspaces. An alternative approach which updates the original truncated SVD so as to preserve the orthogonality among document vectors corresponding to the new term-document matrix is presented. The cost of the numerical computations and available memory needed to update the SVD versus the potential inaccuracy of former updating methods presents an interesting tradeoff for LSI database management. The computational cost of recomputing the truncated SVD of perturbed term-document matrices, updating current truncated SVD`s of term-document matrices, and the folding-in of new documents into an existing LSI model is presented.

  3. Potentiation of latent inhibition.

    PubMed

    Rodriguez, Gabriel; Hall, Geoffrey

    2008-07-01

    Rats were given exposure either to an odor (almond) or a compound of odor plus taste (almond plus saline), prior to training in which the odor served as the conditioned stimulus. It was found, for both appetitive and aversive procedures, that conditioning was retarded by preexposure (a latent inhibition effect), and the extent of the retardation was greater in rats preexposed to the compound (i.e., latent inhibition to the odor was potentiated by the presence of the taste). In contrast, the presence of the taste during conditioning itself overshadowed learning about the odor. We argue that the presence of the salient taste in compound with the odor enhances the rate of associative learning, producing a rapid loss in the associability of the odor. This loss of associability will generate both overshadowing and the potentiation of latent inhibition that is observed after preexposure to the compound.

  4. Biomedical semantics in the Semantic Web.

    PubMed

    Splendiani, Andrea; Burger, Albert; Paschke, Adrian; Romano, Paolo; Marshall, M Scott

    2011-03-07

    The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.

  5. Semantic contextual cuing and visual attention.

    PubMed

    Goujon, Annabelle; Didierjean, André; Marmèche, Evelyne

    2009-02-01

    Since M. M. Chun and Y. Jiang's (1998) original study, a large body of research based on the contextual cuing paradigm has shown that the visuocognitive system is capable of capturing certain regularities in the environment in an implicit way. The present study investigated whether regularities based on the semantic category membership of the context can be learned implicitly and whether that learning depends on attention. The contextual cuing paradigm was used with lexical displays in which the semantic category of the contextual words either did or did not predict the target location. Experiments 1 and 2 revealed that implicit contextual cuing effects can be extended to semantic category regularities. Experiments 3 and 4 indicated an implicit contextual cuing effect when the predictive context appeared in an attended color but not when the predictive context appeared in an ignored color. However, when the previously ignored context suddenly became attended, it immediately facilitated performance. In contrast, when the previously attended context suddenly became ignored, no benefit was observed. Results suggest that the expression of implicit semantic knowledge depends on attention but that latent learning can nevertheless take place outside the attentional field.

  6. Latent Variable Interaction Modeling.

    ERIC Educational Resources Information Center

    Schumacker, Randall E.

    2002-01-01

    Used simulation to study two different approaches to latent variable interaction modeling with continuous observed variables: (1) a LISREL 8.30 program and (2) data analysis through PRELIS2 and SIMPLIS programs. Results show that parameter estimation was similar but standard errors were different. Discusses differences in ease of implementation.…

  7. Somatotopic Semantic Priming and Prediction in the Motor System

    PubMed Central

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

    2016-01-01

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

  8. Somatotopic Semantic Priming and Prediction in the Motor System.

    PubMed

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

    2016-05-01

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

  9. Exploring MEDLINE space with random indexing and pathfinder networks.

    PubMed

    Cohen, Trevor

    2008-11-06

    The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search.

  10. Exploring MEDLINE Space with Random Indexing and Pathfinder Networks

    PubMed Central

    Cohen, Trevor

    2008-01-01

    The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search. PMID:18999236

  11. Semantic Networks and Social Networks

    ERIC Educational Resources Information Center

    Downes, Stephen

    2005-01-01

    Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

  12. Is There Semantic Interference in Delayed Naming?

    ERIC Educational Resources Information Center

    Madebach, Andreas; Oppermann, Frank; Hantsch, Ansgar; Curda, Christian; Jescheniak, Jorg D.

    2011-01-01

    The semantic interference effect in the picture-word interference task is interpreted as an index of lexical competition in prominent speech production models. Janssen, Schirm, Mahon, and Caramazza (2008) challenged this interpretation on the basis of experiments with a novel version of this task, which introduced a task-switching component.…

  13. Semantic search via concept annealing

    NASA Astrophysics Data System (ADS)

    Dunkelberger, Kirk A.

    2007-04-01

    Annealing, in metallurgy and materials science, is a heat treatment wherein the microstructure of a material is altered, causing changes in its properties such as strength and hardness. We define concept annealing as a lexical, syntactic, and semantic expansion capability (the removal of defects and the internal stresses that cause term- and phrase-based search failure) coupled with a directed contraction capability (semantically-related terms, queries, and concepts nucleate and grow to replace those originally deformed by internal stresses). These two capabilities are tied together in a control loop mediated by the information retrieval precision and recall metrics coupled with intuition provided by the operator. The specific representations developed have been targeted at facilitating highly efficient and effective semantic indexing and searching. This new generation of Find capability enables additional processing (i.e. all-source tracking, relationship extraction, and total system resource management) at rates, precisions, and accuracies previously considered infeasible. In a recent experiment, an order magnitude reduction in time to actionable intelligence and nearly three orderss magnitude reduction in false alarm rate was achieved.

  14. Latent effects decision analysis

    DOEpatents

    Cooper, J. Arlin; Werner, Paul W.

    2004-08-24

    Latent effects on a system are broken down into components ranging from those far removed in time from the system under study (latent) to those which closely effect changes in the system. Each component is provided with weighted inputs either by a user or from outputs of other components. A non-linear mathematical process known as `soft aggregation` is performed on the inputs to each component to provide information relating to the component. This information is combined in decreasing order of latency to the system to provide a quantifiable measure of an attribute of a system (e.g., safety) or to test hypotheses (e.g., for forensic deduction or decisions about various system design options).

  15. Latent common genetic components of obesity traits

    PubMed Central

    Harders, R; Luke, A; Zhu, X; Cooper, RS

    2008-01-01

    Background Obesity is rapidly becoming a global epidemic. Unlike many complex human diseases, obesity is defined not just by a single trait or phenotype, but jointly by measures of anthropometry and metabolic status. Methods We applied maximum likelihood factor analysis to identify common latent factors underlying observed covariance in multiple obesity-related measures. Both the genetic components and the mode of inheritance of the common factors were evaluated. A total of 1775 participants from 590 families for whom measures on obesity-related traits were available were included in this study. Results The average age of participants was 37 years, 39% of the participants were obese (body mass index ≥ 30.0 kg/m2) and 26% were overweight (body mass index 25.0 - 29.9 kg/m2). Two latent common factors jointly accounting for over 99% of the correlations among obesity-related traits were identified. Complex segregation analysis of the age and sex-adjusted latent factors provide evidence for a Mendelian mode of inheritance of major genetic effect with heritability estimates of 40.4% and 47.5% for the first and second factors, respectively. Conclusions These findings provide a support for multivariate-based approach for investigating pleiotropic effects on obesity-related traits which can be applied in both genetic linkage and association mapping. PMID:18936762

  16. Semantic significance: a new measure of feature salience.

    PubMed

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

    2014-04-01

    According to the feature-based model of semantic memory, concepts are described by a set of semantic features that contribute, with different weights, to the meaning of a concept. Interestingly, this theoretical framework has introduced numerous dimensions to describe semantic features. Recently, we proposed a new parameter to measure the importance of a semantic feature for the conceptual representation-that is, semantic significance. Here, with speeded verification tasks, we tested the predictive value of our index and investigated the relative roles of conceptual and featural dimensions on the participants' performance. The results showed that semantic significance is a good predictor of participants' verification latencies and suggested that it efficiently captures the salience of a feature for the computation of the meaning of a given concept. Therefore, we suggest that semantic significance can be considered an effective index of the importance of a feature in a given conceptual representation. Moreover, we propose that it may have straightforward implications for feature-based models of semantic memory, as an important additional factor for understanding conceptual representation.

  17. An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

    NASA Astrophysics Data System (ADS)

    Hildebrandt, Mario; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

    2014-02-01

    In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, "Golden-Oak" veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates

  18. The Semantic Learning Organization

    ERIC Educational Resources Information Center

    Sicilia, Miguel-Angel; Lytras, Miltiadis D.

    2005-01-01

    Purpose: The aim of this paper is introducing the concept of a "semantic learning organization" (SLO) as an extension of the concept of "learning organization" in the technological domain. Design/methodology/approach: The paper takes existing definitions and conceptualizations of both learning organizations and Semantic Web technology to develop…

  19. Aging and Semantic Activation.

    ERIC Educational Resources Information Center

    Howard, Darlene V.

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

  20. Semantic Sensor Web

    NASA Astrophysics Data System (ADS)

    Sheth, A.; Henson, C.; Thirunarayan, K.

    2008-12-01

    Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the Semantic Sensor Web (SSW) [1] proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge. Kno.e.sis Center's approach to SSW is an evolutionary one. It adds semantic annotations to the existing standard sensor languages of the Sensor Web Enablement (SWE) defined by OGC. These annotations enhance primarily syntactic XML-based descriptions in OGC's SWE languages with microformats, and W3C's Semantic Web languages- RDF and OWL. In association with semantic annotation and semantic web capabilities including ontologies and rules, SSW supports interoperability, analysis and reasoning over heterogeneous multi-modal sensor data. In this presentation, we will also demonstrate a mashup with support for complex spatio-temporal-thematic queries [2] and semantic analysis that utilize semantic annotations, multiple ontologies and rules. It uses existing services (e.g., GoogleMap) and semantics enhanced SWE's Sensor Observation Service (SOS) over weather and road condition data from various sensors that are part of Ohio's transportation network. Our upcoming plans are to demonstrate end to end (heterogeneous sensor to application) semantics support and study scalability of SSW involving thousands of sensors to about a billion triples. Keywords: Semantic Sensor Web, Spatiotemporal thematic queries, Semantic Web Enablement, Sensor Observation Service [1] Amit Sheth, Cory Henson, Satya

  1. Order Theoretical Semantic Recommendation

    SciTech Connect

    Joslyn, Cliff A.; Hogan, Emilie A.; Paulson, Patrick R.; Peterson, Elena S.; Stephan, Eric G.; Thomas, Dennis G.

    2013-07-23

    Mathematical concepts of order and ordering relations play multiple roles in semantic technologies. Discrete totally ordered data characterize both input streams and top-k rank-ordered recommendations and query output, while temporal attributes establish numerical total orders, either over time points or in the more complex case of startend temporal intervals. But also of note are the fully partially ordered data, including both lattices and non-lattices, which actually dominate the semantic strcuture of ontological systems. Scalar semantic similarities over partially-ordered semantic data are traditionally used to return rank-ordered recommendations, but these require complementation with true metrics available over partially ordered sets. In this paper we report on our work in the foundations of partial order measurement in ontologies, with application to top-k semantic recommendation in workflows.

  2. Enhancing medical database semantics.

    PubMed Central

    Leão, B. de F.; Pavan, A.

    1995-01-01

    Medical Databases deal with dynamic, heterogeneous and fuzzy data. The modeling of such complex domain demands powerful semantic data modeling methodologies. This paper describes GSM-Explorer a Case Tool that allows for the creation of relational databases using semantic data modeling techniques. GSM Explorer fully incorporates the Generic Semantic Data Model-GSM enabling knowledge engineers to model the application domain with the abstraction mechanisms of generalization/specialization, association and aggregation. The tool generates a structure that implements persistent database-objects through the automatic generation of customized SQL ANSI scripts that sustain the semantics defined in the higher lever. This paper emphasizes the system architecture and the mapping of the semantic model into relational tables. The present status of the project and its further developments are discussed in the Conclusions. PMID:8563288

  3. Advancement of Latent Trait Theory.

    DTIC Science & Technology

    1988-02-01

    latent trait theory further, and include more varieties of situations. I [51 Investigation of ways of bridging across mathematical psychology and...five years on various topics in Latent Trait Theory, including more general topics such as the method of moments as the least squares solution for...response theory." The address described as (3) in the above list was a one hour special lecture overviewing latent trait models. There were more than two

  4. Semantics, Pragmatics, and the Nature of Semantic Theories

    ERIC Educational Resources Information Center

    Spewak, David Charles, Jr.

    2013-01-01

    The primary concern of this dissertation is determining the distinction between semantics and pragmatics and how context sensitivity should be accommodated within a semantic theory. I approach the question over how to distinguish semantics from pragmatics from a new angle by investigating what the objects of a semantic theory are, namely…

  5. Predicting protein-protein relationships from literature using latent topics.

    PubMed

    Aso, Tatsuya; Eguchi, Koji

    2009-10-01

    This paper investigates applying statistical topic models to extract and predict relationships between biological entities, especially protein mentions. A statistical topic model, Latent Dirichlet Allocation (LDA) is promising; however, it has not been investigated for such a task. In this paper, we apply the state-of-the-art Collapsed Variational Bayesian Inference and Gibbs Sampling inference to estimating the LDA model. We also apply probabilistic Latent Semantic Analysis (pLSA) as a baseline for comparison, and compare them from the viewpoints of log-likelihood, classification accuracy and retrieval effectiveness. We demonstrate through experiments that the Collapsed Variational LDA gives better results than the others, especially in terms of classification accuracy and retrieval effectiveness in the task of the protein-protein relationship prediction.

  6. Identification of novel type III effectors using latent Dirichlet allocation.

    PubMed

    Yang, Yang

    2012-01-01

    Among the six secretion systems identified in Gram-negative bacteria, the type III secretion system (T3SS) plays important roles in the disease development of pathogens. T3SS has attracted a great deal of research interests. However, the secretion mechanism has not been fully understood yet. Especially, the identification of effectors (secreted proteins) is an important and challenging task. This paper adopts machine learning methods to identify type III secreted effectors (T3SEs). We extract features from amino acid sequences and conduct feature reduction based on latent semantic information by using latent Dirichlet allocation model. The experimental results on Pseudomonas syringae data set demonstrate the good performance of the new methods.

  7. A Latent Transition Model with Logistic Regression

    ERIC Educational Resources Information Center

    Chung, Hwan; Walls, Theodore A.; Park, Yousung

    2007-01-01

    Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian…

  8. A Semantic Graph Query Language

    SciTech Connect

    Kaplan, I L

    2006-10-16

    Semantic graphs can be used to organize large amounts of information from a number of sources into one unified structure. A semantic query language provides a foundation for extracting information from the semantic graph. The graph query language described here provides a simple, powerful method for querying semantic graphs.

  9. A Defense of Semantic Minimalism

    ERIC Educational Resources Information Center

    Kim, Su

    2012-01-01

    Semantic Minimalism is a position about the semantic content of declarative sentences, i.e., the content that is determined entirely by syntax. It is defined by the following two points: "Point 1": The semantic content is a complete/truth-conditional proposition. "Point 2": The semantic content is useful to a theory of…

  10. A predictive framework for evaluating models of semantic organization in free recall

    PubMed Central

    Morton, Neal W; Polyn, Sean M.

    2016-01-01

    Research in free recall has demonstrated that semantic associations reliably influence the organization of search through episodic memory. However, the specific structure of these associations and the mechanisms by which they influence memory search remain unclear. We introduce a likelihood-based model-comparison technique, which embeds a model of semantic structure within the context maintenance and retrieval (CMR) model of human memory search. Within this framework, model variants are evaluated in terms of their ability to predict the specific sequence in which items are recalled. We compare three models of semantic structure, latent semantic analysis (LSA), global vectors (GloVe), and word association spaces (WAS), and find that models using WAS have the greatest predictive power. Furthermore, we find evidence that semantic and temporal organization is driven by distinct item and context cues, rather than a single context cue. This finding provides important constraint for theories of memory search.

  11. A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

    ERIC Educational Resources Information Center

    Leite, Walter L.

    2007-01-01

    Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared…

  12. Semantic Services for Wikipedia

    NASA Astrophysics Data System (ADS)

    Wang, Haofen; Penin, Thomas; Fu, Linyun; Liu, Qiaoling; Xue, Guirong; Yu, Yong

    Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It features many attractive characteristics, like entity-based link graph, abundant categorization and semi-structured layout, and can serve as an ideal data source to extract high quality and well-structured data. In this chapter, we first propose several solutions to extract knowledge from Wikipedia. We do not only consider information from the relational summaries of articles (infoboxes) but also semi-automatically extract it from the article text using the structured content available. Due to differences with information extraction from the Web, it is necessary to tackle new problems, like the lack of redundancy in Wikipedia that is dealt with by extending traditional machine learning algorithms to work with few labeled data. Furthermore, we also exploit the widespread categories as a complementary way to discover additional knowledge. Benefiting from both structured and textural information, we additionally provide a suggestion service for Wikipedia authoring. With the aim to facilitate semantic reuse, our proposal provides users with facilities such as link, categories and infobox content suggestions. The proposed enhancements can be applied to attract more contributors and lighten the burden of professional editors. Finally, we developed an enhanced search system, which can ease the process of exploiting Wikipedia. To provide a user-friendly interface, it extends the faceted search interface with relation navigation and let the user easily express his complex information needs in an interactive way. In order to achieve efficient query answering, it extends scalable IR engines to index and search both the textual and structured information with an integrated ranking support.

  13. Toward Automatic Determination of the Semantics of Connectives in Large Newspaper Corpora

    ERIC Educational Resources Information Center

    Bestgen, Yves; Degand, Liesbeth; Spooren, Wilbert

    2006-01-01

    We explored the possibility of using automatic techniques to analyze the use of backward causal connectives in large Dutch newspaper corpora. With the help of 2 techniques, Latent Semantic Analysis and Thematic Text Analysis, the contexts of more than 14,000 connectives were studied. The method of analysis is described. We found that differences…

  14. Music, language and meaning: brain signatures of semantic processing.

    PubMed

    Koelsch, Stefan; Kasper, Elisabeth; Sammler, Daniela; Schulze, Katrin; Gunter, Thomas; Friederici, Angela D

    2004-03-01

    Semantics is a key feature of language, but whether or not music can activate brain mechanisms related to the processing of semantic meaning is not known. We compared processing of semantic meaning in language and music, investigating the semantic priming effect as indexed by behavioral measures and by the N400 component of the event-related brain potential (ERP) measured by electroencephalography (EEG). Human subjects were presented visually with target words after hearing either a spoken sentence or a musical excerpt. Target words that were semantically unrelated to prime sentences elicited a larger N400 than did target words that were preceded by semantically related sentences. In addition, target words that were preceded by semantically unrelated musical primes showed a similar N400 effect, as compared to target words preceded by related musical primes. The N400 priming effect did not differ between language and music with respect to time course, strength or neural generators. Our results indicate that both music and language can prime the meaning of a word, and that music can, as language, determine physiological indices of semantic processing.

  15. The role of textual semantic constraints in knowledge-based inference generation during reading comprehension: A computational approach.

    PubMed

    Yeari, Menahem; van den Broek, Paul

    2015-01-01

    The present research adopted a computational approach to explore the extent to which the semantic content of texts constrains the activation of knowledge-based inferences. Specifically, we examined whether textual semantic constraints (TSC) can explain (1) the activation of predictive inferences, (2) the activation of bridging inferences and (3) the higher prevalence of the activation of bridging inferences compared to predictive inferences. To examine these hypotheses, we computed the strength of semantic associations between texts and probe items as presented to human readers in previous behavioural studies, using the Latent Semantic Analysis (LSA) algorithm. We tested whether stronger semantic associations are observed for inferred items compared to control items. Our results show that in 15 out of 17 planned comparisons, the computed strength of semantic associations successfully simulated the activation of inferences. These findings suggest that TSC play a central role in the activation of knowledge-based inferences.

  16. Trusting Crowdsourced Geospatial Semantics

    NASA Astrophysics Data System (ADS)

    Goodhue, P.; McNair, H.; Reitsma, F.

    2015-08-01

    The degree of trust one can place in information is one of the foremost limitations of crowdsourced geospatial information. As with the development of web technologies, the increased prevalence of semantics associated with geospatial information has increased accessibility and functionality. Semantics also provides an opportunity to extend indicators of trust for crowdsourced geospatial information that have largely focused on spatio-temporal and social aspects of that information. Comparing a feature's intrinsic and extrinsic properties to associated ontologies provides a means of semantically assessing the trustworthiness of crowdsourced geospatial information. The application of this approach to unconstrained semantic submissions then allows for a detailed assessment of the trust of these features whilst maintaining the descriptive thoroughness this mode of information submission affords. The resulting trust rating then becomes an attribute of the feature, providing not only an indication as to the trustworthiness of a specific feature but is able to be aggregated across multiple features to illustrate the overall trustworthiness of a dataset.

  17. Impact of Latent Infection Treatment in Indigenous Populations

    PubMed Central

    Yuhara, Lucia Suemi; Sacchi, Flávia Patussi Correia; Croda, Julio

    2013-01-01

    The aims of the present study were to identify risk factors associated with latent tuberculosis (TB), examine the development of active disease among contacts, and assess the effectiveness of treating latent infection in indigenous Brazilians from January 2006 to December 2011. This was a retrospective study consisting of 1,371 tuberculosis contacts, 392 of whom underwent treatment for latent infection. Morbidity-from-TB data were obtained from the Information System for Disease Notification (SINAN) database, and the contacts’ data were collected from the clinical records using forms employed by Special Department of Indigenous Health (SESAI) multidisciplinary teams, according to SESAI’s instructions. The variables that were associated with latent infection among the contacts were age (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 1.02–1.04) and close contact with a smear-positive index case (OR: 2.26, 95% CI: 1.59–3.22). The variables associated with the development of active TB among the contacts were a tuberculin skin test (TST) ≥10 mm (relative risk [RR]: 1.12, 95% CI: 1.07–1.17), age (RR: 1.01, 95% CI: 1.00–1.03), and treatment of latent infection (RR: 0.03, 95% CI: 0.01–0.27). The estimated number of latent infection treatments needed to prevent one case of active TB among the contacts was 51 treatments (95% CI: 33–182). In contacts with TST ≥10 mm, 10 (95% CI: 6–19) latent infection treatments were necessary to prevent one case of active TB. Age and close contact with a smear-positive index case were associated with latent TB. Screening with TST is a high priority among individuals contacting smear-positive index cases. Age and TST are associated with the development of active TB among contacts, and treatment of latent infection is an effective measure to control TB in indigenous communities. PMID:23936264

  18. Toward a brain-based componential semantic representation.

    PubMed

    Binder, Jeffrey R; Conant, Lisa L; Humphries, Colin J; Fernandino, Leonardo; Simons, Stephen B; Aguilar, Mario; Desai, Rutvik H

    2016-01-01

    Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced. In this study we explore the possibility of devising a componential model of semantic representation based entirely on such functional divisions in the human brain. We propose a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences. We provide normative data on the salience of each attribute for a large set of English nouns, verbs, and adjectives, and show how these attribute vectors distinguish a priori conceptual categories and capture semantic similarity. Robust quantitative differences between concrete object categories were observed across a large number of attribute dimensions. A within- versus between-category similarity metric showed much greater separation between categories than representations derived from distributional (latent semantic) analysis of text. Cluster analyses were used to explore the similarity structure in the data independent of a priori labels, revealing several novel category distinctions. We discuss how such a representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and

  19. Arabic Literature: Uniterm Indexing System for Storage and Retrieval.

    ERIC Educational Resources Information Center

    Ghani, Abdul

    1987-01-01

    Describes the uniterm system of coordinate indexing and examines its applicability to the indexing of Arabic literature. Unresolved problems in semantics, thesauri development, and standardization are identified and discussed. (CLB)

  20. Non-semantic contributions to "semantic" redundancy gain.

    PubMed

    Shepherdson, Peter; Miller, Jeff

    2016-01-01

    Recently, two groups of researchers have reported redundancy gains (enhanced performance with multiple, redundant targets) in tasks requiring semantic categorization. Here we report two experiments aimed at determining whether the gains found by one of these groups resulted from some form of semantic coactivation. We asked undergraduate psychology students to complete choice RT tasks requiring the semantic categorization of visually presented words, and compared performance with redundant targets from the same semantic category to performance with redundant targets from different semantic categories. If the redundancy gains resulted from the combination of information at a semantic level, they should have been greater in the former than the latter situation. However, our results showed no significant differences in redundancy gain (for latency and accuracy) between same-category and different-category conditions, despite gains appearing in both conditions. Thus, we suggest that redundancy gain in the semantic categorization task may result entirely from statistical facilitation or combination of information at non-semantic levels.

  1. LATENT LIFE OF ARTERIES.

    PubMed

    Carrel, A

    1910-07-23

    When a segment of artery, killed by heat, formalin or glycerin is transplanted, it undergoes a rapid degeneration. Its muscle fibers disappear while the tissue of the host reacts by building a new wall of connective tissue. When the transplanted vessel has been preserved in a condition of latent life, no degeneration of the wall occurs, or the wall undergoes only partial degeneration. The muscle fibers can keep their normal appearance, even for a long time after the operation. It is, therefore, demonstrated that arteries can be preserved outside of the body in a condition of unmanifested actual life. The best method of preservation consists of placing the vessels, immersed in vaselin, in an ice box, the temperature of which is slightly above the freezing point. From a surgical standpoint, the transplantation of preserved vessels can be used with some safety. When the arteries were kept in defibrinated blood or vaselin and in cold storage, the proportion of positive results was 75 and 80 per cent., and this can probably be increased.

  2. Reflective Random Indexing and indirect inference: a scalable method for discovery of implicit connections.

    PubMed

    Cohen, Trevor; Schvaneveldt, Roger; Widdows, Dominic

    2010-04-01

    The discovery of implicit connections between terms that do not occur together in any scientific document underlies the model of literature-based knowledge discovery first proposed by Swanson. Corpus-derived statistical models of semantic distance such as Latent Semantic Analysis (LSA) have been evaluated previously as methods for the discovery of such implicit connections. However, LSA in particular is dependent on a computationally demanding method of dimension reduction as a means to obtain meaningful indirect inference, limiting its ability to scale to large text corpora. In this paper, we evaluate the ability of Random Indexing (RI), a scalable distributional model of word associations, to draw meaningful implicit relationships between terms in general and biomedical language. Proponents of this method have achieved comparable performance to LSA on several cognitive tasks while using a simpler and less computationally demanding method of dimension reduction than LSA employs. In this paper, we demonstrate that the original implementation of RI is ineffective at inferring meaningful indirect connections, and evaluate Reflective Random Indexing (RRI), an iterative variant of the method that is better able to perform indirect inference. RRI is shown to lead to more clearly related indirect connections and to outperform existing RI implementations in the prediction of future direct co-occurrence in the MEDLINE corpus.

  3. Understanding Latent Heat of Vaporization.

    ERIC Educational Resources Information Center

    Linz, Ed

    1995-01-01

    Presents a simple exercise for students to do in the kitchen at home to determine the latent heat of vaporization of water using typical household materials. Designed to stress understanding by sacrificing precision for simplicity. (JRH)

  4. Latent Dirichlet allocation models for image classification.

    PubMed

    Rasiwasia, Nikhil; Vasconcelos, Nuno

    2013-11-01

    Two new extensions of latent Dirichlet allocation (LDA), denoted topic-supervised LDA (ts-LDA) and class-specific-simplex LDA (css-LDA), are proposed for image classification. An analysis of the supervised LDA models currently used for this task shows that the impact of class information on the topics discovered by these models is very weak in general. This implies that the discovered topics are driven by general image regularities, rather than the semantic regularities of interest for classification. To address this, ts-LDA models are introduced which replace the automated topic discovery of LDA with specified topics, identical to the classes of interest for classification. While this results in improvements in classification accuracy over existing LDA models, it compromises the ability of LDA to discover unanticipated structure of interest. This limitation is addressed by the introduction of css-LDA, an LDA model with class supervision at the level of image features. In css-LDA topics are discovered per class, i.e., a single set of topics shared across classes is replaced by multiple class-specific topic sets. The css-LDA model is shown to combine the labeling strength of topic-supervision with the flexibility of topic-discovery. Its effectiveness is demonstrated through an extensive experimental evaluation, involving multiple benchmark datasets, where it is shown to outperform existing LDA-based image classification approaches.

  5. Bridging the semantic gap in sports

    NASA Astrophysics Data System (ADS)

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

    2003-01-01

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

  6. Predicting Latent Class Scores for Subsequent Analysis

    ERIC Educational Resources Information Center

    Petersen, Janne; Bandeen-Roche, Karen; Budtz-Jorgensen, Esben; Larsen, Klaus Groes

    2012-01-01

    Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose…

  7. Comparing the performance of two CBIRS indexing schemes

    NASA Astrophysics Data System (ADS)

    Mueller, Wolfgang; Robbert, Guenter; Henrich, Andreas

    2003-01-01

    Content based image retrieval (CBIR) as it is known today has to deal with a number of challenges. Quickly summarized, the main challenges are firstly, to bridge the semantic gap between high-level concepts and low-level features using feedback, secondly to provide performance under adverse conditions. High-dimensional spaces, as well as a demanding machine learning task make the right way of indexing an important issue. When indexing multimedia data, most groups opt for extraction of high-dimensional feature vectors from the data, followed by dimensionality reduction like PCA (Principal Components Analysis) or LSI (Latent Semantic Indexing). The resulting vectors are indexed using spatial indexing structures such as kd-trees or R-trees, for example. Other projects, such as MARS and Viper propose the adaptation of text indexing techniques, notably the inverted file. Here, the Viper system is the most direct adaptation of text retrieval techniques to quantized vectors. However, while the Viper query engine provides decent performance together with impressive user-feedback behavior, as well as the possibility for easy integration of long-term learning algorithms, and support for potentially infinite feature vectors, there has been no comparison of vector-based methods and inverted-file-based methods under similar conditions. In this publication, we compare a CBIR query engine that uses inverted files (Bothrops, a rewrite of the Viper query engine based on a relational database), and a CBIR query engine based on LSD (Local Split Decision) trees for spatial indexing using the same feature sets. The Benchathlon initiative works on providing a set of images and ground truth for simulating image queries by example and corresponding user feedback. When performing the Benchathlon benchmark on a CBIR system (the System Under Test, SUT), a benchmarking harness connects over internet to the SUT, performing a number of queries using an agreed-upon protocol, the multimedia

  8. Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet Allocation

    DTIC Science & Technology

    2013-09-01

    Con- ferences on Artificial Intelligence , 682–687. 1999. [57] Hofmann, Thomas. “Probabilistic Latent Semantic Indexing”. Proceedings of the Twenty...Journal of Artificial Intelligence Research, 11:169–198, 1999. [92] Park, Bo Gun, Kyoung Mu Lee, and Sang Uk Lee. “Color-based image retrieval using...Department of Defense mission. Often, rapid exploitation of digital media can produce time-sensitive intelligence that could mean the difference

  9. Temporal Representation in Semantic Graphs

    SciTech Connect

    Levandoski, J J; Abdulla, G M

    2007-08-07

    A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.

  10. [PSA variations in persons with latent prostate cancer].

    PubMed

    Stamatiou, K; Danciu, M; Karakos, C; Sofras, F

    2008-01-01

    The introduction and common use of serum PSA (Prostate Specific Antigen) has been demonstrated a useful index on latent prostate cancer diagnostic but in the same time has increased surgical intervention on histological forms with no eventual future evolution. Benign comportment of latent carcinomas being well known in advance, we correlated in vitro serum PSA from latent tumors, with the samples from a control group (prostates without signs of malignization). Levels of PSA were slightly elevated compared to age norms, mainly in cases with a large coexistent hypertrophy. Our reduced sample does not stand any statistic analysis, but this observation could eventually explain increased diagnostic and hyper-treatment of non-important carcinomas from a clinical point of view.

  11. Semantic Search of Web Services

    ERIC Educational Resources Information Center

    Hao, Ke

    2013-01-01

    This dissertation addresses semantic search of Web services using natural language processing. We first survey various existing approaches, focusing on the fact that the expensive costs of current semantic annotation frameworks result in limited use of semantic search for large scale applications. We then propose a vector space model based service…

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

    PubMed

    Huang, Chung-Chi; Lu, Zhiyong

    2016-01-01

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

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

    PubMed Central

    Huang, Chung-Chi; Lu, Zhiyong

    2016-01-01

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

  14. Semantic Processing in Children and Adults: Incongruity and the N400

    ERIC Educational Resources Information Center

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

    2011-01-01

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

  15. A Revised Semantic Differential Scale Distinguishing between Negative and Positive God Images

    ERIC Educational Resources Information Center

    Francis, Leslie J.; Robbins, Mandy; Gibson, Harry M.

    2006-01-01

    A sample of 755 school pupils between the ages of 11 and 18 years completed the Benson and Spilka semantic differential measure of God images. Factor analysis indicated the advantages of re-scoring the measure as an eight item unidimensional index, defining semantic space relating to God images ranging from negative affect to positive affect.…

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

    PubMed

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

    2013-10-01

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

  17. GOOSE: semantic search on internet connected sensors

    NASA Astrophysics Data System (ADS)

    Schutte, Klamer; Bomhof, Freek; Burghouts, Gertjan; van Diggelen, Jurriaan; Hiemstra, Peter; van't Hof, Jaap; Kraaij, Wessel; Pasman, Huib; Smith, Arthur; Versloot, Corne; de Wit, Joost

    2013-05-01

    More and more sensors are getting Internet connected. Examples are cameras on cell phones, CCTV cameras for traffic control as well as dedicated security and defense sensor systems. Due to the steadily increasing data volume, human exploitation of all this sensor data is impossible for effective mission execution. Smart access to all sensor data acts as enabler for questions such as "Is there a person behind this building" or "Alert me when a vehicle approaches". The GOOSE concept has the ambition to provide the capability to search semantically for any relevant information within "all" (including imaging) sensor streams in the entire Internet of sensors. This is similar to the capability provided by presently available Internet search engines which enable the retrieval of information on "all" web pages on the Internet. In line with current Internet search engines any indexing services shall be utilized cross-domain. The two main challenge for GOOSE is the Semantic Gap and Scalability. The GOOSE architecture consists of five elements: (1) an online extraction of primitives on each sensor stream; (2) an indexing and search mechanism for these primitives; (3) a ontology based semantic matching module; (4) a top-down hypothesis verification mechanism and (5) a controlling man-machine interface. This paper reports on the initial GOOSE demonstrator, which consists of the MES multimedia analysis platform and the CORTEX action recognition module. It also provides an outlook into future GOOSE development.

  18. Epigenotypes of latent herpesvirus genomes.

    PubMed

    Minarovits, J

    2006-01-01

    Epigenotypes are modified cellular or viral genotypes which differ in transcriptional activity in spite of having an identical (or nearly identical) DNA sequence. Restricted expression of latent, episomal herpesvirus genomes is also due to epigenetic modifications. There is no virus production (lytic viral replication, associated with the expression of all viral genes) in tight latency. In vitro experiments demonstrated that DNA methylation could influence the activity of latent (and/or crucial lytic) promoters of prototype strains belonging to the three herpesvirus subfamilies (alpha-, beta-, and gamma-herpesviruses). In vivo, however, DNA methylation is not a major regulator of herpes simplex virus type 1 (HSV-1, a human alpha-herpesvirus) latent gene expression in neurons of infected mice. In these cells, the promoter/enhancer region of latency-associated transcripts (LATs) is enriched with acetyl histone H3, suggesting that histone modifications may control HSV-1 latency in terminally differentiated, quiescent neurons. Epstein-Barr virus (EBV, a human gamma-herpesvirus) is associated with a series of neoplasms. Latent, episomal EBV genomes are subject to host cell-dependent epigenetic modifications (DNA methylation, binding of proteins and protein complexes, histone modifications). The distinct viral epigenotypes are associated with distinct EBV latency types, i.e., cell type-specific usage of latent EBV promoters controlling the expression of latent, growth transformation-associated EBV genes. The contribution of major epigenetic mechanisms to the regulation of latent EBV promoters is variable. DNA methylation contributes to silencing of Wp and Cp (alternative promoters for transcripts coding for the nuclear antigens EBNA 1-6) and LMP1p, LMP2Ap, and LMP2Bp (promoters for transcripts encoding transmembrane proteins). DNA methylation does not control, however, Qp (a promoter for EBNA1 transcripts only) in lymphoblastoid cell lines (LCLs), although in vitro

  19. Class-specific Gaussian-multinomial latent Dirichlet allocation for image annotation

    NASA Astrophysics Data System (ADS)

    Qian, Zhiming; Zhong, Ping; Wang, Runsheng

    2015-12-01

    Image annotation has been a challenging problem due to the well-known semantic gap between two heterogeneous information modalities, i.e., the visual modality referring to low-level visual features and the semantic modality referring to high-level human concepts. To bridge the semantic gap, we present an extension of latent Dirichlet allocation (LDA), denoted as class-specific Gaussian-multinomial latent Dirichlet allocation (csGM-LDA), in an effort to simulate the human's visual perception system. An analysis of previous supervised LDA models shows that the topics discovered by generative LDA models are driven by general image regularities rather than the semantic regularities for image annotation. To address this, csGM-LDA is introduced by using class supervision at the level of visual features for multimodal topic modeling. The csGM-LDA model combines the labeling strength of topic supervision with the flexibility of topic discovery, and the modeling problem can be effectively solved by a variational expectation-maximization (EM) algorithm. Moreover, as natural images usually generate an enormous size of high-dimensional data in annotation applications, an efficient descriptor based on Laplacian regularized uncorrelated tensor representation is proposed for explicitly exploiting the manifold structures in the high-order image space. Experimental results on two standard annotation datasets have shown the effectiveness of the proposed method by comparing with several state-of-the-art annotation methods.

  20. Environmental Attitudes Semantic Differential.

    ERIC Educational Resources Information Center

    Mehne, Paul R.; Goulard, Cary J.

    This booklet is an evaluation instrument which utilizes semantic differential data to assess environmental attitudes. Twelve concepts are included: regulated access to beaches, urban planning, dune vegetation, wetlands, future cities, reclaiming wetlands for building development, city parks, commercial development of beaches, existing cities,…

  1. Semantically Grounded Briefings

    DTIC Science & Technology

    2005-12-01

    occurring relations. AeroText and consequently AeroDAML can be tailored to particular domains through training sessions with annotated corpuses...the complexities of semantic markup by using mnemonic names for URIs, hiding unnamed intermediate objects (represented by “ GenSym ” identifiers), and

  2. Semantic and Lexical Coherence.

    ERIC Educational Resources Information Center

    Fahnestock, Jeanne

    Helping students understand coherence in terms of the lexical ties and semantic relations possible between clauses and sentences formalizes an area of writing instruction that has been somewhat vague before and makes the process of creating a coherent paragraph less mysterious. Many students do not have the intuitive knowledge base for absorbing…

  3. "Dyslexia": Toward Semantical Clarification.

    ERIC Educational Resources Information Center

    Manzo, Anthony V.; Duffelmeyer, Fred

    A formulated definition of the term dyslexia is proposed in this paper in order to clarify the semantical confusion which exists among both specialists and the general public. Dyslexia is explained as a generic term for severe and puzzling reading disability, found to be both acute (where reading-age lags 25 percent or more below mental age) and…

  4. Semantic Web Development

    DTIC Science & Technology

    2006-09-01

    many documents are not expressible in logica at all, and many in logic but not in N3. However, we are building a system for which a prime goal is the...demonstrate that conventional logica programming tools are efficent and straightforwradly adapted to semantic web work. • Jena RDF toolkit now accepts N3 as

  5. Exploring context and content links in social media: a latent space method.

    PubMed

    Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

    2012-05-01

    Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

  6. Latent geometry of bipartite networks

    NASA Astrophysics Data System (ADS)

    Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

    2017-03-01

    Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

  7. From Data to Semantic Information

    NASA Astrophysics Data System (ADS)

    Floridi, Luciano

    2003-06-01

    There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene on semantic information, and misinformation (that is, false semantic information) is not a type of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates the important implications of the revised definition for the analysis of the deflationary theories of truth, the standard definition of knowledge and the classic, quantitative theory of semantic information.

  8. SEMANTIC INFORMATION EXTRACTION FROM MULTISPECTRAL GEOSPATIAL IMAGERY VIA A FLEXIBLE FRAMEWORK

    SciTech Connect

    Gleason, Shaun Scott; Ferrell, Regina Kay; Cheriyadat, Anil M; Vatsavai, Raju; De, Soumya

    2010-01-01

    Identification and automatic labeling of facilities in high-resolution satellite images is a challenging task as the current thematic classification schemes and the low-level image features are not good enough to capture complex objects and their spatial relationships. In this paper we present a novel algorithm framework for automated semantic labeling of large image collections. The framework consists of various segmentation, feature extraction, vector quantization, and Latent Dirichlet Allocation modules. Initial experimental results show promise as well as the challenges in semantic classification technology development for nuclear proliferation monitoring.

  9. Estimation in Latent Trait Models.

    ERIC Educational Resources Information Center

    Rigdon, Steven E.; Tsutakawa, Robert K.

    Estimation of ability and item parameters in latent trait models is discussed. When both ability and item parameters are considered fixed but unknown, the method of maximum likelihood for the logistic or probit models is well known. Discussed are techniques for estimating ability and item parameters when the ability parameters or item parameters…

  10. Euphorbia Kansui Reactivates Latent HIV

    PubMed Central

    Cary, Daniele C.; Fujinaga, Koh; Peterlin, B. Matija

    2016-01-01

    While highly active anti-retroviral therapy has greatly improved the lives of HIV infected individuals, these treatments are unable to eradicate the virus. Current approaches to reactivate the virus have been limited by toxicity, lack of an orally available therapy, and limited responses in primary CD4+ T cells and in clinical trials. The PKC agonist ingenol, purified from Euphorbia plants, is a potent T cell activator and reactivates latent HIV. Euphorbia kansui itself has been used for centuries in traditional Chinese medicine to treat ascites, fluid retention, and cancer. We demonstrate that an extract of this plant, Euphorbia kansui, is capable of recapitulating T cell activation induced by the purified ingenol. Indeed, Euphorbia kansui induced expression of the early T cell activation marker CD69 and P-TEFb in a dose-dependent manner. Furthermore, Euphorbia kansui reactivated latent HIV in a CD4+ T cell model of latency and in HIV+ HAART suppressed PBMC. When combined with the other latency reversing agents, the effective dose of Euphorbia kansui required to reactive HIV was reduced 10-fold and resulted in synergistic reactivation of latent HIV. We conclude that Euphorbia Euphorbia kansui reactivates latent HIV and activates CD4+ T cells. When used in combination with a latency reversing agent, the effective dose of Euphorbia kansui is reduced; which suggests its application as a combination strategy to reactivate latent HIV while limiting the toxicity due to global T cell activation. As a natural product, which has been used in traditional medicine for thousands of years, Euphorbia kansui is attractive as a potential treatment strategy, particularly in resource poor countries with limited treatment options. Further clinical testing will be required to determine its safety with current anti-retroviral therapies. PMID:27977742

  11. A Latent Class Model for Rating Data.

    ERIC Educational Resources Information Center

    Rost, Jurgen

    1985-01-01

    A latent class model for rating data is presented which provides an alternative to the latent trait approach of analyzing test data. It is the analog of Andrich's binomial Rasch model for Lazarsfeld's latent class analysis (LCA). Response probabilities for rating categories follow a binomial distribution and depend on class-specific item…

  12. Latent Growth Modeling for Logistic Response Functions

    ERIC Educational Resources Information Center

    Choi, Jaehwa; Harring, Jeffrey R.; Hancock, Gregory R.

    2009-01-01

    Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following…

  13. Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

    PubMed Central

    Crangle, Colleen E.; Perreau-Guimaraes, Marcos; Suppes, Patrick

    2013-01-01

    This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model. PMID:23799009

  14. Large-margin predictive latent subspace learning for multiview data analysis.

    PubMed

    Chen, Ning; Zhu, Jun; Sun, Fuchun; Xing, Eric Poe

    2012-12-01

    Learning salient representations of multiview data is an essential step in many applications such as image classification, retrieval, and annotation. Standard predictive methods, such as support vector machines, often directly use all the features available without taking into consideration the presence of distinct views and the resultant view dependencies, coherence, and complementarity that offer key insights to the semantics of the data, and are therefore offering weak performance and are incapable of supporting view-level analysis. This paper presents a statistical method to learn a predictive subspace representation underlying multiple views, leveraging both multiview dependencies and availability of supervising side-information. Our approach is based on a multiview latent subspace Markov network (MN) which fulfills a weak conditional independence assumption that multiview observations and response variables are conditionally independent given a set of latent variables. To learn the latent subspace MN, we develop a large-margin approach which jointly maximizes data likelihood and minimizes a prediction loss on training data. Learning and inference are efficiently done with a contrastive divergence method. Finally, we extensively evaluate the large-margin latent MN on real image and hotel review datasets for classification, regression, image annotation, and retrieval. Our results demonstrate that the large-margin approach can achieve significant improvements in terms of prediction performance and discovering predictive latent subspace representations.

  15. Latent class model characterization of neighborhood socioeconomic status

    PubMed Central

    Michael, Yvonne; Hyslop, Terry

    2016-01-01

    Purpose Neighborhood-level socioeconomic status (NSES) can influence breast cancer mortality and poorer health outcomes are observed in deprived neighborhoods. Commonly used NSES indexes are difficult to interpret. Latent class models allow for alternative characterization of NSES for use in studies of cancer causes and control. Methods Breast cancer data was from a cohort of women diagnosed at an academic medical center in Philadelphia, PA. NSES variables were defined using Census data. Latent class modeling was used to characterize NSES. Results Complete data was available for 1,664 breast cancer patients diagnosed between 1994 and 2002. Two separate latent variables, each with 2-classes (LC2) best represented NSES. LC2 demonstrated strong associations with race and tumor stage and size. Conclusions Latent variable models identified specific characteristics associated with advantaged or disadvantaged neighborhoods, potentially improving our understanding of the impact of socioeconomic influence on breast cancer prognosis. Improved classification will enhance our ability to identify vulnerable populations and prioritize the targeting of cancer control efforts. PMID:26797452

  16. Latent period in clinical radiation myelopathy

    SciTech Connect

    Schultheiss, T.E.; Higgins, E.M.; El-Mahdi, A.M.

    1984-07-01

    Seventy-seven papers containing data on more than 300 cases of radiation myelopathy have been analyzed. The data suggest that the latent periods are similar in the cervical and thoracic levels of the spinal cord and are bimodally distributed. Myelopathy of lumbar cord apparently has a shorter latent period. As in controlled animal experiments, the latent period decreases with increasing dose. Furthermore, the variation in latent periods also decreases with dose. It is also seen that retreated patients and pediatric or adolescent patients have greatly reduced latent periods. The implications of these findings as they compare with the animal data are discussed.

  17. The UMLS Semantic Network and the Semantic Web.

    PubMed

    Kashyap, Vipul

    2003-01-01

    The Unified Medical Language System is an extensive source of biomedical knowledge developed and maintained by the US National Library of Medicine (NLM) and is being currently used in a wide variety of biomedical applications. The Semantic Network, a component of the UMLS is a structured description of core biomedical knowledge consisting of well defined semantic types and relationships between them. We investigate the expressiveness of DAML+OIL, a markup language proposed for ontologies on the Semantic Web, for representing the knowledge contained in the Semantic Network. Requirements specific to the Semantic Network, such as polymorphic relationships and blocking relationship inheritance are discussed and approaches to represent these in DAML+OIL are presented. Finally, conclusions are presented along with a discussion of ongoing and future work.

  18. Tuberculosis Infection and Latent Tuberculosis

    PubMed Central

    2016-01-01

    Active tuberculosis (TB) has a greater burden of TB bacilli than latent TB and acts as an infection source for contacts. Latent tuberculosis infection (LTBI) is the state in which humans are infected with Mycobacterium tuberculosis without any clinical symptoms, radiological abnormality, or microbiological evidence. TB is transmissible by respiratory droplet nucleus of 1–5 µm in diameter, containing 1–10 TB bacilli. TB transmission is affected by the strength of the infectious source, infectiousness of TB bacilli, immunoresistance of the host, environmental stresses, and biosocial factors. Infection controls to reduce TB transmission consist of managerial activities, administrative control, engineering control, environmental control, and personal protective equipment provision. However, diagnosis and treatment for LTBI as a national TB control program is an important strategy on the precondition that active TB is not missed. Therefore, more concrete evidences for LTBI management based on clinical and public perspectives are needed. PMID:27790271

  19. Semantic Feature Distinctiveness and Frequency

    ERIC Educational Resources Information Center

    Lamb, Katherine M.

    2012-01-01

    Lexical access is the process in which basic components of meaning in language, the lexical entries (words) are activated. This activation is based on the organization and representational structure of the lexical entries. Semantic features of words, which are the prominent semantic characteristics of a word concept, provide important information…

  20. Semantic Research for Digital Libraries.

    ERIC Educational Resources Information Center

    Chen, Hsinchun

    1999-01-01

    Discusses the need for semantic research in digital libraries to help overcome interoperability problems. Highlights include federal initiatives; semantic analysis; knowledge representations; human-computer interactions and information visualization; and the University of Illinois DLI (Digital Libraries Initiative) project through partnership with…

  1. Information tables with neighborhood semantics

    NASA Astrophysics Data System (ADS)

    Yao, Yiyu

    2000-04-01

    Information tables provide a convenient and useful tool for representing a set of objects using a group of attributes. This notion is enriched by introducing neighborhood systems on attribute values. The neighborhood systems represent the semantics relationships between, and knowledge about, attribute values. With added semantics, neighborhood based information tables may provide a more general framework for knowledge discovery, data mining, and information retrieval.

  2. 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.

  3. Latent heat of vehicular motion

    NASA Astrophysics Data System (ADS)

    Ahmadi, Farzad; Berrier, Austin; Habibi, Mohammad; Boreyko, Jonathan

    2016-11-01

    We have used the thermodynamic concept of latent heat, where a system loses energy due to a solid-to-liquid phase transition, to study the flow of a group of vehicles moving from rest. During traffic flow, drivers keep a large distance from the car in front of them to ensure safe driving. When a group of cars comes to a stop, for example at a red light, drivers voluntarily induce a "phase transition" from this "liquid phase" to a close-packed "solid phase." This phase transition is motivated by the intuition that maximizing displacement before stopping will minimize the overall travel time. To test the effects of latent heat on flow efficiency, a drone captured the dynamics of cars flowing through an intersection on a Smart Road where the initial spacing between cars at the red light was systematically varied. By correlating the experimental results with the Optimal Velocity Model (OVM), we find that the convention of inducing phase transitions at intersections offers no benefit, as the lag time (latent heat) of resumed flow offsets the initial increase in displacement. These findings suggest that in situations where gridlock is not an issue, drivers should not decrease their spacing during stoppages in order to maximize safety with no loss in flow efficiency.

  4. [Semantic information. Internal language. Thinking].

    PubMed

    Azcoaga, J E

    1993-06-01

    Semantic information has reached an objective condition after a lengthy history of semantic inquiries that instrumental neurophysiological devices--such as event-related potentials, electroencephalographic spectral analysis, regional brain circulation, PET scan, deep brain electrodes, and other--have made easier. In turn, internal language, as screened according to Vigotsky's perspective, is considered a product of semantic information circulation understood as neurosemae interconnection. Finally, in normal adults, thinking processes are assumed to be made up by both sensoperceptive information (proprioceptive information included) and semantic information. Thus, an "extraverbal thinking" can be distinguished, whose activity is hardly describable in healthy adults but should be considered as a condition of non-educated deaf persons, and a "verbal thinking", or internal language, made up by semantic information.

  5. Hierarchical abstract semantic model for image classification

    NASA Astrophysics Data System (ADS)

    Ye, Zhipeng; Liu, Peng; Zhao, Wei; Tang, Xianglong

    2015-09-01

    Semantic gap limits the performance of bag-of-visual-words. To deal with this problem, a hierarchical abstract semantics method that builds abstract semantic layers, generates semantic visual vocabularies, measures semantic gap, and constructs classifiers using the Adaboost strategy is proposed. First, abstract semantic layers are proposed to narrow the semantic gap between visual features and their interpretation. Then semantic visual words are extracted as features to train semantic classifiers. One popular form of measurement is used to quantify the semantic gap. The Adaboost training strategy is used to combine weak classifiers into strong ones to further improve performance. For a testing image, the category is estimated layer-by-layer. Corresponding abstract hierarchical structures for popular datasets, including Caltech-101 and MSRC, are proposed for evaluation. The experimental results show that the proposed method is capable of narrowing semantic gaps effectively and performs better than other categorization methods.

  6. Distributed Semantic Overlay Networks

    NASA Astrophysics Data System (ADS)

    Doulkeridis, Christos; Vlachou, Akrivi; Nørvåg, Kjetil; Vazirgiannis, Michalis

    Semantic Overlay Networks (SONs) have been recently proposed as a way to organize content in peer-to-peer (P2P) networks. The main objective is to discover peers with similar content and then form thematically focused peer groups. Efficient content retrieval can be performed by having queries selectively forwarded only to relevant groups of peers to the query. As a result, less peers need to be contacted, in order to answer a query. In this context, the challenge is to generate SONs in a decentralized and distributed manner, as the centralized assembly of global information is not feasible. Different approaches for exploiting the generated SONs for content retrieval have been proposed in the literature, which are examined in this chapter, with a particular focus on SON interconnections for efficient search. Several applications, such as P2P document and image retrieval, can be deployed over generated SONs, motivating the need for distributed and truly scalable SON creation. Therefore, recently several research papers focus on SONs as stated in our comprehensive overview of related work in the field of semantic overlay networks. A classification of existing algorithms according to a set of qualitative criteria is also provided. In spite of the rich existing work in the field of SONs, several challenges have not been efficiently addressed yet, therefore, future promising research directions are pointed out and discussed at the end of this chapter.

  7. Semantic Workflows and Provenance

    NASA Astrophysics Data System (ADS)

    Gil, Y.

    2011-12-01

    While sharing and disseminating data is widely practiced across scientific communities, we have yet to recognize the importance of sharing and disseminating the analytic processes that leads to published data. Data retrieved from shared repositories and archives is often hard to interpret because we lack documentation about those processes: what models were used, what assumptions were made, what calibrations were carried out, etc. This process documentation is also key to aggregate data in a meaningful way, whether aggregating shared third party data or aggregating shared data with local sensor data collected by individual investigators. We suggest that augmenting published data with process documentation would greatly enhance our ability to find, reuse, interpret, and aggregate data and therefore have a significant impact in the utility of data repositories and archives. We will show that semantic workflows and provenance provide key technologies for capturing process documentation. Semantic workflows describe the kinds of data transformation and analysis steps used to create new data products, and can include useful constraints about why specific models were selected or parameters chosen. Provenance records can be used to publish workflow descriptions in standard formats that can be reused to enable verification and reproducibility of data products.

  8. High density ERP indices of conscious and unconscious semantic priming.

    PubMed

    Ruz, María; Madrid, Eduardo; Lupiáñez, Juan; Tudela, Pío

    2003-10-01

    The existence of differential brain mechanisms of conscious and unconscious processing is a matter of debate nowadays. The present experiment explores whether conscious and unconscious semantic priming in a lexical decision task at a long prime-target stimulus onset asynchrony (SOA) correlate with overlapping or different event related potential (ERP) effects. Results show that the N400 effect, which appeared when words were consciously perceived, completely disappeared when primes were masked at a level where the ability of participants to detect the prime was near chance. Instead, a rather different set of ERP effects was found to index unconscious semantic priming. This suggests that the processes at the basis of conscious and unconscious semantic analyses can under some circumstances be rather different. Moreover, our results support the notion that conscious and unconscious processes are at least partially separable in the brain.

  9. The Ontological Perspectives of the Semantic Web and the Metadata Harvesting Protocol: Applications of Metadata for Improving Web Search.

    ERIC Educational Resources Information Center

    Fast, Karl V.; Campbell, D. Grant

    2001-01-01

    Compares the implied ontological frameworks of the Open Archives Initiative Protocol for Metadata Harvesting and the World Wide Web Consortium's Semantic Web. Discusses current search engine technology, semantic markup, indexing principles of special libraries and online databases, and componentization and the distinction between data and…

  10. "Pre-Semantic" Cognition Revisited: Critical Differences between Semantic Aphasia and Semantic Dementia

    ERIC Educational Resources Information Center

    Jefferies, Elizabeth; Rogers, Timothy T.; Hopper, Samantha; Lambon Ralph, Matthew A.

    2010-01-01

    Patients with semantic dementia show a specific pattern of impairment on both verbal and non-verbal "pre-semantic" tasks, e.g., reading aloud, past tense generation, spelling to dictation, lexical decision, object decision, colour decision and delayed picture copying. All seven tasks are characterised by poorer performance for items that are…

  11. Enhancing clinical concept extraction with distributional semantics

    PubMed Central

    Cohen, Trevor; Wu, Stephen; Gonzalez, Graciela

    2011-01-01

    Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first

  12. Body-part-specific representations of semantic noun categories.

    PubMed

    Carota, Francesca; Moseley, Rachel; Pulvermüller, Friedemann

    2012-06-01

    Word meaning processing in the brain involves ventrolateral temporal cortex, but a semantic contribution of the dorsal stream, especially frontocentral sensorimotor areas, has been controversial. We here examine brain activation during passive reading of object-related nouns from different semantic categories, notably animal, food, and tool words, matched for a range of psycholinguistic features. Results show ventral stream activation in temporal cortex along with category-specific activation patterns in both ventral and dorsal streams, including sensorimotor systems and adjacent pFC. Precentral activation reflected action-related semantic features of the word categories. Cortical regions implicated in mouth and face movements were sparked by food words, and hand area activation was seen for tool words, consistent with the actions implicated by the objects the words are used to speak about. Furthermore, tool words specifically activated the right cerebellum, and food words activated the left orbito-frontal and fusiform areas. We discuss our results in the context of category-specific semantic deficits in the processing of words and concepts, along with previous neuroimaging research, and conclude that specific dorsal and ventral areas in frontocentral and temporal cortex index visual and affective-emotional semantic attributes of object-related nouns and action-related affordances of their referent objects.

  13. Semantic Representation and Naming in Young Children.

    ERIC Educational Resources Information Center

    McGregor, Karla K.; Friedman, Rena M.; Reilly, Renee M.; Newman, Robyn M.

    2002-01-01

    Two experiments examined children's semantic representations and semantic naming errors. Results suggested that functional and physical properties are core aspects of object representations in the semantic lexicon and that the degree of semantic knowledge makes words more or less vulnerable to retrieval failure. Discussion focuses on the dynamic…

  14. The Semantic Distance Model of Relevance Assessment.

    ERIC Educational Resources Information Center

    Brooks, Terrence A.

    1998-01-01

    Presents the Semantic Distance Model (SDM) of Relevance Assessment, a cognitive model of the relationship between semantic distance and relevance assessment. Discusses premises of the model such as the subjective nature of information and the metaphor of semantic distance. Empirical results illustrate the effects of semantic distance and semantic…

  15. Mapping the Structure of Semantic Memory

    ERIC Educational Resources Information Center

    Morais, Ana Sofia; Olsson, Henrik; Schooler, Lael J.

    2013-01-01

    Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual's semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1-hr daily sessions. The semantic networks of individuals…

  16. Exploiting Recurring Structure in a Semantic Network

    NASA Technical Reports Server (NTRS)

    Wolfe, Shawn R.; Keller, Richard M.

    2004-01-01

    With the growing popularity of the Semantic Web, an increasing amount of information is becoming available in machine interpretable, semantically structured networks. Within these semantic networks are recurring structures that could be mined by existing or novel knowledge discovery methods. The mining of these semantic structures represents an interesting area that focuses on mining both for and from the Semantic Web, with surprising applicability to problems confronting the developers of Semantic Web applications. In this paper, we present representative examples of recurring structures and show how these structures could be used to increase the utility of a semantic repository deployed at NASA.

  17. 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.

  18. High Performance Descriptive Semantic Analysis of Semantic Graph Databases

    SciTech Connect

    Joslyn, Cliff A.; Adolf, Robert D.; al-Saffar, Sinan; Feo, John T.; Haglin, David J.; Mackey, Greg E.; Mizell, David W.

    2011-06-02

    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 comprising 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.

  19. Indexing Learning Objects: Vocabularies and Empirical Investigation of Consistency

    ERIC Educational Resources Information Center

    Kabel, Suzanne; De Hoog, Robert; Wielinga, Bob; Anjewierden, Anjo

    2004-01-01

    In addition to the LOM standard and instructional design specifications, as well as domain specific indexing vocabularies, a structured indexing vocabulary for the more elementary learning objects is advisable in order to support retrieval tasks of developers. Furthermore, because semantic indexing is seen as a difficult task, three issues…

  20. Latent Heating from TRMM Satellite Measurements

    NASA Astrophysics Data System (ADS)

    Tao, W.; Takayabu, Y. N.; Shige, S.; Lang, S. E.; Olson, W. S.

    2012-12-01

    Rainfall production is a fundamental process within the Earth's hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global Tropics and sub-tropics - information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies has been developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM measurements. These algorithms are briefly described followed by a discussion of the foremost latent heating products that can be generated from them. The investigation then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  1. Bayesian variable selection for latent class models.

    PubMed

    Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

    2011-09-01

    In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

  2. Latent variable modeling of cognitive processes in true and false recognition of words: A developmental perspective.

    PubMed

    Bouwmeester, Samantha; Verkoeijen, Peter P J L

    2010-05-01

    The present study aimed at testing theoretical predictions of the fuzzy-trace theory about true and false recognition. The effects of semantic relatedness and study opportunity on true and false recognition of words from Deese, Roediger, McDermott lists (J. Deese, 1959; D. R. Read, 1996; H. L. Roediger & K. B. McDermott, 1995) were evaluated in 7- to 12-year-old children (N = 151). Instead of a traditional analysis of variance, the authors used a relatively novel statistical analysis technique, latent class factor analysis, to test the hypotheses pertaining to the effect of semantic relatedness and study opportunity on children's true and false recognition given their low or high verbatim-trace and gist-trace level. The results showed that variation in true recognition of target words from semantically related and unrelated word lists that were either studied once or repeated could be explained well by variation in verbatim-trace and gist-trace level. Variation in false recognition of semantically related distractors also could be explained by variation in gist-trace level, but the recollection-rejection hypothesis was not confirmed. The variable age was positively but weakly related to gist-trace level, but no significant relationship was found between age and verbatim-trace level.

  3. Does semantic redundancy gain result from multiple semantic priming?

    PubMed

    Schröter, Hannes; Bratzke, Daniel; Fiedler, Anja; Birngruber, Teresa

    2015-10-01

    Fiedler, Schröter, and Ulrich (2013) reported faster responses to a single written word when the semantic content of this word (e.g., "elephant") matched both targets (e.g., "animal", "gray") as compared to a single target (e.g., "animal", "brown"). This semantic redundancy gain was explained by statistical facilitation due to a race of independent memory retrieval processes. The present experiment addresses one alternative explanation, namely that semantic redundancy gain results from multiple pre-activation of words that match both targets. In different blocks of trials, participants performed a redundant-targets task and a lexical decision task. The targets of the redundant-targets task served as primes in the lexical decision task. Replicating the findings of Fiedler et al., a semantic redundancy gain was observed in the redundant-targets task. Crucially, however, there was no evidence of a multiple semantic priming effect in the lexical decision task. This result suggests that semantic redundancy gain cannot be explained by multiple pre-activation of words that match both targets.

  4. WORD STATISTICS IN THE GENERATION OF SEMANTIC TOOLS FOR INFORMATION SYSTEMS.

    ERIC Educational Resources Information Center

    STONE, DON C.

    ONE OF THE PROBLEMS IN INFORMATION STORAGE AND RETRIEVAL SYSTEMS OF TECHNICAL DOCUMENTS IS THE INTERPRETATION OF WORDS USED TO INDEX DOCUMENTS. SEMANTIC TOOLS, DEFINED AS CHANNELS FOR THE COMMUNICATION OF WORD MEANINGS BETWEEN TECHNICAL EXPERTS, DOCUMENT INDEXERS, AND SEARCHERS, PROVIDE ONE METHOD OF DEALING WITH THE PROBLEM OF MULTIPLE…

  5. Problem Solving with General Semantics.

    ERIC Educational Resources Information Center

    Hewson, David

    1996-01-01

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

  6. Nine Principles of Semantic Harmonization.

    PubMed

    Cunningham, James A; Van Speybroeck, Michel; Kalra, Dipak; Verbeeck, Rudi

    2016-01-01

    Medical data is routinely collected, stored and recorded across different institutions and in a range of different formats. Semantic harmonization is the process of collating this data into a singular consistent logical view, with many approaches to harmonizing both possible and valid. The broad scope of possibilities for undertaking semantic harmonization do lead however to the development of bespoke and ad-hoc systems; this is particularly the case when it comes to cohort data, the format of which is often specific to a cohort's area of focus. Guided by work we have undertaken in developing the 'EMIF Knowledge Object Library', a semantic harmonization framework underpinning the collation of pan-European Alzheimer's cohort data, we have developed a set of nine generic guiding principles for developing semantic harmonization frameworks, the application of which will establish a solid base for constructing similar frameworks.

  7. Nine Principles of Semantic Harmonization

    PubMed Central

    Cunningham, James A.; Van Speybroeck, Michel; Kalra, Dipak; Verbeeck, Rudi

    2016-01-01

    Medical data is routinely collected, stored and recorded across different institutions and in a range of different formats. Semantic harmonization is the process of collating this data into a singular consistent logical view, with many approaches to harmonizing both possible and valid. The broad scope of possibilities for undertaking semantic harmonization do lead however to the development of bespoke and ad-hoc systems; this is particularly the case when it comes to cohort data, the format of which is often specific to a cohort’s area of focus. Guided by work we have undertaken in developing the ‘EMIF Knowledge Object Library’, a semantic harmonization framework underpinning the collation of pan-European Alzheimer’s cohort data, we have developed a set of nine generic guiding principles for developing semantic harmonization frameworks, the application of which will establish a solid base for constructing similar frameworks. PMID:28269840

  8. Distributed semantic networks and CLIPS

    NASA Technical Reports Server (NTRS)

    Snyder, James; Rodriguez, Tony

    1991-01-01

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

  9. Estimating and Interpreting Latent Variable Interactions: A Tutorial for Applying the Latent Moderated Structural Equations Method

    ERIC Educational Resources Information Center

    Maslowsky, Julie; Jager, Justin; Hemken, Douglas

    2015-01-01

    Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS)…

  10. Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

    ERIC Educational Resources Information Center

    Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

    2011-01-01

    Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

  11. Semi-Nonparametric Methods for Detecting Latent Non-Normality: A Fusion of Latent Trait and Ordered Latent Class Modeling

    ERIC Educational Resources Information Center

    Schmitt, J. Eric; Mehta, Paras D.; Aggen, Steven H.; Kubarych, Thomas S.; Neale, Michael C.

    2006-01-01

    Ordered latent class analysis (OLCA) can be used to approximate unidimensional latent distributions. The main objective of this study is to evaluate the method of OLCA in detecting non-normality of an unobserved continuous variable (i.e., a common factor) used to explain the covariation between dichotomous item-level responses. Using simulation,…

  12. The Latent Structure of Autistic Traits: A Taxometric, Latent Class and Latent Profile Analysis of the Adult Autism Spectrum Quotient

    ERIC Educational Resources Information Center

    James, Richard J.; Dubey, Indu; Smith, Danielle; Ropar, Danielle; Tunney, Richard J.

    2016-01-01

    Autistic traits are widely thought to operate along a continuum. A taxometric analysis of Adult Autism Spectrum Quotient data was conducted to test this assumption, finding little support but identifying a high severity taxon. To understand this further, latent class and latent profile models were estimated that indicated the presence of six…

  13. NASA and The Semantic Web

    NASA Technical Reports Server (NTRS)

    Ashish, Naveen

    2005-01-01

    We provide an overview of several ongoing NASA endeavors based on concepts, systems, and technology from the Semantic Web arena. Indeed NASA has been one of the early adopters of Semantic Web Technology and we describe ongoing and completed R&D efforts for several applications ranging from collaborative systems to airspace information management to enterprise search to scientific information gathering and discovery systems at NASA.

  14. Semantic Interoperability on the Web

    DTIC Science & Technology

    2000-01-01

    these agents would not be affected by presentation changes if the pages were available in XML, they would still break if the XML representation of the... these semantics into tools that are used to interpret or translate the XML documents, but software tools cannot acquire these semantics independently...mapping differences in naming conventions. As with natural language, XML DTDs have the problems of polysemy and synonymy. For example, the elements

  15. Neural substrates of semantic memory.

    PubMed

    Hart, John; Anand, Raksha; Zoccoli, Sandra; Maguire, Mandy; Gamino, Jacque; Tillman, Gail; King, Richard; Kraut, Michael A

    2007-09-01

    Semantic memory is described as the storage of knowledge, concepts, and information that is common and relatively consistent across individuals (e.g., memory of what is a cup). These memories are stored in multiple sensorimotor modalities and cognitive systems throughout the brain (e.g., how a cup is held and manipulated, the texture of a cup's surface, its shape, its function, that is related to beverages such as coffee, and so on). Our ability to engage in purposeful interactions with our environment is dependent on the ability to understand the meaning and significance of the objects and actions around us that are stored in semantic memory. Theories of the neural basis of the semantic memory of objects have produced sophisticated models that have incorporated to varying degrees the results of cognitive and neural investigations. The models are grouped into those that are (1) cognitive models, where the neural data are used to reveal dissociations in semantic memory after a brain lesion occurs; (2) models that incorporate both cognitive and neuroanatomical information; and (3) models that use cognitive, neuroanatomic, and neurophysiological data. This review highlights the advances and issues that have emerged from these models and points to future directions that provide opportunities to extend these models. The models of object memory generally describe how category and/or feature representations encode for object memory, and the semantic operations engaged in object processing. The incorporation of data derived from multiple modalities of investigation can lead to detailed neural specifications of semantic memory organization. The addition of neurophysiological data can potentially provide further elaboration of models to include semantic neural mechanisms. Future directions should incorporate available and newly developed techniques to better inform the neural underpinning of semantic memory models.

  16. Semantic preview benefit during reading.

    PubMed

    Hohenstein, Sven; Kliegl, Reinhold

    2014-01-01

    Word features in parafoveal vision influence eye movements during reading. The question of whether readers extract semantic information from parafoveal words was studied in 3 experiments by using a gaze-contingent display change technique. Subjects read German sentences containing 1 of several preview words that were replaced by a target word during the saccade to the preview (boundary paradigm). In the 1st experiment the preview word was semantically related or unrelated to the target. Fixation durations on the target were shorter for semantically related than unrelated previews, consistent with a semantic preview benefit. In the 2nd experiment, half the sentences were presented following the rules of German spelling (i.e., previews and targets were printed with an initial capital letter), and the other half were presented completely in lowercase. A semantic preview benefit was obtained under both conditions. In the 3rd experiment, we introduced 2 further preview conditions, an identical word and a pronounceable nonword, while also manipulating the text contrast. Whereas the contrast had negligible effects, fixation durations on the target were reliably different for all 4 types of preview. Semantic preview benefits were greater for pretarget fixations closer to the boundary (large preview space) and, although not as consistently, for long pretarget fixation durations (long preview time). The results constrain theoretical proposals about eye movement control in reading. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

  17. Semantic Similarities between a Keyword Database and a Controlled Vocabulary Database: An Investigation in the Antibiotic Resistance Literature.

    ERIC Educational Resources Information Center

    Qin, Jian

    2000-01-01

    Explores similarities or dissimilarities between citation-semantic and analytic indexing based on a study of records in the Science Citation Index and MEDLINE databases on antibiotic resistance in pneumonia. Concludes that disparate indexing terms may be an advantage for better recall and precision in information retrieval. (Contains 42…

  18. Cognitive Diagnosis Using Latent Trait Models.

    ERIC Educational Resources Information Center

    Samejima, Fumiko

    This paper discusses the competency space approach to diagnosing misconceptions, skill, and knowledge acquisition. In some approaches that combine misconceptions, skill, and knowledge acquisition, the latent ability theta is used more or less as an insignificant element, but in the competency space approach, a multidimensional latent space is…

  19. Latent Heating from TRMM Satellite Measurements

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Smith, E.; Olson, W.

    2005-01-01

    Rainfall production is a fundamental process within the Earth;s hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations with the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional amount of rainfall over the global Tropics and sub-tropics - information which can be used to estimate the spacetime structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies has and continues to be developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM measurements. These algorithms are briefly described followed by a discussion of the foremost latent heating products that can be generate from them. The investigation then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  20. Introduction to Latent Class Analysis with Applications

    ERIC Educational Resources Information Center

    Porcu, Mariano; Giambona, Francesca

    2017-01-01

    Latent class analysis (LCA) is a statistical method used to group individuals (cases, units) into classes (categories) of an unobserved (latent) variable on the basis of the responses made on a set of nominal, ordinal, or continuous observed variables. In this article, we introduce LCA in order to demonstrate its usefulness to early adolescence…

  1. A Vernacular for Linear Latent Growth Models

    ERIC Educational Resources Information Center

    Hancock, Gregory R.; Choi, Jaehwa

    2006-01-01

    In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit…

  2. Latent Memory for Sensitization in "Aplysia"

    ERIC Educational Resources Information Center

    Philips, Gary T.; Tzvetkova, Ekaterina I.; Marinesco, Stephane; Carew, Thomas J.

    2006-01-01

    In the analysis of memory it is commonly observed that, even after a memory is apparently forgotten, its latent presence can still be revealed in a subsequent learning task. Although well established on a behavioral level, the mechanisms underlying latent memory are not well understood. To begin to explore these mechanisms, we have used "Aplysia,"…

  3. Consequences of Fitting Nonidentified Latent Class Models

    ERIC Educational Resources Information Center

    Abar, Beau; Loken, Eric

    2012-01-01

    Latent class models are becoming more popular in behavioral research. When models with a large number of latent classes relative to the number of manifest indicators are estimated, researchers must consider the possibility that the model is not identified. It is not enough to determine that the model has positive degrees of freedom. A well-known…

  4. Tracking the dynamics of divergent thinking via semantic distance: Analytic methods and theoretical implications.

    PubMed

    Hass, Richard W

    2017-02-01

    Divergent thinking has often been used as a proxy measure of creative thinking, but this practice lacks a foundation in modern cognitive psychological theory. This article addresses several issues with the classic divergent-thinking methodology and presents a new theoretical and methodological framework for cognitive divergent-thinking studies. A secondary analysis of a large dataset of divergent-thinking responses is presented. Latent semantic analysis was used to examine the potential changes in semantic distance between responses and the concept represented by the divergent-thinking prompt across successive response iterations. The results of linear growth modeling showed that although there is some linear increase in semantic distance across response iterations, participants high in fluid intelligence tended to give more distant initial responses than those with lower fluid intelligence. Additional analyses showed that the semantic distance of responses significantly predicted the average creativity rating given to the response, with significant variation in average levels of creativity across participants. Finally, semantic distance does not seem to be related to participants' choices of their own most creative responses. Implications for cognitive theories of creativity are discussed, along with the limitations of the methodology and directions for future research.

  5. Variable Assessment in Latent Class Models

    PubMed Central

    Zhang, Q.; Ip, E. H.

    2014-01-01

    The latent class model provides an important platform for jointly modeling mixed-mode data — i.e., discrete and continuous data with various parametric distributions. Multiple mixed-mode variables are used to cluster subjects into latent classes. While the mixed-mode latent class analysis is a powerful tool for statisticians, few studies are focused on assessing the contribution of mixed-mode variables in discriminating latent classes. Novel measures are derived for assessing both absolute and relative impacts of mixed-mode variables in latent class analysis. Specifically, the expected posterior gradient and the Kolmogorov variation of the posterior distribution, as well as related properties are studied. Numerical results are presented to illustrate the measures. PMID:24910486

  6. Latent inhibition in human adults without masking.

    PubMed

    Escobar, Martha; Arcediano, Francisco; Miller, Ralph R

    2003-09-01

    Latent inhibition refers to attenuated responding to Cue X observed when the X-outcome pairings are preceded by X-alone presentations. It has proven difficult to obtain in human adults unless the preexposure (X-alone) presentations are embedded within a masking (i.e., distracting) task. The authors hypothesized that the difficulty in obtaining latent inhibition with unmasked tasks is related to the usual training procedures, in which the preexposure and conditioning experiences are separated by a set of instructions. Experiment 1 reports latent inhibition without masking in a task in which preexposure and conditioning occur without interruption. Experiments 2 and 3 demonstrate that this attenuation in responding to target Cue X does not pass a summation test for conditioned inhibition and is context specific, thereby confirming that it is latent inhibition. Experiments 3 and 4 confirm that introducing instructions between preexposure and conditioning disrupts latent inhibition.

  7. Epstein-Barr virus latent genes.

    PubMed

    Kang, Myung-Soo; Kieff, Elliott

    2015-01-23

    Latent Epstein-Barr virus (EBV) infection has a substantial role in causing many human disorders. The persistence of these viral genomes in all malignant cells, yet with the expression of limited latent genes, is consistent with the notion that EBV latent genes are important for malignant cell growth. While the EBV-encoded nuclear antigen-1 (EBNA-1) and latent membrane protein-2A (LMP-2A) are critical, the EBNA-leader proteins, EBNA-2, EBNA-3A, EBNA-3C and LMP-1, are individually essential for in vitro transformation of primary B cells to lymphoblastoid cell lines. EBV-encoded RNAs and EBNA-3Bs are dispensable. In this review, the roles of EBV latent genes are summarized.

  8. Behavioral Scale Reliability and Measurement Invariance Evaluation Using Latent Variable Modeling

    ERIC Educational Resources Information Center

    Raykov, Tenko

    2004-01-01

    A latent variable modeling approach to reliability and measurement invariance evaluation for multiple-component measuring instruments is outlined. An initial discussion deals with the limitations of coefficient alpha, a frequently used index of composite reliability. A widely and readily applicable structural modeling framework is next described…

  9. A Latent Trait Look at Pretest-Posttest Validation of Criterion-referenced Test Items.

    ERIC Educational Resources Information Center

    van der Linden, Wim J.

    1981-01-01

    Using criterion-referenced test item data collected in an empirical study, differences in item selection between Cox and Vargas' pretest-posttest validity index and a latent trait approach (evaluation of the item information function for the mastery score) are analyzed. (Author/GK)

  10. Group Comparisons in the Presence of Missing Data Using Latent Variable Modeling Techniques

    ERIC Educational Resources Information Center

    Raykov, Tenko; Marcoulides, George A.

    2010-01-01

    A latent variable modeling approach for examining population similarities and differences in observed variable relationship and mean indexes in incomplete data sets is discussed. The method is based on the full information maximum likelihood procedure of model fitting and parameter estimation. The procedure can be employed to test group identities…

  11. Latent Heating from TRMM Satellite Measurements

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Smith, E. A.; Adler, R.; Haddad, Z.; Hou, A.; Iguchi, T.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.

    2004-01-01

    Rainfall production is the fundamental variable within the Earth's hydrological cycle because it is both the principal forcing term in surface water budgets and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the tropics - as well as modifying the energetic efficiencies of midlatitude weather systems. This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global tropics and sub-tropics, information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. The paper examines how the observed TRMM distribution of rainfall has advanced an understanding of the global water and energy cycle and its consequent relationship to the atmospheric general circulation and climate via latent heat release. A set of algorithm methodologies that are being used to estimate latent heating based on rain rate retrievals from the TRMM observations are described. The characteristics of these algorithms and the latent heating products that can be generated from them are also described, along with validation analyses of the heating products themselves. Finally, the investigation provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  12. NCBO Technology: Powering semantically aware applications.

    PubMed

    Whetzel, Patricia L

    2013-04-15

    As new biomedical technologies are developed, the amount of publically available biomedical data continues to increase. To help manage these vast and disparate data sources, researchers have turned to the Semantic Web. Specifically, ontologies are used in data annotation, natural language processing, information retrieval, clinical decision support, and data integration tasks. The development of software applications to perform these tasks requires the integration of Web services to incorporate the wide variety of ontologies used in the health care and life sciences. The National Center for Biomedical Ontology, a National Center for Biomedical Computing created under the NIH Roadmap, developed BioPortal, which provides access to one of the largest repositories of biomedical ontologies. The NCBO Web services provide programmtic access to these ontologies and can be grouped into four categories; Ontology, Mapping, Annotation, and Data Access. The Ontology Web services provide access to ontologies, their metadata, ontology versions, downloads, navigation of the class hierarchy (parents, children, siblings) and details of each term. The Mapping Web services provide access to the millions of ontology mappings published in BioPortal. The NCBO Annotator Web service "tags" text automatically with terms from ontologies in BioPortal, and the NCBO Resource Index Web services provides access to an ontology-based index of public, online data resources. The NCBO Widgets package the Ontology Web services for use directly in Web sites. The functionality of the NCBO Web services and widgets are incorporated into semantically aware applications for ontology development and visualization, data annotation, and data integration. This overview will describe these classes of applications, discuss a few examples of each type, and which NCBO Web services are used by these applications.

  13. Effective Web and Desktop Retrieval with Enhanced Semantic Spaces

    NASA Astrophysics Data System (ADS)

    Daoud, Amjad M.

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

  14. A Methodology for Simulating Net-Centric Technologies: An Operations Research Approach

    DTIC Science & Technology

    2007-06-01

    Keywords: Captured enemy documents, latent semantic text analysis, network centric warfare, network science, language instant screening tool...document in any language (currently being developed in Arabic), index it for keywords through advanced latent semantic text analysis, append...metadata (GPS, date-time, keywords, unit), and relay the information to higher in near real time. Latent semantic text

  15. Orientation field estimation for latent fingerprint enhancement.

    PubMed

    Feng, Jianjiang; Zhou, Jie; Jain, Anil K

    2013-04-01

    Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint orientation field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.

  16. Action semantics modulate action prediction.

    PubMed

    Springer, Anne; Prinz, Wolfgang

    2010-11-01

    Previous studies have demonstrated that action prediction involves an internal action simulation that runs time-locked to the real action. The present study replicates and extends these findings by indicating a real-time simulation process (Graf et al., 2007), which can be differentiated from a similarity-based evaluation of internal action representations. Moreover, results showed that action semantics modulate action prediction accuracy. The semantic effect was specified by the processing of action verbs and concrete nouns (Experiment 1) and, more specifically, by the dynamics described by action verbs (Experiment 2) and the speed described by the verbs (e.g., "to catch" vs. "to grasp" vs. "to stretch"; Experiment 3). These results propose a linkage between action simulation and action semantics as two yet unrelated domains, a view that coincides with a recent notion of a close link between motor processes and the understanding of action language.

  17. Semantic processing of crowded stimuli?

    PubMed

    Huckauf, Anke; Knops, Andre; Nuerk, Hans-Christoph; Willmes, Klaus

    2008-11-01

    Effects of semantic processing of crowded characters were investigated using numbers as stimuli. In an identification task, typical spacing effects in crowding were replicated. Using the same stimuli in a magnitude comparison task, a smaller effect of spacing was observed as well as an effect of response congruency. These effects were replicated in a second experiment with varying stimulus-onset asynchronies. In addition, decreasing performance with increasing onset-asynchrony (so-called type-B masking) for incongruent flankers indicates semantic processing of target and flankers. The data show that semantic processing takes place even in crowded stimuli. This argues strongly against common accounts of crowding in terms of early stimulus-driven impairments of processing.

  18. Ontology Matching with Semantic Verification

    PubMed Central

    Jean-Mary, Yves R.; Shironoshita, E. Patrick; Kabuka, Mansur R.

    2009-01-01

    ASMOV (Automated Semantic Matching of Ontologies with Verification) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies. PMID:20186256

  19. Recent advances in testing for latent TB.

    PubMed

    Schluger, Neil W; Burzynski, Joseph

    2010-12-01

    After more than a century of relying on skin testing for the diagnosis of latent TB infection, clinicians now have access to blood-based diagnostics in the form of interferon γ release assays (IGRAs). These tests are generally associated with higher sensitivity and specificity for diagnosis of latent TB infection. This article reviews the indications for testing and treatment of latent TB infection in the overall context of a TB control program and describes how IGRAs might be used in specific clinical settings and populations, including people having close contact with an active case of TB, the foreign born, and health-care workers.

  20. Extraction of latent images from printed media

    NASA Astrophysics Data System (ADS)

    Sergeyev, Vladislav; Fedoseev, Victor

    2015-12-01

    In this paper we propose an automatic technology for extraction of latent images from printed media such as documents, banknotes, financial securities, etc. This technology includes image processing by adaptively constructed Gabor filter bank for obtaining feature images, as well as subsequent stages of feature selection, grouping and multicomponent segmentation. The main advantage of the proposed technique is versatility: it allows to extract latent images made by different texture variations. Experimental results showing performance of the method over another known system for latent image extraction are given.

  1. Semantic Web Research Trends and Directions

    DTIC Science & Technology

    2006-01-01

    social trust on the semantic web that builds upon the previous work to create end user applications that benefit from the semantic foundation. 2 Swoop...security, authentication, and privacy. However, the social component of trust is one that is both important and ideally suited for the Semantic Web. When the...Semantic Web-based social networks are augmented with trust information, it is possible to make computations over the values, and integrate the

  2. Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science databases.

    PubMed

    Kobayashi, Norio; Ishii, Manabu; Takahashi, Satoshi; Mochizuki, Yoshiki; Matsushima, Akihiro; Toyoda, Tetsuro

    2011-07-01

    Global cloud frameworks for bioinformatics research databases become huge and heterogeneous; solutions face various diametric challenges comprising cross-integration, retrieval, security and openness. To address this, as of March 2011 organizations including RIKEN published 192 mammalian, plant and protein life sciences databases having 8.2 million data records, integrated as Linked Open or Private Data (LOD/LPD) using SciNetS.org, the Scientists' Networking System. The huge quantity of linked data this database integration framework covers is based on the Semantic Web, where researchers collaborate by managing metadata across public and private databases in a secured data space. This outstripped the data query capacity of existing interface tools like SPARQL. Actual research also requires specialized tools for data analysis using raw original data. To solve these challenges, in December 2009 we developed the lightweight Semantic-JSON interface to access each fragment of linked and raw life sciences data securely under the control of programming languages popularly used by bioinformaticians such as Perl and Ruby. Researchers successfully used the interface across 28 million semantic relationships for biological applications including genome design, sequence processing, inference over phenotype databases, full-text search indexing and human-readable contents like ontology and LOD tree viewers. Semantic-JSON services of SciNetS.org are provided at http://semanticjson.org.

  3. Scene Classfication Based on the Semantic-Feature Fusion Fully Sparse Topic Model for High Spatial Resolution Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Zhu, Qiqi; Zhong, Yanfei; Zhang, Liangpei

    2016-06-01

    Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features - the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

  4. Semantic processing in information retrieval.

    PubMed Central

    Rindflesch, T. C.; Aronson, A. R.

    1993-01-01

    Intuition suggests that one way to enhance the information retrieval process would be the use of phrases to characterize the contents of text. A number of researchers, however, have noted that phrases alone do not improve retrieval effectiveness. In this paper we briefly review the use of phrases in information retrieval and then suggest extensions to this paradigm using semantic information. We claim that semantic processing, which can be viewed as expressing relations between the concepts represented by phrases, will in fact enhance retrieval effectiveness. The availability of the UMLS domain model, which we exploit extensively, significantly contributes to the feasibility of this processing. PMID:8130547

  5. Bootstrapping to a Semantic Grid

    SciTech Connect

    Schwidder, Jens; Talbott, Tara; Myers, James D.

    2005-02-28

    The Scientific Annotation Middleware (SAM) is a set of components and services that enable researchers, applications, problem solving environments (PSE) and software agents to create metadata and annotations about data objects and document the semantic relationships between them. Developed starting in 2001, SAM allows applications to encode metadata within files or to manage metadata at the level of individual relationships as desired. SAM then provides mechanisms to expose metadata and relation¬ships encoded either way as WebDAV properties. In this paper, we report on work to further map this metadata into RDF and discuss the role of middleware such as SAM in bridging between traditional and semantic grid applications.

  6. Language networks in semantic dementia.

    PubMed

    Agosta, Federica; Henry, Roland G; Migliaccio, Raffaella; Neuhaus, John; Miller, Bruce L; Dronkers, Nina F; Brambati, Simona M; Filippi, Massimo; Ogar, Jennifer M; Wilson, Stephen M; Gorno-Tempini, Maria Luisa

    2010-01-01

    Cognitive deficits in semantic dementia have been attributed to anterior temporal lobe grey matter damage; however, key aspects of the syndrome could be due to altered anatomical connectivity between language pathways involving the temporal lobe. The aim of this study was to investigate the left language-related cerebral pathways in semantic dementia using diffusion tensor imaging-based tractography and to combine the findings with cortical anatomical and functional magnetic resonance imaging data obtained during a reading activation task. The left inferior longitudinal fasciculus, arcuate fasciculus and fronto-parietal superior longitudinal fasciculus were tracked in five semantic dementia patients and eight healthy controls. The left uncinate fasciculus and the genu and splenium of the corpus callosum were also obtained for comparison with previous studies. From each tract, mean diffusivity, fractional anisotropy, as well as parallel and transverse diffusivities were obtained. Diffusion tensor imaging results were related to grey and white matter atrophy volume assessed by voxel-based morphometry and functional magnetic resonance imaging activations during a reading task. Semantic dementia patients had significantly higher mean diffusivity, parallel and transverse in the inferior longitudinal fasciculus. The arcuate and uncinate fasciculi demonstrated significantly higher mean diffusivity, parallel and transverse and significantly lower fractional anisotropy. The fronto-parietal superior longitudinal fasciculus was relatively spared, with a significant difference observed for transverse diffusivity and fractional anisotropy, only. In the corpus callosum, the genu showed lower fractional anisotropy compared with controls, while no difference was found in the splenium. The left parietal cortex did not show significant volume changes on voxel-based morphometry and demonstrated normal functional magnetic resonance imaging activation in response to reading items that

  7. Language networks in semantic dementia

    PubMed Central

    Agosta, Federica; Henry, Roland G.; Migliaccio, Raffaella; Neuhaus, John; Miller, Bruce L.; Dronkers, Nina F.; Brambati, Simona M.; Filippi, Massimo; Ogar, Jennifer M.; Wilson, Stephen M.

    2010-01-01

    Cognitive deficits in semantic dementia have been attributed to anterior temporal lobe grey matter damage; however, key aspects of the syndrome could be due to altered anatomical connectivity between language pathways involving the temporal lobe. The aim of this study was to investigate the left language-related cerebral pathways in semantic dementia using diffusion tensor imaging-based tractography and to combine the findings with cortical anatomical and functional magnetic resonance imaging data obtained during a reading activation task. The left inferior longitudinal fasciculus, arcuate fasciculus and fronto-parietal superior longitudinal fasciculus were tracked in five semantic dementia patients and eight healthy controls. The left uncinate fasciculus and the genu and splenium of the corpus callosum were also obtained for comparison with previous studies. From each tract, mean diffusivity, fractional anisotropy, as well as parallel and transverse diffusivities were obtained. Diffusion tensor imaging results were related to grey and white matter atrophy volume assessed by voxel-based morphometry and functional magnetic resonance imaging activations during a reading task. Semantic dementia patients had significantly higher mean diffusivity, parallel and transverse in the inferior longitudinal fasciculus. The arcuate and uncinate fasciculi demonstrated significantly higher mean diffusivity, parallel and transverse and significantly lower fractional anisotropy. The fronto-parietal superior longitudinal fasciculus was relatively spared, with a significant difference observed for transverse diffusivity and fractional anisotropy, only. In the corpus callosum, the genu showed lower fractional anisotropy compared with controls, while no difference was found in the splenium. The left parietal cortex did not show significant volume changes on voxel-based morphometry and demonstrated normal functional magnetic resonance imaging activation in response to reading items that

  8. Abstraction and natural language semantics.

    PubMed Central

    Kayser, Daniel

    2003-01-01

    According to the traditional view, a word prototypically denotes a class of objects sharing similar features, i.e. it results from an abstraction based on the detection of common properties in perceived entities. I explore here another idea: words result from abstraction of common premises in the rules governing our actions. I first argue that taking 'inference', instead of 'reference', as the basic issue in semantics does matter. I then discuss two phenomena that are, in my opinion, particularly difficult to analyse within the scope of traditional semantic theories: systematic polysemy and plurals. I conclude by a discussion of my approach, and by a summary of its main features. PMID:12903662

  9. Examining Lateralized Semantic Access Using Pictures

    ERIC Educational Resources Information Center

    Lovseth, Kyle; Atchley, Ruth Ann

    2010-01-01

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

  10. Semantic Relatedness for Evaluation of Course Equivalencies

    ERIC Educational Resources Information Center

    Yang, Beibei

    2012-01-01

    Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the…

  11. Semantic Weight and Verb Retrieval in Aphasia

    ERIC Educational Resources Information Center

    Barde, Laura H. F.; Schwartz, Myrna F.; Boronat, Consuelo B.

    2006-01-01

    Individuals with agrammatic aphasia may have difficulty with verb production in comparison to nouns. Additionally, they may have greater difficulty producing verbs that have fewer semantic components (i.e., are semantically "light") compared to verbs that have greater semantic weight. A connectionist verb-production model proposed by Gordon and…

  12. Chinese Character Decoding: A Semantic Bias?

    ERIC Educational Resources Information Center

    Williams, Clay; Bever, Thomas

    2010-01-01

    The effects of semantic and phonetic radicals on Chinese character decoding were examined. Our results suggest that semantic and phonetic radicals are each available for access when a corresponding task emphasizes one or the other kind of radical. But in a more neutral lexical recognition task, the semantic radical is more informative. Semantic…

  13. Metasemantics: On the Limits of Semantic Theory

    ERIC Educational Resources Information Center

    Parent, T.

    2009-01-01

    METASEMANTICS is a wake-up call for semantic theory: It reveals that some semantic questions have no adequate answer. (This is meant to be the "epistemic" point that certain semantic questions cannot be "settled"--not a metaphysical point about whether there is a fact-of-the-matter.) METASEMANTICS thus checks our default "optimism" that any…

  14. Retrieved Latent Heating from TRMM

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Smith, Eric A.; Houze Jr, Robert

    2008-01-01

    The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for three-fourths of the total heat energy available to the Earth's atmosphere. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to meso-synoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations in environmental prediction models. The status of retrieved TRMM LH products, TRMM LH inter-comparison and validation project, current TRMM LH applications and critic issues/action items (based on previous five TRMM LH workshops) is presented in this article.

  15. Latent Work and Latent Heat of the Liquid/Vapor Transformation

    DTIC Science & Technology

    2014-08-01

    latent heat and latent work of liquid/vapor phase transformation for variously constrained thermodynamic processes . thermodynamics, phase...1. Introduction 1 2. Latent Heat and Work of Thermodynamic Process 3 3. Equations of Phase Equilibrium 5 4. Vaporization/Condensation under Fixed...between the phase in the process of vaporization/condensation. Thermodynamical identities allow one to express p, T, and µ in terms of the derivatives of

  16. Reactivation of Latent Viruses in Space

    NASA Technical Reports Server (NTRS)

    Pierson, D. L.; Mehta, S. K.; Tyring, S. K.; Lugg, D. J.

    1999-01-01

    Reactivation of latent viruses is an important health risk for people working and living in physically isolated extreme environments such as Antarctica and space. Preflight quarantine does not significantly reduce the risk associated with latent viruses, however, pharmaceutical countermeasures are available for some viruses. The molecular basis of latency is not fully understood, but physical and psychosocial stresses are known to initiate the reactivation of latent viruses. Presumably, stress induced changes in selected hormones lead to alterations in the cell- mediated immune (CMI) response resulting in increased shedding of latent viruses. Limited access to space makes the use of ground-based analogs essential. The Australian Antarctic stations serve as a good stress model and simulate many aspects of space flight. Closed environmental chambers have been used to simulate space flight since the Skylab missions and have also proven to be a valuable analog of selected aspects of space flight.

  17. On the Latent Variable Interpretation in Sum-Product Networks.

    PubMed

    Peharz, Robert; Gens, Robert; Pernkopf, Franz; Domingos, Pedro

    2016-11-18

    One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

  18. Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation.

    PubMed

    Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu

    2016-09-20

    In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate and fast retrieval method for breast histopathological image. Specifically, the method presents local statistical feature of nuclei for morphology and distribution of nuclei, and employs Gabor feature to describe texture information. Latent Dirichlet Allocation model is utilized for high-level semantic mining. Locality- Sensitive Hashing is used to speed up the search. Experiments on a WSI database with over 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, WSI archiving and management.

  19. An Efficient Web Page Ranking for Semantic Web

    NASA Astrophysics Data System (ADS)

    Chahal, P.; Singh, M.; Kumar, S.

    2014-01-01

    With the enormous amount of information presented on the web, the retrieval of relevant information has become a serious problem and is also the topic of research for last few years. The most common tools to retrieve information from web are search engines like Google. The Search engines are usually based on keyword searching and indexing of web pages. This approach is not very efficient as the result-set of web pages obtained include large irrelevant pages. Sometimes even the entire result-set may contain lot of irrelevant pages for the user. The next generation of search engines must address this problem. Recently, many semantic web search engines have been developed like Ontolook, Swoogle, which help in searching meaningful documents presented on semantic web. In this process the ranking of the retrieved web pages is very crucial. Some attempts have been made in ranking of semantic web pages but still the ranking of these semantic web documents is neither satisfactory and nor up to the user's expectations. In this paper we have proposed a semantic web based document ranking scheme that relies not only on the keywords but also on the conceptual instances present between the keywords. As a result only the relevant page will be on the top of the result-set of searched web pages. We explore all relevant relations between the keywords exploring the user's intention and then calculate the fraction of these relations on each web page to determine their relevance. We have found that this ranking technique gives better results than those by the prevailing methods.

  20. Action representation: crosstalk between semantics and pragmatics.

    PubMed

    Prinz, Wolfgang

    2014-03-01

    Marc Jeannerod pioneered a representational approach to movement and action. In his approach, motor representations provide both, declarative knowledge about action and procedural knowledge for action (action semantics and action pragmatics, respectively). Recent evidence from language comprehension and action simulation supports the claim that action pragmatics and action semantics draw on common representational resources, thus challenging the traditional divide between declarative and procedural action knowledge. To account for these observations, three kinds of theoretical frameworks are discussed: (i) semantics is grounded in pragmatics, (ii) pragmatics is anchored in semantics, and (iii) pragmatics is part and parcel of semantics.

  1. Semantic Annotation of Computational Components

    NASA Technical Reports Server (NTRS)

    Vanderbilt, Peter; Mehrotra, Piyush

    2004-01-01

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

  2. Semantic Borders and Incomplete Understanding.

    PubMed

    Silva-Filho, Waldomiro J; Dazzani, Maria Virgínia

    2016-03-01

    In this article, we explore a fundamental issue of Cultural Psychology, that is our "capacity to make meaning", by investigating a thesis from contemporary philosophical semantics, namely, that there is a decisive relationship between language and rationality. Many philosophers think that for a person to be described as a rational agent he must understand the semantic content and meaning of the words he uses to express his intentional mental states, e.g., his beliefs and thoughts. Our argument seeks to investigate the thesis developed by Tyler Burge, according to which our mastery or understanding of the semantic content of the terms which form our beliefs and thoughts is an "incomplete understanding". To do this, we discuss, on the one hand, the general lines of anti-individualism or semantic externalism and, on the other, criticisms of the Burgean notion of incomplete understanding - one radical and the other moderate. We defend our understanding that the content of our beliefs must be described in the light of the limits and natural contingencies of our cognitive capacities and the normative nature of our rationality. At heart, anti-individualism leads us to think about the fact that we are social creatures, living in contingent situations, with important, but limited, cognitive capacities, and that we receive the main, and most important, portion of our knowledge simply from what others tell us. Finally, we conclude that this discussion may contribute to the current debate about the notion of borders.

  3. Incrementally Dissociating Syntax and Semantics

    ERIC Educational Resources Information Center

    Brennan, Jonathan R.

    2010-01-01

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

  4. Genres, Semantics, and Classroom Education.

    ERIC Educational Resources Information Center

    Lemke, Jay

    1988-01-01

    Argues that competence in academic subjects depends on mastery of their specialized patterns of language use. These patterns are described in terms of: 1) the semantics underlying Halliday's functional linguistics and 2) the structural analysis of communication genres. A sample classroom episode illustrates relationships among semantic…

  5. A Note on Semantic Selection.

    ERIC Educational Resources Information Center

    Endo, Yoshio

    1989-01-01

    The notions of categorical selection (c-selection) and semantic selection (s-selection) as outlined in recent research on generative grammar are discussed. The first section addresses the type of selectional constraint imposed on English small clauses (e.g., "John considers [Mary smart]"). In the second section, it is suggested that the constraint…

  6. Taxonomic and Thematic Semantic Systems.

    PubMed

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

    2017-03-23

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

  7. Semantic Fission through Dialect Fusion.

    ERIC Educational Resources Information Center

    Linn, Michael D.

    The linguistic atlas projects have provided much information on the regional distribution of pronunciation, vocabulary, and syntax and have given important evidence for a greater understanding of problems involved in semantic change, particularly in pointing out transition areas where dialects become fused. In a study supplementary to that…

  8. The Semantic Web in Education

    ERIC Educational Resources Information Center

    Ohler, Jason

    2008-01-01

    The semantic web or Web 3.0 makes information more meaningful to people by making it more understandable to machines. In this article, the author examines the implications of Web 3.0 for education. The author considers three areas of impact: knowledge construction, personal learning network maintenance, and personal educational administration.…

  9. Colourful Semantics: A Clinical Investigation

    ERIC Educational Resources Information Center

    Bolderson, Sarah; Dosanjh, Christine; Milligan, Claudine; Pring, Tim; Chiat, Shula

    2011-01-01

    Children with language difficulties often omit verbs and grammatical elements and fail to complete sentences. Bryan (1997) described "colourful semantics", a therapy she used to treat a 5-year-old boy. The therapy uses colour coding to highlight the predicate argument structure of sentences. This study further tested the therapy's…

  10. Semantic Preview Benefit during Reading

    ERIC Educational Resources Information Center

    Hohenstein, Sven; Kliegl, Reinhold

    2014-01-01

    Word features in parafoveal vision influence eye movements during reading. The question of whether readers extract semantic information from parafoveal words was studied in 3 experiments by using a gaze-contingent display change technique. Subjects read German sentences containing 1 of several preview words that were replaced by a target word…

  11. Event-related potential study of dynamic neural mechanisms of semantic organizational strategies in verbal learning.

    PubMed

    Blanchet, Sophie; Gagnon, Geneviève; Bastien, Célyne

    2007-09-19

    Neuroimaging and neuropsychological data indicate that the frontal regions are implicated in semantic organizational strategies in verbal learning. Whereas these approaches tend to adopt a localizationist view, we used event-related potentials (ERPs) to investigate the dynamic neural mechanisms involved in these strategies. We recorded ERPs using a 128-channel system in 12 young adults (23.75+/-3.02 years) during 3 encoding conditions that manipulated the levels of semantic organization demands. In the Unrelated condition, the words to encode did not share any semantic attributes. For both Spontaneous and Guided conditions, the words in each list were drawn from four semantic categories. In the Spontaneous condition, participants were not informed about the semantic relationship between items. In contrast, in the Guided condition, participants were instructed to improve their subsequent recall by mentally regrouping related items with the aid of category labels. Results indicated that the P200 amplitude increased with the greater organizational demand of semantic strategies. In contrast, the late positive component (LPC) amplitude was larger in both encoding conditions with semantic related words regardless of their instructions as compared to the Unrelated condition. Finally, there was greater right frontal sustained activity in the Spontaneous condition than in the Unrelated condition. Thus, our data indicate that the P200 is sensitive to attentional processes that increase with the organizational semantic demand. The LPC indexes associative processes voluntarily involved in linking related items together. Finally, the right frontal region appears to play an important role in the self-initiation of semantic organizational strategies.

  12. Latent Semantic Analysis As a Tool for Learner Positioning in Learning Networks for Lifelong Learning

    ERIC Educational Resources Information Center

    van Bruggen, Jan; Sloep, Peter; van Rosmalen, Peter; Brouns, Francis; Vogten, Hubert; Koper, Rob; Tattersall, Colin

    2004-01-01

    As we move towards distributed, self-organised learning networks for lifelong learning to which multiple providers contribute content, there is a need to develop new techniques to determine where learners can be positioned in these networks. Positioning requires us to map characteristics of the learner onto characteristics of learning materials…

  13. Automatic Evaluation for E-Learning Using Latent Semantic Analysis: A Use Case

    ERIC Educational Resources Information Center

    Farrus, Mireia; Costa-jussa, Marta R.

    2013-01-01

    Assessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. The current paper aims at analysing and discussing some of the most state-of-the-art assessment systems in education. Later, this work presents a specific use case developed for the Universitat Oberta de…

  14. Habituation, latent inhibition, and extinction.

    PubMed

    Jordan, Wesley P; Todd, Travis P; Bucci, David J; Leaton, Robert N

    2015-06-01

    In two conditioned suppression experiments with a latent inhibition (LI) design, we measured the habituation of rats in preexposure, their LI during conditioning, and then extinction over days. In the first experiment, lick suppression, the preexposed group (PE) showed a significant initial unconditioned response (UR) to the target stimulus and significant long-term habituation (LTH) of that response over days. The significant difference between the PE and nonpreexposed (NPE) groups on the first conditioning trial was due solely to the difference in their URs to the conditioned stimulus (CS)-a habituated response (PE) and an unhabituated response (NPE). In the second experiment, bar-press suppression, little UR to the target stimulus was apparent during preexposure, and no detectable LTH. Thus, there was no difference between the PE and NPE groups on the first conditioning trial. Whether the UR to the CS confounds the interpretation of LI (Exp. 1) or not (Exp. 2) can only be known if the UR is measured. In both experiments, LI was observed in acquisition. Also in both experiments, rats that were preexposed and then conditioned to asymptote were significantly more resistant to extinction than were the rats not preexposed. This result contrasts with the consistently reported finding that preexposure either produces less resistance to extinction or has no effect on extinction. The effect of stimulus preexposure survived conditioning to asymptote and was reflected directly in extinction. These two experiments provide a cautionary procedural note for LI experiments and have shown an unexpected extinction effect that may provide new insights into the interpretation of LI.

  15. The effect of semantic categorisation on recall memory in amnesia.

    PubMed

    Channon, S; Daum, I

    2000-01-01

    Amnesic patients were compared to a healthy control group on recall of word lists containing semantically-related or unrelated words. As expected on the basis of previous literature, the amnesic group performed below the control group on all measures of recall. When total recall scores for each list were used as the index of performance, their scores were not significantly affected by the type of list, unlike those of the control group. Comparison of serial position effects for different parts of the lists revealed that the control group derived greater benefit from semantic relatedness in recall of items from the middle positions. This effect was not shown by the amnesic group, who showed similar U-shaped serial position curves for recall of all three lists, and appeared to use a more passive recall strategy than the control group. The findings are discussed in relation to our current understanding of amnesic deficits.

  16. Knowledge networks in the age of the Semantic Web.

    PubMed

    Neumann, Eric; Prusak, Larry

    2007-05-01

    The Web has become the major medium for various communities to share their knowledge. To this end, it provides an optimal environment for knowledge networks. The web offers global connectivity that is virtually instantaneous, and whose resources and documents can easily be indexed for easy searching. In the coupled realms of biomedical research and healthcare, this has become especially important where today many thousands of communities already exist that connect across academia, hospitals and industry. These communities also rely on several forms of knowledge assets, including publications, experimental data, domain-specific vocabularies and policies. Web-based communities will be one of the earlier beneficiaries of the emerging Semantic Web. With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.

  17. Lexical Semantics and Irregular Inflection

    PubMed Central

    Huang, Yi Ting; Pinker, Steven

    2010-01-01

    Whether a word has an irregular inflection does not depend on its sound alone: compare lie-lay (recline) and lie-lied (prevaricate). Theories of morphology, particularly connectionist and symbolic models, disagree on which nonphonological factors are responsible. We test four possibilities: (1) Lexical effects, in which two lemmas differ in whether they specify an irregular form; (2) Semantic effects, in which the semantic features of a word become associated with regular or irregular forms; (3) Morphological structure effects, in which a word with a headless structure (e.g., a verb derived from a noun) blocks access to a stored irregular form; (4) Compositionality effects, in which the stored combination of an irregular word’s meaning (e.g., the verb’s inherent aspect) with the meaning of the inflection (e.g., pastness) doesn’t readily transfer to new senses with different combinations of such meanings. In four experiments, speakers were presented with existing and novel verbs and asked to rate their past-tense forms, semantic similarities, grammatical structure, and aspectual similarities. We found (1) an interaction between semantic and phonological similarity, coinciding with reported strategies of analogizing to known verbs and implicating lexical effects; (2) weak and inconsistent effects of semantic similarity; (3) robust effects of morphological structure, and (4) robust effects of aspectual compositionality. Results are consistent with theories of language that invoke lexical entries and morphological structure, and which differentiate the mode of storage of regular and irregular verbs. They also suggest how psycholinguistic processes have shaped vocabulary structure over history. PMID:21151703

  18. An Experiment in Scientific Code Semantic Analysis

    NASA Technical Reports Server (NTRS)

    Stewart, Mark E. M.

    1998-01-01

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

  19. Synonym extraction and abbreviation expansion with ensembles of semantic spaces

    PubMed Central

    2014-01-01

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

  20. Continuous Time Dynamic Topic Models

    DTIC Science & Technology

    2008-06-20

    called topics, can be used to explain the observed collection. LDA is a probabilistic extension of latent semantic indexing (LSI) [5] and probabilistic... latent semantic indexing (pLSI) [11]. Owing to its formal generative semantics, LDA has been extended and applied to authorship [19], email [15...Steyvers. Probabilistic topic models. In Latent Semantic Analysis: A Road to Meaning. 2006. [9] T. L. Griffiths and M. Steyvers. Finding scientific

  1. Unsupervised Semantic Labeling Framework for Identification of Complex Facilities in High-resolution Remote Sensing Images

    SciTech Connect

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

    2010-01-01

    Nuclear proliferation is a major national security concern for many countries. 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 an unsupervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 70 images collected under different spatial and temporal settings over the globe representing two major semantic categories: nuclear and coal power plants. Initial experimental results show a reasonable discrimination of these two categories even though they share highly overlapping and common objects. This research also identified several research challenges associated with nuclear proliferation monitoring using high resolution remote sensing images.

  2. Category-specific semantic deficits in Alzheimer's disease: a semantic priming study.

    PubMed

    Hernández, Mireia; Costa, Albert; Juncadella, Montserrat; Sebastián-Gallés, Núria; Reñé, Ramón

    2008-03-07

    Category-specific semantic deficits in individuals suffering brain damage after relatively focal lesions provide an important source of evidence about the organization of semantic knowledge. However, whether Alzheimer's disease (AD), in which the brain damage is more widespread, affects semantic categories to a different extent is still controversial. In the present study, we assess this issue by means of the semantic priming technique. AD patients with a mild impairment of their semantic knowledge showed comparable priming effects to that of controls for the categories of animals and artifacts. Interestingly, however, patients with a moderate impairment of their semantic knowledge showed a normal priming effect for animals but a very reduced priming effect (if any) for artifacts. These results reveal that AD may affect the semantic knowledge of different semantic categories to a different extent. The implications of this observation for current theoretical accounts of semantic representation in the brain are discussed.

  3. Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension

    PubMed Central

    Günther, Fritz; Dudschig, Carolin; Kaup, Barbara

    2016-01-01

    In two experiments, we attempted to replicate and extend findings by Günther et al. (2016) that word similarity measures obtained from distributional semantics models—Latent Semantic Analysis (LSA) and Hyperspace Analog to Language (HAL)—predict lexical priming effects. To this end, we used the pseudo-random method to generate item material while systematically controlling for word similarities introduced by Günther et al. (2016) which was based on LSA cosine similarities (Experiment 1) and HAL cosine similarities (Experiment 2). Extending the original study, we used semantic spaces created from far larger corpora, and implemented several additional methodological improvements. In Experiment 1, we only found a significant effect of HAL cosines on lexical decision times, while we found significant effects for both LSA and HAL cosines in Experiment 2. As further supported by an analysis of the pooled data from both experiments, this indicates that HAL cosines are a better predictor of priming effects than LSA cosines. Taken together, the results replicate the finding that priming effects can be predicted from distributional semantic similarity measures. PMID:27822195

  4. Author Indexing.

    ERIC Educational Resources Information Center

    Diodato, Virgil P.

    1981-01-01

    Discusses the effectiveness of using author-supplied indexing to increase subject control in information retrieval, and describes a study which compared author indexing for articles published in "American Mathematical Society" journals to indexing of the same articles by an editor of "Mathematical Reviews." Nine references are…

  5. The Aging Semantic Differential in Mandarin Chinese: Measuring Attitudes toward Older Adults in China.

    PubMed

    Gonzales, Ernest; Marchiondo, Lisa A; Tan, Jing; Wang, Yi; Chen, Huajuan

    2017-02-16

    The Aging Semantic Differential (ASD) is the most widely used instrument to measure young people's attitudes towards older adults. This study translated the ASD to Mandarin and examined its psychometric properties. The Mandarin-ASD contains three latent factors (Personality and Mental Health, Societal Participation, and Physical) that have high internal reliability and reasonable discriminate validity. Social work researchers, practitioners and allied professionals may utilize the ASD-Mandarin instrument to measure young people's attitudes towards older adults in China. We issue a call for a universal-ASD that can be applied across different cultural contexts.

  6. Latent Curve Models and Latent Change Score Models Estimated in R

    ERIC Educational Resources Information Center

    Ghisletta, Paolo; McArdle, John J.

    2012-01-01

    In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R…

  7. 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.

  8. Taxometrics, Polytomous Constructs, and the Comparison Curve Fit Index: A Monte Carlo Analysis

    ERIC Educational Resources Information Center

    Walters, Glenn D.; McGrath, Robert E.; Knight, Raymond A.

    2010-01-01

    The taxometric method effectively distinguishes between dimensional (1-class) and taxonic (2-class) latent structure, but there is virtually no information on how it responds to polytomous (3-class) latent structure. A Monte Carlo analysis showed that the mean comparison curve fit index (CCFI; Ruscio, Haslam, & Ruscio, 2006) obtained with 3…

  9. Adopting Abstract Images for Semantic Scene Understanding.

    PubMed

    Zitnick, C Lawrence; Vedantam, Ramakrishna; Parikh, Devi

    2016-04-01

    Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual information from an image, which is in general a difficult and yet unsolved problem. In this paper, we propose studying semantic information in abstract images created from collections of clip art. Abstract images provide several advantages over real images. They allow for the direct study of how to infer high-level semantic information, since they remove the reliance on noisy low-level object, attribute and relation detectors, or the tedious hand-labeling of real images. Importantly, abstract images also allow the ability to generate sets of semantically similar scenes. Finding analogous sets of real images that are semantically similar would be nearly impossible. We create 1,002 sets of 10 semantically similar abstract images with corresponding written descriptions. We thoroughly analyze this dataset to discover semantically important features, the relations of words to visual features and methods for measuring semantic similarity. Finally, we study the relation between the saliency and memorability of objects and their semantic importance.

  10. Are poker players all the same? Latent class analysis.

    PubMed

    Dufour, Magali; Brunelle, Natacha; Roy, Élise

    2015-06-01

    Poker is the gambling game that is currently gaining the most in popularity. However, there is little information on poker players' characteristics and risk factors. Furthermore, the first studies described poker players, often recruited in universities, as an homogeneous group who played in only one of the modes (land based or on the Internet). This study aims to identify, through latent class analyses, poker player subgroups. A convenience sample of 258 adult poker players was recruited across Quebec during special events or through advertising in various media. Participants filled out a series of questionnaires (Canadian Problem Gambling Index, Beck Depression, Beck Anxiety, erroneous belief and alcohol/drug consumption). The latent class analysis suggests that there are three classes of poker players. Class I (recreational poker players) includes those who have the lowest probability of engaging intensively in different game modes. Participants in class II (Internet poker players) all play poker on the Internet. This class includes the highest proportion of players who consider themselves experts or professionals. They make a living in part or in whole from poker. Class III (multiform players) includes participants with the broadest variety of poker patterns. This group is complex: these players are positioned halfway between professional and recreational players. Results indicate that poker players are not an homogeneous group identified simply on the basis of the form of poker played. The specific characteristics associated with each subgroup points to vulnerabilities that could potentially be targeted for preventive interventions.

  11. Latent classes in the developmental trajectories of infant handedness.

    PubMed

    Michel, George F; Babik, Iryna; Sheu, Ching-Fan; Campbell, Julie M

    2014-02-01

    Handedness for acquiring objects was assessed monthly from 6 to 14 months in 328 infants (182 males). A group based trajectory model identified 3 latent groups with different developmental trajectories: those with an identifiable right preference (38%) or left preference (14%) and those without an identifiable preference (48%) but with a significant trend toward right-handedness. Each group exhibited significant quadratic trends: Those with a right preference increased to asymptote at about 10 months and began decreasing thereafter; those with a left preference increased to asymptote at about 11 months; those without a preference exhibited increasing right-hand use. Since adult handedness reflects different patterns of neural organization which relate to differences in psychological functioning, the observed differences in infant handedness development may relate to differences in the development of infant neurobehavioral organization and functioning. Several methods were used to explore the relation of latent classes to more conventional ways of classifying infant handedness. Classification into handedness groups according to either a monthly z-score or a combination of 4 or fewer months for a handedness index failed to provide reliable estimates of handedness identified by the trajectory analysis. If identified trajectories of handedness development relate to the development of the infant's neurobehavioral organization, researchers who assess infant handedness only once in order to relate it to cognitive, social and emotional functioning may risk misclassifying the handedness of as many as 37-45% of infants.

  12. The evolution of semantic systems.

    PubMed

    Bainbridge, William Sims

    2004-05-01

    Semantic or cultural systems are sets of concepts connected by meaningful relationships, and they exhibit properties similar to those of populations of biological organisms. Drawing upon ideas from evolutionary biology and methods from information technology, this article explores the potential for research and engineering on the evolution of semantic systems. Such work in cultural genetics requires two things: (1) a rigorous but evolving taxonomic system to categorize cultural artifacts, elements, and clusters, and (2) a set of hypotheses about the processes that cause evolutionary change. This article illustrates systematic approaches to cultural taxonomy with data on the popular ideology of the space program, science fiction motion pictures, nanotechnology books, and nanotechnology research grants. It offers hypotheses derived from evolutionary and population biology that might be useful in explaining cultural evolution.

  13. The Formal Semantics of PVS

    NASA Technical Reports Server (NTRS)

    Owre, Sam; Shankar, Natarajan

    1999-01-01

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

  14. Semantic priming of familiar songs.

    PubMed

    Johnson, Sarah K; Halpern, Andrea R

    2012-05-01

    We explored the functional organization of semantic memory for music by comparing priming across familiar songs both within modalities (Experiment 1, tune to tune; Experiment 3, category label to lyrics) and across modalities (Experiment 2, category label to tune; Experiment 4, tune to lyrics). Participants judged whether or not the target tune or lyrics were real (akin to lexical decision tasks). We found significant priming, analogous to linguistic associative-priming effects, in reaction times for related primes as compared to unrelated primes, but primarily for within-modality comparisons. Reaction times to tunes (e.g., "Silent Night") were faster following related tunes ("Deck the Hall") than following unrelated tunes ("God Bless America"). However, a category label (e.g., Christmas) did not prime tunes from within that category. Lyrics were primed by a related category label, but not by a related tune. These results support the conceptual organization of music in semantic memory, but with potentially weaker associations across modalities.

  15. Semantic Event Correlation Using Ontologies

    NASA Astrophysics Data System (ADS)

    Moser, Thomas; Roth, Heinz; Rozsnyai, Szabolcs; Mordinyi, Richard; Biffl, Stefan

    Complex event processing (CEP) is a software architecture paradigm that aims at low latency, high throughput, and quick adaptability of applications for supporting and improving event-driven business processes. Events sensed in real time are the basic information units on which CEP applications operate and react in self-contained decision cycles based on defined processing logic and rules. Event correlation is necessary to relate events gathered from various sources for detecting patterns and situations of interest in the business context. Unfortunately, event correlation has been limited to syntactically identical attribute values instead of addressing semantically equivalent attribute meanings. Semantic equivalence is particularly relevant if events come from organizations that use different terminologies for common concepts.

  16. An Electrophysiological Investigation of Semantic Incongruity Processing by People with Asperger's Syndrome

    ERIC Educational Resources Information Center

    Ring, Howard; Sharma, Simeran; Wheelwright, Sally; Barrett, Geoff

    2007-01-01

    The aim of this study was to investigate whether a physiological measure of impaired use of context could be obtained in people with Asperger's Syndrome (AS). The experimental paradigm employed was the use of electroencephalography to measure the detection of semantic incongruity within written sentences, as indexed by an N400 event-related…

  17. A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

    ERIC Educational Resources Information Center

    Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

    2011-01-01

    This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…

  18. Brain Responses to Lexical-Semantic Priming in Children At-Risk for Dyslexia

    ERIC Educational Resources Information Center

    Torkildsen, Janne von Koss; Syversen, Gro; Simonsen, Hanne Gram; Moen, Inger; Lindgren, Magnus

    2007-01-01

    Deviances in early event-related potential (ERP) components reflecting auditory and phonological processing are well-documented in children at familial risk for dyslexia. However, little is known about brain responses which index processing in other linguistic domains such as lexicon, semantics and syntax in this group. The present study…

  19. Integration of Sentence-Level Semantic Information in Parafovea: Evidence from the RSVP-Flanker Paradigm.

    PubMed

    Zhang, Wenjia; Li, Nan; Wang, Xiaoyue; Wang, Suiping

    2015-01-01

    During text reading, the parafoveal word was usually presented between 2° and 5° from the point of fixation. Whether semantic information of parafoveal words can be processed during sentence reading is a critical and long-standing issue. Recently, studies using the RSVP-flanker paradigm have shown that the incongruent parafoveal word, presented as right flanker, elicited a more negative N400 compared with the congruent parafoveal word. This suggests that the semantic information of parafoveal words can be extracted and integrated during sentence reading, because the N400 effect is a classical index of semantic integration. However, as most previous studies did not control the word-pair congruency of the parafoveal and the foveal words that were presented in the critical triad, it is still unclear whether such integration happened at the sentence level or just at the word-pair level. The present study addressed this question by manipulating verbs in Chinese sentences to yield either a semantically congruent or semantically incongruent context for the critical noun. In particular, the interval between the critical nouns and verbs was controlled to be 4 or 5 characters. Thus, to detect the incongruence of the parafoveal noun, participants had to integrate it with the global sentential context. The results revealed that the N400 time-locked to the critical triads was more negative in incongruent than in congruent sentences, suggesting that parafoveal semantic information can be integrated at the sentence level during Chinese reading.

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

    PubMed

    Küper, Kristina; Heil, Martin

    2009-04-03

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

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

    PubMed Central

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

    2014-01-01

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

  2. Morphological Cues for Lexical Semantics

    DTIC Science & Technology

    1996-06-01

    father, and turned thirty-graduate school has been a long varied haul and I could never have made it alone. Thus it is a good time to thank all the...distinction between world and linguistic knowledge has a long history in philosophy. Whether the distinction exists and if so how it should be drawn are...explaining human language acquisition. Language semantics cueing is more promising from a computational perspective and consequently has a long (for

  3. Entropy, semantic relatedness and proximity.

    PubMed

    Hahn, Lance W; Sivley, Robert M

    2011-09-01

    Although word co-occurrences within a document have been demonstrated to be semantically useful, word interactions over a local range have been largely neglected by psychologists due to practical challenges. Shannon's (Bell Systems Technical Journal, 27, 379-423, 623-665, 1948) conceptualization of information theory suggests that these interactions should be useful for understanding communication. Computational advances make an examination of local word-word interactions possible for a large text corpus. We used Brants and Franz's (2006) dataset to generate conditional probabilities for 62,474 word pairs and entropy calculations for 9,917 words in Nelson, McEvoy, and Schreiber's (Behavior Research Methods, Instruments, & Computers, 36, 402-407, 2004) free association norms. Semantic associativity correlated moderately with the probabilities and was stronger when the two words were not adjacent. The number of semantic associates for a word and the entropy of a word were also correlated. Finally, language entropy decreases from 11 bits for single words to 6 bits per word for four-word sequences. The probabilities and entropies discussed here are included in the supplemental materials for the article.

  4. Semantic priming in the prime task effect: evidence of automatic semantic processing of distractors.

    PubMed

    Marí-Beffa, P; Fuentes, L J; Catena, A; Houghton, G

    2000-06-01

    The automaticity of the semantic processing of words has been questioned because of the reduction of semantic priming when the prime word is processed nonsemantically--for example, in letter search (the prime task effect). In two experiments, prime distractor words produced semantic priming in a subsequent lexical decision task, but with the direction of priming (positive or negative) depending on the prime task. Lexico-semantic tasks produced negative semantic priming, whereas letter search produced positive semantic priming. These results are discussed in terms of task-based inhibition. We argue that, given the results from the distractors, the absence of semantic priming does not indicate an absence of semantic activation but reflects the action of control processes on prepotent responses when less practiced responses are needed.

  5. Tensor Decompositions for Learning Latent Variable Models

    DTIC Science & Technology

    2012-12-08

    of a tensor, 2011. arXiv:1004.4953. [CSC+12] S. B. Cohen, K. Stratos, M. Collins, D. P. Foster, and L. Ungar . Spectral learning of latent-variable...12] P. S. Dhillon, J. Rodu, M. Collins, D. P. Foster, and L. H. Ungar . Spectral dependency parsing with latent variables. In EMNLP-CoNLL, 2012. [DS07...Foster, J. Rodu, and L. H. Ungar . Spectral dimensionality reduction for HMMs, 2012. arXiv:1203.6130. [GvL96] G. H. Golub and C. F. van Loan. Matrix

  6. Intractable diarrhoea of infancy and latent otomastoiditis.

    PubMed Central

    Salazar de Sousa, J; da Silva, A; da Costa Ribeiro, V

    1980-01-01

    In 16 infants with intractable diarrhoea, latent otomastoiditis was found in 9 (3 at necropsy and 6 at myringotomy-antrotomy). In 5 of the 6 operated group, surgery was followed by a striking cessation of the diarrhoea and with weight gain. It is concluded that (1) latent otomastoiditis may be a perpetuating factor in intractable diarrhoea; (2) myringotomy-antrotomy should be considered if other forms of treatment have failed, and especially if there is leucocytosis; (3) mastoiditis with diffuse osteitis seems to be associated with a poor prognosis. PMID:7458392

  7. Measures of discrimination for latent group-based trajectory models.

    PubMed

    Shah, Nilesh H; Hipwell, Alison E; Stepp, Stephanie D; Chang, Chung-Chou H

    2015-01-01

    In clinical research, patient care decisions are often easier to make if patients are classified into a manageable number of groups based on homogeneous risk patterns. Investigators can use latent group-based trajectory modeling to estimate the posterior probabilities that an individual will be classified into a particular group of risk patterns. Although this method is increasingly used in clinical research, there is currently no measure that can be used to determine whether an individual's group assignment has a high level of discrimination. In this study, we propose a discrimination index and provide confidence intervals of the probability of the assigned group for each individual. We also propose a modified form of entropy to measure discrimination. The two proposed measures were applied to assess the group assignments of the longitudinal patterns of conduct disorders among young adolescent girls.

  8. “Pre-semantic” cognition revisited: Critical differences between semantic aphasia and semantic dementia

    PubMed Central

    Jefferies, Elizabeth; Rogers, Timothy T.; Hopper, Samantha; Lambon Ralph, Matthew A.

    2009-01-01

    Patients with semantic dementia show a specific pattern of impairment on both verbal and non-verbal “pre-semantic” tasks: e.g., reading aloud, past tense generation, spelling to dictation, lexical decision, object decision, colour decision and delayed picture copying. All seven tasks are characterised by poorer performance for items that are atypical of the domain and “regularisation errors” (irregular/atypical items are produced as if they were domain-typical). The emergence of this pattern across diverse tasks in the same patients indicates that semantic memory plays a key role in all of these types of “pre-semantic” processing. However, this claim remains controversial because semantically-impaired patients sometimes fail to show an influence of regularity. This study demonstrates that (a) the location of brain damage and (b) the underlying nature of the semantic deficit affect the likelihood of observing the expected relationship between poor comprehension and regularity effects. We compared the effect of multimodal semantic impairment in the context of semantic dementia and stroke aphasia on the seven “pre-semantic” tasks listed above. In all of these tasks, the semantic aphasia patients were less sensitive to typicality than the semantic dementia patients, even though the two groups obtained comparable scores on semantic tests. The semantic aphasia group also made fewer regularisation errors and many more unrelated and perseverative responses. We propose that these group differences reflect the different locus for the semantic impairment in the two conditions: patients with semantic dementia have degraded semantic representations, whereas semantic aphasia patients show deregulated semantic cognition with concomitant executive deficits. These findings suggest a reinterpretation of single case studies of comprehension-impaired aphasic patients who fail to show the expected effect of regularity on “pre-semantic” tasks. Consequently, such

  9. SSWAP: A Simple Semantic Web Architecture and Protocol for Semantic Web Services

    Technology Transfer Automated Retrieval System (TEKTRAN)

    SSWAP (Simple Semantic Web Architecture and Protocol) is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP is the driving technology behind the Virtual Plant Information Network, an NSF-funded semantic w...

  10. Explaining semantic short-term memory deficits: Evidence for the critical role of semantic control

    PubMed Central

    Hoffman, Paul; Jefferies, Elizabeth; Lambon Ralph, Matthew A.

    2011-01-01

    Patients with apparently selective short-term memory (STM) deficits for semantic information have played an important role in developing multi-store theories of STM and challenge the idea that verbal STM is supported by maintaining activation in the language system. We propose that semantic STM deficits are not as selective as previously thought and can occur as a result of mild disruption to semantic control processes, i.e., mechanisms that bias semantic processing towards task-relevant aspects of knowledge and away from irrelevant information. We tested three semantic STM patients with tasks that tapped four aspects of semantic control: (i) resolving ambiguity between word meanings, (ii) sensitivity to cues, (iii) ignoring irrelevant information and (iv) detecting weak semantic associations. All were impaired in conditions requiring more semantic control, irrespective of the STM demands of the task, suggesting a mild, but task-general, deficit in regulating semantic knowledge. This mild deficit has a disproportionate effect on STM tasks because they have high intrinsic control demands: in STM tasks, control is required to keep information active when it is no longer available in the environment and to manage competition between items held in memory simultaneously. By re-interpreting the core deficit in semantic STM patients in this way, we are able to explain their apparently selective impairment without the need for a specialised STM store. Instead, we argue that semantic STM patients occupy the mildest end of spectrum of semantic control disorders. PMID:21195105

  11. Representations for Semantic Learning Webs: Semantic Web Technology in Learning Support

    ERIC Educational Resources Information Center

    Dzbor, M.; Stutt, A.; Motta, E.; Collins, T.

    2007-01-01

    Recent work on applying semantic technologies to learning has concentrated on providing novel means of accessing and making use of learning objects. However, this is unnecessarily limiting: semantic technologies will make it possible to develop a range of educational Semantic Web services, such as interpretation, structure-visualization, support…

  12. Semantic Clustering of Search Engine Results

    PubMed Central

    Soliman, Sara Saad; El-Sayed, Maged F.; Hassan, Yasser F.

    2015-01-01

    This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision. PMID:26933673

  13. Synonyms Provide Semantic Preview Benefit in English

    PubMed Central

    Schotter, Elizabeth R.

    2013-01-01

    While orthographic and phonological preview benefits in reading are uncontroversial (see Schotter, Angele, & Rayner, 2012 for a review), researchers have debated the existence of semantic preview benefit with positive evidence in Chinese and German, but no support in English. Two experiments, using the gazecontingent boundary paradigm (Rayner, 1975), show that semantic preview benefit can be observed in English when the preview and target are synonyms (share the same or highly similar meaning, e.g., curlers-rollers). However, no semantic preview benefit was observed for semantic associates (e.g., curlers-styling). These different preview conditions represent different degrees to which the meaning of the sentence changes when the preview is replaced by the target. When this continuous variable (determined by a norming procedure) was used as the predictor in the analyses, there was a significant relationship between it and all reading time measures, suggesting that similarity in meaning between what is accessed parafoveally and what is processed foveally may be an important influence on the presence of semantic preview benefit. Why synonyms provide semantic preview benefit in reading English is discussed in relation to (1) previous failures to find semantic preview benefit in English and (2) the fact that semantic preview benefit is observed in other languages even for non-synonymous words. Semantic preview benefit is argued to depend on several factors—attentional resources, depth of orthography, and degree of similarity between preview and target. PMID:24347813

  14. Similarity Based Semantic Web Service Match

    NASA Astrophysics Data System (ADS)

    Peng, Hui; Niu, Wenjia; Huang, Ronghuai

    Semantic web service discovery aims at returning the most matching advertised services to the service requester by comparing the semantic of the request service with an advertised service. The semantic of a web service are described in terms of inputs, outputs, preconditions and results in Ontology Web Language for Service (OWL-S) which formalized by W3C. In this paper we proposed an algorithm to calculate the semantic similarity of two services by weighted averaging their inputs and outputs similarities. Case study and applications show the effectiveness of our algorithm in service match.

  15. Taxometric and Factor Analytic Models of Anxiety Sensitivity among Youth: Exploring the Latent Structure of Anxiety Psychopathology Vulnerability

    ERIC Educational Resources Information Center

    Bernstein, Amit; Zvolensky, Michael J.; Stewart, Sherry; Comeau, Nancy

    2007-01-01

    This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), a well-established affect-sensitivity individual difference factor, among youth by employing taxometric and factor analytic approaches in an integrative manner. Taxometric analyses indicated that AS, as indexed by the Child Anxiety Sensitivity…

  16. 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

  17. Immune Function and Reactivation of Latent Viruses

    NASA Technical Reports Server (NTRS)

    Butel, Janet S.

    1999-01-01

    A major concern associated with long-duration space flight is the possibility of infectious diseases posing an unacceptable medical risk to crew members. One major hypothesis addressed in this project is that space flight will cause alterations in the immune system that will allow latent viruses that are endogenous in the human population to reactivate and shed to higher levels than normal, which may affect the health of crew members. The second major hypothesis being examined is that the effects of space flight will alter the mucosal immune system, the first line of defense against many microbial infections, including herpesviruses, polyomaviruses, and gastroenteritis viruses, rendering crew members more susceptible to virus infections across the mucosa. We are focusing the virus studies on the human herpesviruses and polyomaviruses, important pathogens known to establish latent infections in most of the human population. Both primary infection and reactivation from latent infection with these groups of viruses (especially certain herpesviruses) can cause a variety of illnesses that result in morbidity and, occasionally, mortality. Both herpesviruses and polyomaviruses have been associated with human cancer, as well. Effective vaccines exist for only one of the eight known human herpesviruses and available antivirals are of limited use. Whereas normal individuals display minimal consequences from latent viral infections, events which alter immune function (such as immunosuppressive therapy following solid organ transplantation) are known to increase the risk of complications as a result of viral reactivations.

  18. Dish-mounted latent heat buffer storage

    NASA Technical Reports Server (NTRS)

    Manvi, R.

    1981-01-01

    Dish-mounted latent heat storage subsystems for Rankine, Brayton, and Stirling engines operating at 427 C, 816 C, and 816 C respectively are discussed. Storage requirements definition, conceptual design, media stability and compatibility tests, and thermal performance analyses are considered.

  19. Generalized Structured Component Analysis with Latent Interactions

    ERIC Educational Resources Information Center

    Hwang, Heungsun; Ho, Moon-Ho Ringo; Lee, Jonathan

    2010-01-01

    Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA…

  20. CICATRIZATION OF WOUNDS : XI. LATENT PERIOD.

    PubMed

    Carrel, A; du Noüy, P L

    1921-09-30

    1. The latent period of cicatrization varies generally from 5 to 7 days. 2. It stops abruptly and contraction starts with its maximum velocity. 3. The formula of du Noüy applies to the beginning of the contraction period as well as to the subsequent periods.

  1. Detection of latent prints by Raman imaging

    DOEpatents

    Lewis, Linda Anne [Andersonville, TN; Connatser, Raynella Magdalene [Knoxville, TN; Lewis, Sr., Samuel Arthur

    2011-01-11

    The present invention relates to a method for detecting a print on a surface, the method comprising: (a) contacting the print with a Raman surface-enhancing agent to produce a Raman-enhanced print; and (b) detecting the Raman-enhanced print using a Raman spectroscopic method. The invention is particularly directed to the imaging of latent fingerprints.

  2. Forensic Chemistry: The Revelation of Latent Fingerprints

    ERIC Educational Resources Information Center

    Friesen, J. Brent

    2015-01-01

    The visualization of latent fingerprints often involves the use of a chemical substance that creates a contrast between the fingerprint residues and the surface on which the print was deposited. The chemical-aided visualization techniques can be divided into two main categories: those that chemically react with the fingerprint residue and those…

  3. Component Latent Trait Models for Test Design.

    ERIC Educational Resources Information Center

    Embretson, Susan Whitely

    Latent trait models are presented that can be used for test design in the context of a theory about the variables that underlie task performance. Examples of methods for decomposing and testing hypotheses about the theoretical variables in task performance are given. The methods can be used to determine the processing components that are involved…

  4. Thermally Stable, Latent Olefin Metathesis Catalysts

    PubMed Central

    Thomas, Renee M.; Fedorov, Alexey; Keitz, Benjamin K.

    2011-01-01

    Highly thermally stable N-aryl,N-alkyl N-heterocyclic carbene (NHC) ruthenium catalysts were designed and synthesized for latent olefin metathesis. These catalysts showed excellent latent behavior toward metathesis reactions, whereby the complexes were inactive at ambient temperature and initiated at elevated temperatures, a challenging property to achieve with second generation catalysts. A sterically hindered N-tert-butyl substituent on the NHC ligand of the ruthenium complex was found to induce latent behavior toward cross-metathesis reactions, and exchange of the chloride ligands for iodide ligands was necessary to attain latent behavior during ring-opening metathesis polymerization (ROMP). Iodide-based catalysts showed no reactivity toward ROMP of norbornene-derived monomers at 25 °C, and upon heating to 85 °C gave complete conversion of monomer to polymer in less than 2 hours. All of the complexes were very stable to air, moisture, and elevated temperatures up to at least 90 °C, and exhibited a long catalyst lifetime in solution at elevated temperatures. PMID:22282652

  5. Extended Generalized Linear Latent and Mixed Model

    ERIC Educational Resources Information Center

    Segawa, Eisuke; Emery, Sherry; Curry, Susan J.

    2008-01-01

    The generalized linear latent and mixed modeling (GLLAMM framework) includes many models such as hierarchical and structural equation models. However, GLLAMM cannot currently accommodate some models because it does not allow some parameters to be random. GLLAMM is extended to overcome the limitation by adding a submodel that specifies a…

  6. Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks.

    PubMed

    Yang, Huei-Fang; Lin, Kevin; Chen, Chu-Song

    2017-02-09

    This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.

  7. Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation.

    PubMed

    Kumar, M Pawan; Turki, Haithem; Preston, Dan; Koller, Daphne

    2015-07-01

    We consider the problem of parameter estimation and energy minimization for a region-based semantic segmentation model. The model divides the pixels of an image into non-overlapping connected regions, each of which is to a semantic class. In the context of energy minimization, the main problem we face is the large number of putative pixel-to-region assignments. We address this problem by designing an accurate linear programming based approach for selecting the best set of regions from a large dictionary. The dictionary is constructed by merging and intersecting segments obtained from multiple bottom-up over-segmentations. The linear program is solved efficiently using dual decomposition. In the context of parameter estimation, the main problem we face is the lack of fully supervised data. We address this issue by developing a principled framework for parameter estimation using diverse data. More precisely, we propose a latent structural support vector machine formulation, where the latent variables model any missing information in the human annotation. Of particular interest to us are three types of annotations: (i) images segmented using generic foreground or background classes; (ii) images with bounding boxes specified for objects; and (iii) images labeled to indicate the presence of a class. Using large, publicly available datasets we show that our methods are able to significantly improve the accuracy of the region-based model.

  8. Semantic Integrative Digital Pathology: Insights into Microsemiological Semantics and Image Analysis Scalability.

    PubMed

    Racoceanu, Daniel; Capron, Frédérique

    2016-01-01

    be devoted to morphological microsemiology (microscopic morphology semantics). Besides insuring the traceability of the results (second opinion) and supporting the orchestration of high-content image analysis modules, the role of semantics will be crucial for the correlation between digital pathology and noninvasive medical imaging modalities. In addition, semantics has an important role in modelling the links between traditional microscopy and recent label-free technologies. The massive amount of visual data is challenging and represents a characteristic intrinsic to digital pathology. The design of an operational integrative microscopy framework needs to focus on scalable multiscale imaging formalism. In this sense, we prospectively consider some of the most recent scalable methodologies adapted to digital pathology as marked point processes for nuclear atypia and point-set mathematical morphology for architecture grading. To orchestrate this scalable framework, semantics-based WSI management (analysis, exploration, indexing, retrieval and report generation support) represents an important means towards approaches to integrating big data into biomedicine. This insight reflects our vision through an instantiation of essential bricks of this type of architecture. The generic approach introduced here is applicable to a number of challenges related to molecular imaging, high-content image management and, more generally, bioinformatics.

  9. Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples

    PubMed Central

    Penev, Lyubomir; Agosti, Donat; Georgiev, Teodor; Catapano, Terry; Miller, Jeremy; Blagoderov, Vladimir; Roberts, David; Smith, Vincent S.; Brake, Irina; Ryrcroft, Simon; Scott, Ben; Johnson, Norman F.; Morris, Robert A.; Sautter, Guido; Chavan, Vishwas; Robertson, Tim; Remsen, David; Stoev, Pavel; Parr, Cynthia; Knapp, Sandra; Kress, W. John; Thompson, Chris F.; Erwin, Terry

    2010-01-01

    (PTP) to large international database services and indexers such as Global Biodiversity Information Facility (GBIF), National Center for Biotechnology Information (NCBI), Barcode of Life (BOLD), Encyclopedia of Life (EOL), ZooBank, Wikipedia, Wikispecies, Wikimedia, and others; (vi) GenBank accession numbers autotagged and linked to NCBI; (vii) external links of taxon names to references in PubMed, Google Scholar, Biodiversity Heritage Library and other sources. With the launching of the working example, ZooKeys becomes the first taxonomic journal to provide a complete XML-based editorial, publication and dissemination workflow implemented as a routine and cost-efficient practice. It is anticipated that XML-based workflow will also soon be implemented in botany through PhytoKeys, a forthcoming partner journal of ZooKeys. The semantic markup and enhancements are expected to greatly extend and accelerate the way taxonomic information is published, disseminated and used. PMID:21594113

  10. Latent mnemonic strengths are latent: a comment on Mickes, Wixted, and Wais (2007).

    PubMed

    Rouder, Jeffrey N; Pratte, Michael S; Morey, Richard D

    2010-06-01

    Mickes, Wixted, and Wais (2007) proposed a simple test of latent strength variability in recognition memory. They asked participants to rate their confidence using either a 20-point or a 99-point strength scale and plotted distributions of the resulting ratings. They found 25% more variability in ratings for studied than for new items, which they interpreted as providing evidence that latent mnemonic strength distributions are 25% more variable for studied than for new items. We show here that this conclusion is critically dependent on assumptions--so much so that these assumptions determine the conclusions. In fact, opposite conclusions, such that study does not affect the variability of latent strength, may be reached by making different but equally plausible assumptions. Because all measurements of mnemonic strength variability are critically dependent on untestable assumptions, all are arbitrary. Hence, there is no principled method for assessing the relative variability of latent mnemonic strength distributions.

  11. Variation in verbal fluency: a latent variable analysis of clustering, switching, and overall performance.

    PubMed

    Unsworth, Nash; Spillers, Gregory J; Brewer, Gene A

    2011-03-01

    Verbal fluency tasks have long been used to assess and estimate group and individual differences in executive functioning in both cognitive and neuropsychological research domains. Despite their ubiquity, however, the specific component processes important for success in these tasks have remained elusive. The current work sought to reveal these various components and their respective roles in determining performance in fluency tasks using latent variable analysis. Two types of verbal fluency (semantic and letter) were compared along with several cognitive constructs of interest (working memory capacity, inhibition, vocabulary size, and processing speed) in order to determine which constructs are necessary for performance in these tasks. The results are discussed within the context of a two-stage cyclical search process in which participants first search for higher order categories and then search for specific items within these categories.

  12. Reactions of latent prints exposed to blood.

    PubMed

    Praska, Nicole; Langenburg, Glenn

    2013-01-10

    We explored whether an undeveloped latent print (fingermark) exposed to blood and later developed by enhancement with blood reagents such as amido black (AB) or leucocrystal violet (LCV) could appear as a genuine blood mark. We examined three different experimental conditions. In Experiment I, fingermark residue only was tested, as a control to confirm that fingermark residue alone does not react with the blood reagents AB and LCV. Experiment II investigated whether latent fingermarks exposed to blood dilutions could be treated with AB or LCV and subsequently appear as a genuine blood mark enhanced with AB or LCV. Experiment III tested whether latent fingermarks exposed to whole blood could be processed with AB or LCV and subsequently appear as a genuine blood mark enhanced with AB or LCV. The present study found that indeed, fingermark residue alone does not react with the blood reagents AB and LCV. In Experiment II, an interaction occurred between the fingermark residue and the diluted blood that caused the ridges to appear a red color. In the present study, this interaction is called a faux blood mark. While the faux blood mark phenomenon occurred most often following exposure to diluted blood, it did not occur consistently, and a predictable pattern could not be established. However, the reaction occurred more frequently following extended fingermark residue drying times. Faux blood marks are distinguishable from genuine blood marks prior to enhancement with blood reagents. Following treatment with blood reagents, it became increasingly difficult to determine whether the enhanced mark was a genuine blood print or a latent fingermark exposed to diluted blood. Latent fingermarks exposed to whole blood often resulted in a void prior to enhancement, but following treatment with blood reagents, were difficult to distinguish from a genuine blood mark enhanced with blood reagents.

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

    PubMed

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

    2016-01-01

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

  14. A Latent Variable Approach to the Simple View of Reading

    ERIC Educational Resources Information Center

    Kershaw, Sarah; Schatschneider, Chris

    2012-01-01

    The present study utilized a latent variable modeling approach to examine the Simple View of Reading in a sample of students from 3rd, 7th, and 10th grades (N = 215, 188, and 180, respectively). Latent interaction modeling and other latent variable models were employed to investigate (a) the functional form of the relationship between decoding and…

  15. The latent cytomegalovirus decreases telomere length by microcompetition

    PubMed Central

    Javaherian, Adrian

    2015-01-01

    Reduced telomere length has been associated with aging and age-related diseases. Latent infection with the Cytomegalovirus (CMV) induces telomere shortening in the infected cells. Latent CMV infection may cause reduced telomere length via GABP transcription factor deficiency, according to the Microcompetition Theory. Microcompetition and viral-induced transcription factor deficiency is important since most people harbor a latent viral infection.

  16. Bayesian Semiparametric Structural Equation Models with Latent Variables

    ERIC Educational Resources Information Center

    Yang, Mingan; Dunson, David B.

    2010-01-01

    Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In…

  17. Skills Diagnosis Using IRT-Based Continuous Latent Trait Models

    ERIC Educational Resources Information Center

    Stout, William

    2007-01-01

    This article summarizes the continuous latent trait IRT approach to skills diagnosis as particularized by a representative variety of continuous latent trait models using item response functions (IRFs). First, several basic IRT-based continuous latent trait approaches are presented in some detail. Then a brief summary of estimation, model…

  18. Modeling Interaction Effects in Latent Growth Curve Models.

    ERIC Educational Resources Information Center

    Li, Fuzhong; Duncan, Terry E.; Acock, Alan

    2000-01-01

    Presents an extension of the method of estimating interaction effects among latent variables to latent growth curve models developed by K. Joreskog and F. Yang (1996). Illustrates the procedure and discusses results in terms of practical and statistical problems associated with interaction analyses in latent curve models and structural equation…

  19. Stochastic Approximation Methods for Latent Regression Item Response Models

    ERIC Educational Resources Information Center

    von Davier, Matthias; Sinharay, Sandip

    2010-01-01

    This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…

  20. Using Latent Class Analysis To Set Academic Performance Standards.

    ERIC Educational Resources Information Center

    Brown, Richard S.

    The use of latent class analysis for establishing student performance standards was studied. Latent class analysis (LCA) is an established procedure for investigating the latent structure of a set of data. LCA presumes that groups, classes, or respondents differ qualitatively from one another, and that these differences account for all of the…

  1. Fingerprint Minutiae from Latent and Matching Tenprint Images

    National Institute of Standards and Technology Data Gateway

    NIST Fingerprint Minutiae from Latent and Matching Tenprint Images (PC database for purchase)   NIST Special Database 27 contains latent fingerprints from crime scenes and their matching rolled fingerprint mates. This database can be used to develop and test new fingerprint algorithms, test commercial and research AFIS systems, train latent examiners, and promote the ANSI/NIST file format standard.

  2. A General Approach to Defining Latent Growth Components

    ERIC Educational Resources Information Center

    Mayer, Axel; Steyer, Rolf; Mueller, Horst

    2012-01-01

    We present a 3-step approach to defining latent growth components. In the first step, a measurement model with at least 2 indicators for each time point is formulated to identify measurement error variances and obtain latent variables that are purged from measurement error. In the second step, we use contrast matrices to define the latent growth…

  3. The Algebra of Lexical Semantics

    NASA Astrophysics Data System (ADS)

    Kornai, András

    The current generative theory of the lexicon relies primarily on tools from formal language theory and mathematical logic. Here we describe how a different formal apparatus, taken from algebra and automata theory, resolves many of the known problems with the generative lexicon. We develop a finite state theory of word meaning based on machines in the sense of Eilenberg [11], a formalism capable of describing discrepancies between syntactic type (lexical category) and semantic type (number of arguments). This mechanism is compared both to the standard linguistic approaches and to the formalisms developed in AI/KR.

  4. Speech Generation from Semantic Nets

    DTIC Science & Technology

    1975-09-01

    Speech Generation from Semantic Nets Page 2 (inPUt and output) is monitored bY a "discourse module" ( Deutsch , 1975) to maintain an accurate...in the Phrase, HEURISTIC RULES Hornby describes three basic positions for adverbs in the c1ause1 "front" position, "mid" position, and "end...34 position, Front position adverbs occur before the subject: •YesterdaY he went home, from there he took a taxi," The interrogative adverbs (e,g, how, when

  5. The Semantic Web and Educational Technology

    ERIC Educational Resources Information Center

    Maddux, Cleborne D., Ed.

    2008-01-01

    The "Semantic Web" is an idea proposed by Tim Berners-Lee, the inventor of the "World Wide Web." The topic has been generating a great deal of interest and enthusiasm, and there is a rapidly growing body of literature dealing with it. This article attempts to explain how the Semantic Web would work, and explores short-term and long-term…

  6. Is There a Critical Period for Semantics?

    ERIC Educational Resources Information Center

    Slabakova, Roumyana

    2006-01-01

    This article reviews recent research on the second language acquisition of meaning with a view of establishing whether there is a critical period for the acquisition of compositional semantics. It is claimed that the functional lexicon presents the most formidable challenge, while syntax and phrasal semantics pose less difficulty to learners.…

  7. Computation of Semantic Number from Morphological Information

    ERIC Educational Resources Information Center

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

    2005-01-01

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

  8. Learning the Semantics of Structured Data Sources

    ERIC Educational Resources Information Center

    Taheriyan, Mohsen

    2015-01-01

    Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data, however, they rarely provide a semantic model to describe their contents. Semantic models of data sources capture the intended meaning of data sources by mapping them to the concepts and relationships defined by a…

  9. Quantifying Semantic Linguistic Maturity in Children

    ERIC Educational Resources Information Center

    Hansson, Kristina; Bååth, Rasmus; Löhndorf, Simone; Sahlén, Birgitta; Sikström, Sverker

    2016-01-01

    We propose a method to quantify "semantic linguistic maturity" (SELMA) based on a high dimensional semantic representation of words created from the co-occurrence of words in a large text corpus. The method was applied to oral narratives from 108 children aged 4;0-12;10. By comparing the SELMA measure with maturity ratings made by human…

  10. Exploring the Relationship between Semantics and Space

    PubMed Central

    Turriziani, Patrizia; Oliveri, Massimiliano; Bonnì, Sonia; Koch, Giacomo; Smirni, Daniela; Cipolotti, Lisa

    2009-01-01

    The asymmetric distribution of human spatial attention has been repeatedly documented in both patients and healthy controls. Biases in the distribution of attention and/or in the mental representation of space may also affect some aspects of language processing. We investigated whether biases in attention and/or mental representation of space affect semantic representations. In particular, we investigated whether semantic judgments could be modulated by the location in space where the semantic information was presented and the role of the left and right parietal cortices in this task. Healthy subjects were presented with three pictures arranged horizontally (one middle and two outer pictures) of items belonging to the same semantic category. Subjects were asked to indicate the spatial position in which the semantic distance between the outer and middle pictures was smaller. Subjects systematically overestimated the semantic distance of items presented in the right side of space. We explored the neural correlates underpinning this bias using rTMS over the left and right parietal cortex. rTMS of the left parietal cortex selectively reduced this rightward bias. Our findings suggest the existence of an attentional and/or mental representational bias in semantic judgments, similar to that observed for the processing of space and numbers. Spatial manipulation of semantic material results in the activation of specialised attentional resources located in the left hemisphere. PMID:19396359

  11. Aspects of Semantic Theory and Reading Comprehension.

    ERIC Educational Resources Information Center

    Jeremiah, Milford A.

    This paper investigates the ways readers use two semantic tools, synonymy and entailment, when responding to reading-comprehension questions. After a brief overview of semantic theory, two reading passages and their attendant multiple-choice questions are analyzed, demonstrating how readers might choose the correct answer by analyzing the way it…

  12. Elaborative Retrieval: Do Semantic Mediators Improve Memory?

    ERIC Educational Resources Information Center

    Lehman, Melissa; Karpicke, Jeffrey D.

    2016-01-01

    The elaborative retrieval account of retrieval-based learning proposes that retrieval enhances retention because the retrieval process produces the generation of semantic mediators that link cues to target information. We tested 2 assumptions that form the basis of this account: that semantic mediators are more likely to be generated during…

  13. Semantique et psychologie (Semantics and Psychology)

    ERIC Educational Resources Information Center

    Le Ny, Jean-Francois

    1975-01-01

    Semantic activities constitute a sub-class of psychological activities; from this point of departure the article discusses such topics as: idiosyncrasies, meaning and causality, internal determinants, neo-associationism, componential theories, noun- and verb-formation, sentences and propositions, semantics and cognition, mnemesic compontents, and…

  14. Implicit Causality, Implicit Consequentiality and Semantic Roles

    ERIC Educational Resources Information Center

    Crinean, Marcelle; Garnham, Alan

    2006-01-01

    Stewart, Pickering, and Sanford (1998) reported a new type of semantic inference, implicit consequentiality, which they suggest is comparable to, although not directly related to, the well-documented phenomenon of implicit causality. It is our contention that there is a direct relation between these two semantic phenomena but that this relation…

  15. The Semantic Web in Teacher Education

    ERIC Educational Resources Information Center

    Czerkawski, Betül Özkan

    2014-01-01

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

  16. Analyticity and Features of Semantic Interaction.

    ERIC Educational Resources Information Center

    Steinberg, Danny D.

    The findings reported in this paper are the result of an experiment to determine the empirical validity of such semantic concepts as analytic, synthetic, and contradictory. Twenty-eight university students were presented with 156 sentences to assign to one of four semantic categories: (1) synthetic ("The dog is a poodle"), (2) analytic…

  17. 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…

  18. Research: General Semantics Training: Pride or Prejudice?

    ERIC Educational Resources Information Center

    Eckman, Bruce K.

    1978-01-01

    Argues that general semantics research into prejudice has made only minor contributions to an understanding of prejudice because of weak experimental designs. Suggests improvements in research methodology and urges that knowledge of the semantic world of minority groups be sought as a prerequisite to eliminating cultural bias in standardized…

  19. Implicit Learning of Semantic Preferences of Verbs

    ERIC Educational Resources Information Center

    Paciorek, Albertyna; Williams, John N.

    2015-01-01

    Previous studies of semantic implicit learning in language have only examined learning grammatical form-meaning connections in which learning could have been supported by prior linguistic knowledge. In this study we target the domain of verb meaning, specifically semantic preferences regarding novel verbs (e.g., the preference for a novel verb to…

  20. Semantics vs Pragmatics of a Compound Word

    ERIC Educational Resources Information Center

    Smirnova, Elena A.; Biktemirova, Ella I.; Davletbaeva, Diana N.

    2016-01-01

    This paper is devoted to the study of correlation between semantic and pragmatic potential of a compound word, which functions in informal speech, and the mechanisms of secondary nomination, which realizes the potential of semantic-pragmatic features of colloquial compounds. The relevance and the choice of the research question is based on the…

  1. Semantic and Phonemic Verbal Fluency in Blinds

    ERIC Educational Resources Information Center

    Nejati, Vahid; Asadi, Anoosh

    2010-01-01

    A person who has suffered the total loss of a sensory system has, indirectly, suffered a brain lesion. Semantic and phonologic verbal fluency are used for evaluation of executive function and language. The aim of this study is evaluation and comparison of phonemic and semantic verbal fluency in acquired blinds. We compare 137 blinds and 124…

  2. Orthographic and Semantic Processing in Young Readers

    ERIC Educational Resources Information Center

    Polse, Lara R.; Reilly, Judy S.

    2015-01-01

    This investigation examined orthographic and semantic processing during reading acquisition. Children in first to fourth grade were presented with a target word and two response alternatives, and were asked to identify the semantic match. Words were presented in four conditions: an exact match and unrelated foil (STONE-STONE-EARS), an exact match…

  3. Semantic Processing of Previews within Compound Words

    ERIC Educational Resources Information Center

    White, Sarah J.; Bertram, Raymond; Hyona, Jukka

    2008-01-01

    Previous studies have suggested that previews of words prior to fixation can be processed orthographically, but not semantically, during reading of sentences (K. Rayner, D. A. Balota, & A. Pollatsek, 1986). The present study tested whether semantic processing of previews can occur within words. The preview of the second constituent of…

  4. 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.

  5. SemanticOrganizer Brings Teams Together

    NASA Technical Reports Server (NTRS)

    Laufenberg, Lawrence

    2003-01-01

    SemanticOrganizer enables researchers in different locations to share, search for, and integrate data. Its customizable semantic links offer fast access to interrelated information. This knowledge management and information integration tool also supports real-time instrument data collection and collaborative image annotation.

  6. Semantic search integration to climate data

    SciTech Connect

    Devarakonda, Ranjeet; Palanisamy, Giri; Pouchard, Line Catherine; Shrestha, Biva

    2014-01-01

    In this paper we present how research projects at Oak Ridge National Laboratory are using Semantic Search capabilities to help scientists perform their research. We will discuss how the Mercury metadata search system, with the help of the semantic search capability, is being used to find, retrieve, and link climate change data. DOI: 10.1109/CTS.2014.6867639

  7. Should We Teach Semantic Prosody Awareness?

    ERIC Educational Resources Information Center

    McGee, Iain

    2012-01-01

    While considerable attention has been paid to collocation, and the development of the collocational competence of L2 learners in recent years, very little has been said about a related concept in teaching journals, namely semantic prosody, and L2 learner awareness of this phenomenon. In this paper the concept of semantic prosody is introduced, and…

  8. Phasic Affective Modulation of Semantic Priming

    ERIC Educational Resources Information Center

    Topolinski, Sascha; Deutsch, Roland

    2013-01-01

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

  9. 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…

  10. Approaching semantic interoperability in Health Level Seven

    PubMed Central

    Alschuler, Liora

    2010-01-01

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

  11. Differentiating Sense through Semantic Interaction Data

    PubMed Central

    Elizabeth Workman, T.; Weir, Charlene; Rindflesch, Thomas C.

    2016-01-01

    Words which have different representations but are semantically related, such as dementia and delirium, can pose difficult issues in understanding text. We explore the use of interaction frequency data between semantic elements as a means to differentiate concept pairs, using semantic predications extracted from the biomedical literature. We applied datasets of features drawn from semantic predications for semantically related pairs to two Expectation Maximization clustering processes (without, and with concept labels), then used all data to train and evaluate several concept classifying algorithms. For the unlabeled datasets, 80% displayed expected cluster count and similar or matching proportions; all labeled data exhibited similar or matching proportions when restricting cluster count to unique labels. The highest performing classifier achieved 89% accuracy, with F1 scores for individual concept classification ranging from 0.69 to 1. We conclude with a discussion on how these findings may be applied to natural language processing of clinical text. PMID:28269921

  12. SASL: A Semantic Annotation System for Literature

    NASA Astrophysics Data System (ADS)

    Yuan, Pingpeng; Wang, Guoyin; Zhang, Qin; Jin, Hai

    Due to ambiguity, search engines for scientific literatures may not return right search results. One efficient solution to the problems is to automatically annotate literatures and attach the semantic information to them. Generally, semantic annotation requires identifying entities before attaching semantic information to them. However, due to abbreviation and other reasons, it is very difficult to identify entities correctly. The paper presents a Semantic Annotation System for Literature (SASL), which utilizes Wikipedia as knowledge base to annotate literatures. SASL mainly attaches semantic to terminology, academic institutions, conferences, and journals etc. Many of them are usually abbreviations, which induces ambiguity. Here, SASL uses regular expressions to extract the mapping between full name of entities and their abbreviation. Since full names of several entities may map to a single abbreviation, SASL introduces Hidden Markov Model to implement name disambiguation. Finally, the paper presents the experimental results, which confirm SASL a good performance.

  13. Modeling Nonlinear Change via Latent Change and Latent Acceleration Frameworks: Examining Velocity and Acceleration of Growth Trajectories

    ERIC Educational Resources Information Center

    Grimm, Kevin; Zhang, Zhiyong; Hamagami, Fumiaki; Mazzocco, Michele

    2013-01-01

    We propose the use of the latent change and latent acceleration frameworks for modeling nonlinear growth in structural equation models. Moving to these frameworks allows for the direct identification of "rates of change" and "acceleration" in latent growth curves--information available indirectly through traditional growth…

  14. 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.

  15. Minding the PS, queues, and PXQs: Uniformity of semantic processing across multiple stimulus types

    PubMed Central

    Laszlo, Sarah; Federmeier, Kara D.

    2009-01-01

    An assumption in the reading literature is that access to semantics is gated by stimulus properties such as orthographic regularity or familiarity. In the electrophysiological domain, this assumption has led to a debate about the features necessary to initiate semantic processing as indexed by theN400 event-related potential (ERP) component. To examine this, we recorded ERPs to sentences with endings that were familiar and legal (words), familiar and illegal (acronyms), or unfamiliar and illegal (consonant or vowel strings). N400 congruency effects (reduced negativity to expected relative to unexpected endings) were observed for words and acronyms; these were identical in size, timing, and scalp distribution. Notably, clear N400 potentials were also elicited by unfamiliar, illegal strings, suggesting that, at least in a verbal context, semantic access may be attempted for any letter string, regardless of familiarity or regularity. PMID:18221447

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

    NASA Astrophysics Data System (ADS)

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

    2003-12-01

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

  17. Two Studies of Specification Error in Models for Categorical Latent Variables

    ERIC Educational Resources Information Center

    Kaplan, David; Depaoli, Sarah

    2011-01-01

    This article examines the problem of specification error in 2 models for categorical latent variables; the latent class model and the latent Markov model. Specification error in the latent class model focuses on the impact of incorrectly specifying the number of latent classes of the categorical latent variable on measures of model adequacy as…

  18. The algebraic theory of latent projectors in lambda matrices

    NASA Technical Reports Server (NTRS)

    Denman, E. D.; Leyva-Ramos, J.; Jeon, G. J.

    1981-01-01

    Multivariable systems such as a finite-element model of vibrating structures, control systems, and large-scale systems are often formulated in terms of differential equations which give rise to lambda matrices. The present investigation is concerned with the formulation of the algebraic theory of lambda matrices and the relationship of latent roots, latent vectors, and latent projectors to the eigenvalues, eigenvectors, and eigenprojectors of the companion form. The chain rule for latent projectors and eigenprojectors for the repeated latent root or eigenvalues is given.

  19. SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories.

    PubMed

    Al-Dohuki, Shamal; Wu, Yingyu; Kamw, Farah; Yang, Jing; Li, Xin; Zhao, Ye; Ye, Xinyue; Chen, Wei; Ma, Chao; Wang, Fei

    2017-01-01

    Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as "What were the taxi trips starting from Main Street and ending at Wall Street in the morning?" or "Where are the taxis arriving at the Art Museum at noon typically coming from?", tedious and time consuming interactions are usually needed since the numeric GPS points of trajectories are not directly linked to the keywords such as "Main Street", "Wall Street", and "Art Museum". In this paper, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. With SemanticTraj, domain and public users can find answers to the aforementioned questions easily through direct queries based on the terms. They can also interactively explore the retrieved data in visualizations enhanced by semantic information of the trajectories and trips. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps GPS points into a series of street/POI names and pick-up/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Semantic labels and meta-summaries of the results are integrated with a set of visualizations in a SemanticTraj prototype, which helps users study taxi trajectories quickly and easily. A set of usage scenarios are presented to show the usability of the system. We also collected feedback from domain experts and conducted a preliminary user study to evaluate the visual system.

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

    PubMed

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

    2014-06-01

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

  1. Demonstrating the Qualitative Differences between Semantic Aphasia and Semantic Dementia: A Novel Exploration of Nonverbal Semantic Processing

    PubMed Central

    Noonan, Krist A.; Garrard, Peter; Jefferies, Elizabeth; Eshan, Sheeba; Lambon Ralph, Matthew A.

    2013-01-01

    Semantic dementia (SD) implicates the anterior temporal lobes (ATL) as a critical substrate for semantic memory. Multi-modal semantic impairment can also be a feature of post-stroke aphasia (referred to here as “semantic aphasia” or SA) where patients show impaired regulatory control accompanied by lesions to the frontal and/or temporo-parietal cortices, and thus the two patient groups demonstrate qualitatively different patterns of semantic impairment [1]. Previous comparisons of these two patient groups have tended to focus on verbal receptive tasks. Accordingly, this study investigated nonverbal receptive abilities via a comparison of reality decision judgements in SD and SA. Pictures of objects were presented alongside non-real distracters whose features were altered to make them more/less plausible for the semantic category. The results highlighted a number of critical differences between the two groups. Compared to SD patients, SA patients: (1) were relatively unimpaired on the two alternative forced choice (2AFC) decisions despite showing a comparable degree of semantic impairment on other assessments; (2) showed minimal effects of the plausibility manipulation; (3) were strongly influenced by variations in the regulatory requirements of tasks; and (4) exhibited a reversed effect of familiarity–i.e., better performance on less commonly encountered items. These results support a distinction between semantic impairments which arise from impaired regulatory processes (e.g., SA) versus those where degraded semantic knowledge is the causal factor (e.g., SD). SA patients performed relatively well because the task structure reduced the requirement for internally generated control. In contrast, SD patients performed poorly because their degraded knowledge did not allow the fine-grained distinctions required to complete the task. PMID:22713375

  2. Semantic memory and verbal working memory correlates of N400 to subordinate homographs.

    PubMed

    Salisbury, Dean F

    2004-07-01

    N400 is an event-related brain potential that indexes operations in semantic memory conceptual space, whether elicited by language or some other representation (e.g., drawings). Language models typically propose three stages: lexical access or orthographic- and phonological-level analysis; lexical selection or word-level meaning and associate activation; and lexical integration, sentence- and discourse-level operations. The exact stage that N400 reflects is unknown, although opinion favors lexical integration over lexical selection. Surprisingly, little research has assessed relationships between neuropsychological measures of semantic memory fund of information or verbal working memory capacity and N400. Subjects performed a homograph disambiguation comprehension task with minimal working memory load. Short sentences read: The noun was adjective/verb. The nouns were either homographs or unambiguous. The adjective/verb was disambiguating for the homograph, and congruent or incongruent for the unambiguous noun. The primary noun of interest was the subordinate homograph. Comprehension of the subordinate meaning should correlate with semantic memory stores, reflecting greater knowledge. If N400 primarily reflects lexical access operations, it should also correlate with measures of semantic knowledge. If N400 reflects lexical integration, it should correlate with measures of working memory capacity. Comprehension errors were associated with semantic memory stores, but not working memory capacity. N400 was related to working memory capacity, but not semantic knowledge, suggesting that N400 primarily reflects late-stage working memory operations. N400 to subordinate disambiguating words was larger with greater working memory capacity, and thus may index the absolute capacity of working memory rather than difficulty in contextual integration.

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

    PubMed

    Wiese, Holger; Schweinberger, Stefan R

    2015-01-01

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

  4. Blocking of potentiation of latent inhibition.

    PubMed

    Hall, Geoffrey; Rodriguez, Gabriel

    2011-01-01

    We present a theory of latent inhibition based on the Pearce-Hall (Pearce & Hall, 1980) model for classical conditioning. Its central features are (1) that the associability of a stimulus declines as it comes to predict its consequences and (2) that nonreinforced exposure to a stimulus engages an associative learning process that makes the stimulus an accurate predictor of its consequences (in this case, the occurrence of no event). A formalization of this theory is shown to accommodate the finding that preexposure in compound with another cue can potentiate latent inhibition to the target cue. It further predicts that preexposure to the added cue will eliminate the potentiation effect. An experiment using rats and the flavor-aversion procedure confirmed this prediction.

  5. Improve Precategorized Collection Retrieval by Using Supervised Term Weighting Schemes

    DTIC Science & Technology

    2001-12-10

    based on local latent semantic indexing [5, 12], and techniques based on supervised concept indexing [6]. All these previous approaches only used a...on Knowledge and Data Eng., 9(5):718–730, September/October 1997. [5] D. Hull. Improving text retrieval for the routing problem using latent semantic indexing

  6. The latent class multitrait-multimethod model.

    PubMed

    Oberski, Daniel L; Hagenaars, Jacques A P; Saris, Willem E

    2015-12-01

    A latent class multitrait-multimethod (MTMM) model is proposed to estimate random and systematic measurement error in categorical survey questions while making fewer assumptions than have been made so far in such evaluations, allowing for possible extreme response behavior and other nonmonotone effects. The method is a combination of the MTMM research design of Campbell and Fiske (1959), the basic response model for survey questions of Saris and Andrews (1991), and the latent class factor model of Vermunt and Magidson (2004, pp. 227-230). The latent class MTMM model thus combines an existing design, model, and method to allow for the estimation of the degree to and manner in which survey questions are affected by systematic measurement error. Starting from a general form of the response function for a survey question, we present the MTMM experimental approach to identification of the response function's parameters. A "trait-method biplot" is introduced as a means of interpreting the estimates of systematic measurement error, whereas the quality of the questions can be evaluated by item information curves and the item information function. An experiment from the European Social Survey is analyzed and the results are discussed, yielding valuable insights into the functioning of a set of example questions on the role of women in society in 2 countries.

  7. Factors associated with latent fingerprint exclusion determinations.

    PubMed

    Ulery, Bradford T; Hicklin, R Austin; Roberts, Maria Antonia; Buscaglia, JoAnn

    2017-02-22

    Exclusion is the determination by a latent print examiner that two friction ridge impressions did not originate from the same source. The concept and terminology of exclusion vary among agencies. Much of the literature on latent print examination focuses on individualization, and much less attention has been paid to exclusion. This experimental study assesses the associations between a variety of factors and exclusion determinations. Although erroneous exclusions are more likely to occur on some images and for some examiners, they were widely distributed among images and examiners. Measurable factors found to be associated with exclusion rates include the quality of the latent, value determinations, analysis minutia count, comparison difficulty, and the presence of cores or deltas. An understanding of these associations will help explain the circumstances under which errors are more likely to occur and when determinations are less likely to be reproduced by other examiners; the results should also lead to improved effectiveness and efficiency of training and casework quality assurance. This research is intended to assist examiners in improving the examination process and provide information to the broader community regarding the accuracy, reliability, and implications of exclusion decisions.

  8. Semantic Categories and Context in L2 Vocabulary Learning

    ERIC Educational Resources Information Center

    Bolger, Patrick; Zapata, Gabriela

    2011-01-01

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

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

    ERIC Educational Resources Information Center

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

    2011-01-01

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

  10. Verb Production during Action Naming in Semantic Dementia

    ERIC Educational Resources Information Center

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

    2011-01-01

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

  11. Proof-Theoretic Semantics for a Natural Language Fragment

    NASA Astrophysics Data System (ADS)

    Francez, Nissim; Dyckhoff, Roy

    We propose a Proof - Theoretic Semantics (PTS) for a (positive) fragment E+0 of Natural Language (NL) (English in this case). The semantics is intended [7] to be incorporated into actual grammars, within the framework of Type - Logical Grammar (TLG) [12]. Thereby, this semantics constitutes an alternative to the traditional model - theoretic semantics (MTS), originating in Montague's seminal work [11], used in TLG.

  12. The Influence of Semantic Neighbours on Visual Word Recognition

    ERIC Educational Resources Information Center

    Yates, Mark

    2012-01-01

    Although it is assumed that semantics is a critical component of visual word recognition, there is still much that we do not understand. One recent way of studying semantic processing has been in terms of semantic neighbourhood (SN) density, and this research has shown that semantic neighbours facilitate lexical decisions. However, it is not clear…

  13. Uncovering the Architecture of Action Semantics

    PubMed Central

    Watson, Christine E.; Buxbaum, Laurel J.

    2014-01-01

    Despite research suggesting that stored sensorimotor information about tool use is a component of the semantic representations of tools, little is known about the action features or organizing principles that underlie this knowledge. We used methods similar to those applied in other semantic domains to examine the “architecture” of action semantic knowledge. In Experiment 1, participants sorted photographs of tools into groups according to the similarity of their associated “use” actions and rated tools on dimensions related to action. The results suggest that the magnitude of arm movement, configuration of the hand, and manner of motion during tool use play a role in determining how tools cluster in action “semantic space”. In Experiment 2, we validated the architecture uncovered in Experiment 1 using an implicit semantic task for which tool use knowledge was not ostensibly relevant (blocked cyclic word-picture matching). Using stimuli from Experiment 1, we found that participants performed more poorly during blocks of trials containing tools used with similar versus unrelated actions, and the amount of semantic interference depended on the magnitude of action similarity among tools. Thus, the degree of featural overlap between tool use actions plays a role in determining the overall semantic similarity of tools. PMID:25045905

  14. Peer-to-Peer Semantic Wikis

    NASA Astrophysics Data System (ADS)

    Skaf-Molli, Hala; Rahhal, Charbel; Molli, Pascal

    Wikis have demonstrated how it is possible to convert a community of strangers into a community of collaborators. Semantic wikis have opened an interesting way to mix web 2.0 advantages with the semantic web approach. P2P wikis have illustrated how wikis can be deployed on P2P wikis and take advantages of its intrinsic qualities: fault-tolerance, scalability and infrastructure cost sharing. In this paper, we present the first P2P semantic wiki that combines advantages of semantic wikis and P2P wikis. Building a P2P semantic wiki is challenging. It requires building an optimistic replication algorithm that is compatible with P2P constraints, ensures an acceptable level of consistency and generic enough to handle semantic wiki pages. The contribution of this paper is the definition of a clear model for building P2P semantic wikis. We define the data model, operations on this model, intentions of these operations, algorithms to ensure consistency and finally we implement the SWOOKI prototype based on these algorithms.

  15. Semantic classification of pictures and words.

    PubMed

    Taikh, Alex; Hargreaves, Ian S; Yap, Melvin J; Pexman, Penny M

    2015-01-01

    We provide new behavioural norms for semantic classification of pictures and words. The picture stimuli are 288 black and white line drawings from the International Picture Naming Project ([Székely, A., Jacobsen, T., D'Amico, S., Devescovi, A., Andonova, E., Herron, D., et al. (2004). A new on-line resource for psycholinguistic studies. Journal of Memory & Language, 51, 247-250]). We presented these pictures for classification in a living/nonliving decision, and in a separate version of the task presented the corresponding word labels for classification. We analyzed behavioural responses to a subset of the stimuli in order to explore questions about semantic processing. We found multiple semantic richness effects for both picture and word classification. Further, while lexical-level factors were related to semantic classification of words, they were not related to semantic classification of pictures. We argue that these results are consistent with privileged semantic access for pictures, and point to ways in which these data could be used to address other questions about picture processing and semantic memory.

  16. Semantically aided interpretation and querying of Jefferson Project data using the SemantEco framework

    NASA Astrophysics Data System (ADS)

    Patton, E. W.; Pinheiro, P.; McGuinness, D. L.

    2014-12-01

    We will describe the benefits we realized using semantic technologies to address the often challenging and resource intensive task of ontology alignment in service of data integration. Ontology alignment became relatively simple as we reused our existing semantic data integration framework, SemantEco. We work in the context of the Jefferson Project (JP), an effort to monitor and predict the health of Lake George in NY by deploying a large-scale sensor network in the lake, and analyzing the high-resolution sensor data. SemantEco is an open-source framework for building semantically-aware applications to assist users, particularly non-experts, in exploration and interpretation of integrated scientific data. SemantEco applications are composed of a set of modules that incorporate new datasets, extend the semantic capabilities of the system to integrate and reason about data, and provide facets for extending or controlling semantic queries. Whereas earlier SemantEco work focused on integration of water, air, and species data from government sources, we focus on redeploying it to provide a provenance-aware, semantic query and interpretation interface for JP's sensor data. By employing a minor alignment between SemantEco's ontology and the Human-Aware Sensor Network Ontology used to model the JP's sensor deployments, we were able to bring SemantEco's capabilities to bear on the JP sensor data and metadata. This alignment enabled SemantEco to perform the following tasks: (1) select JP datasets related to water quality; (2) understand how the JP's notion of water quality relates to water quality concepts in previous work; and (3) reuse existing SemantEco interactive data facets, e.g. maps and time series visualizations, and modules, e.g. the regulation module that interprets water quality data through the lens of various federal and state regulations. Semantic technologies, both as the engine driving SemantEco and the means of modeling the JP data, enabled us to rapidly

  17. 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

  18. 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

  19. 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?.

  20. 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

  1. Introduction to geospatial semantics and technology workshop handbook

    USGS Publications Warehouse

    Varanka, Dalia E.

    2012-01-01

    The workshop is a tutorial on introductory geospatial semantics with hands-on exercises using standard Web browsers. The workshop is divided into two sections, general semantics on the Web and specific examples of geospatial semantics using data from The National Map of the U.S. Geological Survey and the Open Ontology Repository. The general semantics section includes information and access to publicly available semantic archives. The specific session includes information on geospatial semantics with access to semantically enhanced data for hydrography, transportation, boundaries, and names. The Open Ontology Repository offers open-source ontologies for public use.

  2. A Statistical Approach for Text Processing in Virtual Humans

    DTIC Science & Technology

    2008-12-01

    Latent Semantic Indexing (PLSI) (Hofmann, 1999) and Latent Dirichlet Allocation (LDA) (Blei et al., 2003), where the authors model text collections by a...human architecture. In Proceed- ings of Language Resources and Evaluation Con- ference (LREC). Hofmann, T. 1999. Probabilistic latent semantic in

  3. On Automatic Support to Indexing a Life Sciences Data Base.

    ERIC Educational Resources Information Center

    Vleduts-Stokolov, N.

    1982-01-01

    Describes technique developed as automatic support to subject heading indexing at BIOSIS based on use of formalized language for semantic representation of biological texts and subject headings. Language structures, experimental results, and analysis of journal/subject heading and author/subject heading correlation data are discussed. References…

  4. Semantic processing and response inhibition.

    PubMed

    Chiang, Hsueh-Sheng; Motes, Michael A; Mudar, Raksha A; Rao, Neena K; Mansinghani, Sethesh; Brier, Matthew R; Maguire, Mandy J; Kraut, Michael A; Hart, John

    2013-11-13

    The present study examined functional MRI (fMRI) BOLD signal changes in response to object categorization during response selection and inhibition. Young adults (N=16) completed a Go/NoGo task with varying object categorization requirements while fMRI data were recorded. Response inhibition elicited increased signal change in various brain regions, including medial frontal areas, compared with response selection. BOLD signal in an area within the right angular gyrus was increased when higher-order categorization was mandated. In addition, signal change during response inhibition varied with categorization requirements in the left inferior temporal gyrus (lIT). lIT-mediated response inhibition when inhibiting the response only required lower-order categorization, but lIT mediated both response selection and inhibition when selecting and inhibiting the response required higher-order categorization. The findings characterized mechanisms mediating response inhibition associated with semantic object categorization in the 'what' visual object memory system.

  5. Semantics of color in chromatism

    NASA Astrophysics Data System (ADS)

    Serov, Nikolai V.

    2002-06-01

    The aim of this investigation is to describe the semantics of color in chromatism (from the ancient Greek triune notion of <>: (1) color as ideal (Id- plan), psychic; (2) tint as physical, verbal; material (M- plan), physiological, syntonic (S-plan), and (3) emotion as their informative-energetic correlation). Being a new field of science, chromatism links humanitarian and natural subjects by means of interdiscipline investigation of a real (f-m) man living in a real (color) surrounding environment. According to the definition for <>, color may be considered to be the most universal notion, permitting to assume the unity of both a man and an environment. Due to this assumption, we may give models of human intellect.

  6. A Semantic Analysis Method for Scientific and Engineering Code

    NASA Technical Reports Server (NTRS)

    Stewart, Mark E. M.

    1998-01-01

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

  7. 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.

  8. 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

  9. The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics.

    PubMed

    Hollis, Geoff; Westbury, Chris

    2016-12-01

    Notable progress has been made recently on computational models of semantics using vector representations for word meaning (Mikolov, Chen, Corrado, & Dean, 2013; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). As representations of meaning, recent models presumably hone in on plausible organizational principles for meaning. We performed an analysis on the organization of the skip-gram model's semantic space. Consistent with human performance (Osgood, Suci, & Tannenbaum, 1957), the skip-gram model primarily relies on affective distinctions to organize meaning. We showed that the skip-gram model accounts for unique variance in behavioral measures of lexical access above and beyond that accounted for by affective and lexical measures. We also raised the possibility that word frequency predicts behavioral measures of lexical access due to the fact that word use is organized by semantics. Deconstruction of the semantic representations in semantic models has the potential to reveal organizing principles of human semantics.

  10. Semantic Metrics for Analysis of Software

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  11. Semantic-Web Technology: Applications at NASA

    NASA Technical Reports Server (NTRS)

    Ashish, Naveen

    2004-01-01

    We provide a description of work at the National Aeronautics and Space Administration (NASA) on building system based on semantic-web concepts and technologies. NASA has been one of the early adopters of semantic-web technologies for practical applications. Indeed there are several ongoing 0 endeavors on building semantics based systems for use in diverse NASA domains ranging from collaborative scientific activity to accident and mishap investigation to enterprise search to scientific information gathering and integration to aviation safety decision support We provide a brief overview of many applications and ongoing work with the goal of informing the external community of these NASA endeavors.

  12. Attention trees and semantic paths

    NASA Astrophysics Data System (ADS)

    Giusti, Christian; Pieroni, Goffredo G.; Pieroni, Laura

    2007-02-01

    In the last few decades several techniques for image content extraction, often based on segmentation, have been proposed. It has been suggested that under the assumption of very general image content, segmentation becomes unstable and classification becomes unreliable. According to recent psychological theories, certain image regions attract the attention of human observers more than others and, generally, the image main meaning appears concentrated in those regions. Initially, regions attracting our attention are perceived as a whole and hypotheses on their content are formulated; successively the components of those regions are carefully analyzed and a more precise interpretation is reached. It is interesting to observe that an image decomposition process performed according to these psychological visual attention theories might present advantages with respect to a traditional segmentation approach. In this paper we propose an automatic procedure generating image decomposition based on the detection of visual attention regions. A new clustering algorithm taking advantage of the Delaunay- Voronoi diagrams for achieving the decomposition target is proposed. By applying that algorithm recursively, starting from the whole image, a transformation of the image into a tree of related meaningful regions is obtained (Attention Tree). Successively, a semantic interpretation of the leaf nodes is carried out by using a structure of Neural Networks (Neural Tree) assisted by a knowledge base (Ontology Net). Starting from leaf nodes, paths toward the root node across the Attention Tree are attempted. The task of the path consists in relating the semantics of each child-parent node pair and, consequently, in merging the corresponding image regions. The relationship detected in this way between two tree nodes generates, as a result, the extension of the interpreted image area through each step of the path. The construction of several Attention Trees has been performed and partial

  13. INDEXING MECHANISM

    DOEpatents

    Kock, L.J.

    1959-09-22

    A device is presented for loading and unloading fuel elements containing material fissionable by neutrons of thermal energy. The device comprises a combination of mechanical features Including a base, a lever pivotally attached to the base, an Indexing plate on the base parallel to the plane of lever rotation and having a plurality of apertures, the apertures being disposed In rows, each aperture having a keyway, an Index pin movably disposed to the plane of lever rotation and having a plurality of apertures, the apertures being disposed in rows, each aperture having a keyway, an index pin movably disposed on the lever normal to the plane rotation, a key on the pin, a sleeve on the lever spaced from and parallel to the index pin, a pair of pulleys and a cable disposed between them, an open collar rotatably attached to the sleeve and linked to one of the pulleys, a pin extending from the collar, and a bearing movably mounted in the sleeve and having at least two longitudinal grooves in the outside surface.

  14. On the roles of distinctiveness and semantic expectancies in episodic encoding of emotional words.

    PubMed

    Kamp, Siri-Maria; Potts, Geoffrey F; Donchin, Emanuel

    2015-12-01

    We examined the factors that contribute to enhanced recall for emotionally arousing words by analyzing behavioral performance, the P300 as an index of distinctiveness, and the N400 as an index of semantic expectancy violation in a modified Von Restorff paradigm. While their EEG was recorded, participants studied three list types (1) neutral words including one emotionally arousing isolate (either positive or negative), (2) arousing, negative words including one neutral isolate, or (3) arousing, positive words including one neutral isolate. Immediately after each list, free recall was tested. Negative, but not positive, words exhibited enhanced recall when presented as isolates in lists of neutral words and elicited a larger P300 for subsequently recalled than not-recalled words. This suggests that arousing, negative words stand out and that their distinctiveness contributes to their superior recall. Positive valence had an enhancing effect on recall only when the list contained mostly other positive words. Neutral isolates placed in either positive or negative lists elicited an N400, suggesting that semantic expectations developed in emotional word lists regardless of valence. However, semantic relatedness appeared to more strongly contribute to recall for positive than negative words. Our results suggest that distinctiveness and semantic relatedness contribute to episodic encoding of arousing words, but the impact of each factor depends on both a word's valence and its context.

  15. Brain systems mediating semantic and syntactic processing in deaf native signers: biological invariance and modality specificity.

    PubMed

    Capek, Cheryl M; Grossi, Giordana; Newman, Aaron J; McBurney, Susan L; Corina, David; Roeder, Brigitte; Neville, Helen J

    2009-05-26

    Studies of written and spoken language suggest that nonidentical brain networks support semantic and syntactic processing. Event-related brain potential (ERP) studies of spoken and written languages show that semantic anomalies elicit a posterior bilateral N400, whereas syntactic anomalies elicit a left anterior negativity, followed by a broadly distributed late positivity. The present study assessed whether these ERP indicators index the activity of language systems specific for the processing of aural-oral language or if they index neural systems underlying any natural language, including sign language. The syntax of a signed language is mediated through space. Thus the question arises of whether the comprehension of a signed language requires neural systems specific for this kind of code. Deaf native users of American Sign Language (ASL) were presented signed sentences that were either correct or that contained either a semantic or a syntactic error (1 of 2 types of verb agreement errors). ASL sentences were presented at the natural rate of signing, while the electroencephalogram was recorded. As predicted on the basis of earlier studies, an N400 was elicited by semantic violations. In addition, signed syntactic violations elicited an early frontal negativity and a later posterior positivity. Crucially, the distribution of the anterior negativity varied as a function of the type of syntactic violation, suggesting a unique involvement of spatial processing in signed syntax. Together, these findings suggest that biological constraints and experience shape the development of neural systems important for language.

  16. Brain systems mediating semantic and syntactic processing in deaf native signers: Biological invariance and modality specificity

    PubMed Central

    Capek, Cheryl M.; Grossi, Giordana; Newman, Aaron J.; McBurney, Susan L.; Corina, David; Roeder, Brigitte; Neville, Helen J.

    2009-01-01

    Studies of written and spoken language suggest that nonidentical brain networks support semantic and syntactic processing. Event-related brain potential (ERP) studies of spoken and written languages show that semantic anomalies elicit a posterior bilateral N400, whereas syntactic anomalies elicit a left anterior negativity, followed by a broadly distributed late positivity. The present study assessed whether these ERP indicators index the activity of language systems specific for the processing of aural-oral language or if they index neural systems underlying any natural language, including sign language. The syntax of a signed language is mediated through space. Thus the question arises of whether the comprehension of a signed language requires neural systems specific for this kind of code. Deaf native users of American Sign Language (ASL) were presented signed sentences that were either correct or that contained either a semantic or a syntactic error (1 of 2 types of verb agreement errors). ASL sentences were presented at the natural rate of signing, while the electroencephalogram was recorded. As predicted on the basis of earlier studies, an N400 was elicited by semantic violations. In addition, signed syntactic violations elicited an early frontal negativity and a later posterior positivity. Crucially, the distribution of the anterior negativity varied as a function of the type of syntactic violation, suggesting a unique involvement of spatial processing in signed syntax. Together, these findings suggest that biological constraints and experience shape the development of neural systems important for language. PMID:19433795

  17. Semantic Facilitation and Semantic Interference in Language Production: Further Evidence for the Conceptual Selection Model of Lexical Access

    ERIC Educational Resources Information Center

    Bloem, Ineke; van den Boogaard, Sylvia; Heij, Wido La

    2004-01-01

    Bloem and La Heij (2003) reported that in a word-translation task context words induce semantic interference whereas context pictures induce semantic facilitation. This finding was accounted for by a model of lexical access in which: (a) semantic facilitation is localized at the conceptual level, (b) semantic interference is localized at the…

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

    PubMed

    Kazic, Toni

    2006-01-01

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

  19. Latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint.

    PubMed

    Hu, Chuanpu; Szapary, Philippe O; Mendelsohn, Alan M; Zhou, Honghui

    2014-08-01

    Informative exposure-response modeling of clinical endpoints is important in drug development. There has been much recent progress in latent variable modeling of ordered categorical endpoints, including the application of indirect response (IDR) models and accounting for residual correlations between multiple categorical endpoints. This manuscript describes a framework of latent-variable-based IDR models that facilitate easy simultaneous modeling of a continuous and a categorical clinical endpoint. The model was applied to data from two phase III clinical trials of subcutaneously administered ustekinumab for the treatment of psoriatic arthritis, where Psoriasis Area and Severity Index scores and 20, 50, and 70 % improvement in the American College of Rheumatology response criteria were used as efficacy endpoints. Visual predictive check and external validation showed reasonable parameter estimation precision and model performance.

  20. The Relationship Between Anxiety Sensitivity and Latent Symptoms of Emotional Problems: A Structural Equation Modeling Approach

    PubMed Central

    Lewis, Alison R.; Zinbarg, Richard E.; Mineka, Susan; Craske, Michelle G.; Epstein, Alyssa; Griffith, James W.

    2011-01-01

    A large body of research suggests that common and specific psychopathology dimensions underlie the symptoms that occur within mood and anxiety disorders. As of yet, it is unclear precisely how the facets of Anxiety Sensitivity (AS), or fear of the symptoms of fear and anxiety, relate to these latent factors. Using data from 606 adolescents participating in the baseline phase of a longitudinal study on risk factors for emotional disorders, we modeled the facets of AS as measured by the Anxiety Sensitivity Index-Expanded Form (ASI-X) and related these facets to a hierarchical model of latent symptoms of psychological distress. Results suggest that one facet of AS is associated with a broad General Distress factor underlying symptoms of most emotional disorders while others relate to intermediate-level and conceptually-meaningful narrow factors representing aspects of psychological distress specific to particular emotional disorders. PMID:20510917

  1. Biological Indexing of Graft Transmissible Diseases of Citrus

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Biological indexing for the detection of graft transmissible diseases of citrus is essential for maintaining a citrus certification program. Many of the graft transmissible diseases of citrus are harbored as latent infections in the scions, but when propagated on a susceptible rootstock that allow...

  2. Ranunculus latent virus: a strain of artichoke latent virus or a new macluravirus infecting artichoke?

    PubMed

    Ciuffo, M; Testa, M; Lenzi, R; Turina, M

    2011-06-01

    An elongated virus was isolated from artichoke crops in Liguria, and a 700-bp fragment was amplified by RT-PCR using oligonucleotides to detect members of the family Potyviridae. Comparison of fragment sequences showed 98% identity at the nucleotide level with the ranunculus isolate of the macluravirus Ranunculus latent virus (RaLV). RaLV was then detected by DAS-ELISA in symptomatic and asymptomatic artichoke plants from Liguria, Sardinia and Latium. The sequence of a 5.5-kb region was assembled from a cDNA library, and a 500-bp NIa fragment showed 80% identity to Artichoke latent virus.

  3. A Collection of Features for Semantic Graphs

    SciTech Connect

    Eliassi-Rad, T; Fodor, I K; Gallagher, B

    2007-05-02

    Semantic graphs are commonly used to represent data from one or more data sources. Such graphs extend traditional graphs by imposing types on both nodes and links. This type information defines permissible links among specified nodes and can be represented as a graph commonly referred to as an ontology or schema graph. Figure 1 depicts an ontology graph for data from National Association of Securities Dealers. Each node type and link type may also have a list of attributes. To capture the increased complexity of semantic graphs, concepts derived for standard graphs have to be extended. This document explains briefly features commonly used to characterize graphs, and their extensions to semantic graphs. This document is divided into two sections. Section 2 contains the feature descriptions for static graphs. Section 3 extends the features for semantic graphs that vary over time.

  4. Semantic Considerations in the Treatment of Echolalia

    ERIC Educational Resources Information Center

    Garber, Norman B.; David, Leigh E.

    1975-01-01

    Two mentally retarded preschool children, both of whom echoed all forms of yes-no questions, were subjected to different treatment procedures varied according to the semantic function of the antecedent verbal stimulus. (Author)

  5. Semantic Antinomies and Deep Structure Analysis

    ERIC Educational Resources Information Center

    Zuber, Ryszard

    1975-01-01

    This article discusses constructions known as semantic antinomies, that is, the paradoxical results of false presuppositions, and how they can be dealt with by means of deep structure analysis. See FL 508 186 for availability. (CLK)

  6. Static Semantics and Compiler Error Recovery

    DTIC Science & Technology

    1985-06-01

    for Semantics-directed Error Recovery .......... 24 5.1 Attribute Grammars ...25 5.2 LL- and LR-attributed Grammars .............................................................. 25 5.3 A Practical Organization that...96 Appendix A: The Grammar for the Pascal Auditor

  7. A Prolegomenon to Situation Semantics.

    DTIC Science & Technology

    1983-07-01

    but by how they describe things to be... Frege adopted a third strategy. He postulated a third realm, a realm neither of ideas nor of worldly events...enough, indexicality gave nightmares to both Frege and Russell. 12 It might seem that the issue of indexicality did not escape Montague’s attention; and...24 The conventional wisdom, from Frege through to its logical culmination in Montague, has been that propositional attitude constructions are

  8. Project Integration Architecture: Formulation of Semantic Parameters

    NASA Technical Reports Server (NTRS)

    Jones, William Henry

    2005-01-01

    One of several key elements of the Project Integration Architecture (PIA) is the intention to formulate parameter objects which convey meaningful semantic information. In so doing, it is expected that a level of automation can be achieved in the consumption of information content by PIA-consuming clients outside the programmatic boundary of a presenting PIA-wrapped application. This paper discusses the steps that have been recently taken in formulating such semantically-meaningful parameters.

  9. Conceptual graphs for semantics and knowledge processing

    SciTech Connect

    Fargues, J.; Landau, M.C.; Dugourd, A.; Catach, L.

    1986-01-01

    This paper discusses the representational and algorithmic power of the conceptual graph model for natural language semantics and knowledge processing. Also described is a Prolog-like resolution method for conceptual graphs, which allows to perform deduction on very large semantic domains. The interpreter developed is similar to a Prolog interpreter in which the terms are any conceptual graphs and in which the unification algorithm is replaced by a specialized algorithm for conceptual graphs.

  10. Evaluating word semantic properties using Sketch Engine

    NASA Astrophysics Data System (ADS)

    Stoykova, Velislava; Simkova, Maria

    2015-02-01

    The paper describes approach to use statistically-based tools incorporated into Sketch Engine system for electronic text corpora processing to mining big textual data for search and extract word semantic properties. It presents and compares series of word search experiments using different statistical approaches and evaluates results for Bulgarian language EUROPARL 7 Corpus search to extract word semantic properties. Finally, the methodology is extended for multilingual application using Slovak language EUROPARL 7 Corpus.

  11. Ontology Reuse in Geoscience Semantic Applications

    NASA Astrophysics Data System (ADS)

    Mayernik, M. S.; Gross, M. B.; Daniels, M. D.; Rowan, L. R.; Stott, D.; Maull, K. E.; Khan, H.; Corson-Rikert, J.

    2015-12-01

    The tension between local ontology development and wider ontology connections is fundamental to the Semantic web. It is often unclear, however, what the key decision points should be for new semantic web applications in deciding when to reuse existing ontologies and when to develop original ontologies. In addition, with the growth of semantic web ontologies and applications, new semantic web applications can struggle to efficiently and effectively identify and select ontologies to reuse. This presentation will describe the ontology comparison, selection, and consolidation effort within the EarthCollab project. UCAR, Cornell University, and UNAVCO are collaborating on the EarthCollab project to use semantic web technologies to enable the discovery of the research output from a diverse array of projects. The EarthCollab project is using the VIVO Semantic web software suite to increase discoverability of research information and data related to the following two geoscience-based communities: (1) the Bering Sea Project, an interdisciplinary field program whose data archive is hosted by NCAR's Earth Observing Laboratory (EOL), and (2) diverse research projects informed by geodesy through the UNAVCO geodetic facility and consortium. This presentation will outline of EarthCollab use cases, and provide an overview of key ontologies being used, including the VIVO-Integrated Semantic Framework (VIVO-ISF), Global Change Information System (GCIS), and Data Catalog (DCAT) ontologies. We will discuss issues related to bringing these ontologies together to provide a robust ontological structure to support the EarthCollab use cases. It is rare that a single pre-existing ontology meets all of a new application's needs. New projects need to stitch ontologies together in ways that fit into the broader semantic web ecosystem.

  12. Lexical Semantic Techniques for Corpus Analysis

    DTIC Science & Technology

    1993-06-01

    work suggests how linguistic phenomena such as metonymy and polysemy might be exploitable for semantic tagging of lexical items. Unlike with purely...discover what kinds of knowledge can be reliably acquired through the use of these methods, exploiting, as they do, general linguis- tic knowledge...language. These devices and the associated dictionary make up a generative lexicon, where semantic information is distributed throughout the lexicon to

  13. 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.

  14. Latent Viruses: A Space Travel Hazard??

    NASA Technical Reports Server (NTRS)

    Ling, P. D.; Peng, R. S.; Pierson, D.; Lednicky, J.; Butel, J. S.

    1999-01-01

    A major issue associated with long-duration space flight is the possibility of infectious disease causing an unacceptable medical risk to crew members. Our proposal is designed to gain information that addresses several issues outlined in the Immunology/Infectious disease critical path. The major hypothesis addressed is that space flight causes alterations in the immune system that may allow latent viruses which are endogenous in the human population to reactivate and shed to higher levels than normal which can affect the health of crew members during a long term space-flight mission. We will initially focus our studies on the human herpesviruses and human polyomaviruses which are important pathogens known to establish latent infections in the human population. Both primary infection and reactivation from latent infection with this group of viruses can cause a variety of illnesses that result in morbidity and occasionally mortality of infected individuals. Effective vaccines exist for only one of the eight known human herpesviruses and the vaccine itself can still reactivate from latent infection. Available antivirals are of limited use and are effective against only a few of the human herpesviruses. Although most individuals display little if any clinical consequences from latent infection, events which alter immune function such as immunosuppressive therapy following solid organ transplantation are known to increase the risk of developing complications as a result of latent virus reactivation. This proposal will measure both the frequency and magnitude of viral shedding and genome loads in the blood from humans participating in activities that serve as ground based models of space flight conditions. Our initial goal is to develop sensitive quantitative competitive PCR- based assays (QC-PCR) to detect the herpesvirus Epstein-Barr virus (EBV), and the polyomaviruses SV40, BKV, and JCV. Using these assays we will establish baseline patterns of viral genome load in

  15. Targeting the latent reservoir to achieve functional HIV cure

    PubMed Central

    Cary, Daniele C.; Peterlin, B. Matija

    2016-01-01

    While highly active anti-retroviral therapy has greatly improved the lives of HIV-infected individuals, current treatments are unable to completely eradicate the virus. This is due to the presence of HIV latently infected cells which harbor transcriptionally silent HIV. Latent HIV does not replicate or produce viral proteins, thereby preventing efficient targeting by anti-retroviral drugs. Strategies to target the HIV latent reservoir include viral reactivation, enhancing host defense mechanisms, keeping latent HIV silent, and using gene therapy techniques to knock out or reactivate latent HIV. While research into each of these areas has yielded promising results, currently no one mechanism eradicates latent HIV. Instead, combinations of these approaches should be considered for a potential HIV functional cure. PMID:27303638

  16. 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.

  17. Semantics by analogy for illustrative volume visualization.

    PubMed

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

    2012-05-01

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

  18. Usage of semantic representations in recognition memory.

    PubMed

    Nishiyama, Ryoji; Hirano, Tetsuji; Ukita, Jun

    2017-04-11

    Meanings of words facilitate false acceptance as well as correct rejection of lures in recognition memory tests, depending on the experimental context. This suggests that semantic representations are both directly and indirectly (i.e., mediated by perceptual representations) used in remembering. Studies using memory conjunction errors (MCEs) paradigms, in which the lures consist of component parts of studied words, have reported semantic facilitation of rejection of the lures. However, attending to components of the lures could potentially cause this. Therefore, we investigated whether semantic overlap of lures facilitates MCEs using Japanese Kanji words in which a whole-word image is more concerned in reading. Experiments demonstrated semantic facilitation of MCEs in a delayed recognition test (Experiment 1), and in immediate recognition tests in which participants were prevented from using phonological or orthographic representations (Experiment 2), and the salient effect on individuals with high semantic memory capacities (Experiment 3). Additionally, analysis of the receiver operating characteristic suggested that this effect is attributed to familiarity-based memory judgement and phantom recollection. These findings indicate that semantic representations can be directly used in remembering, even when perceptual representations of studied words are available.

  19. Semantics by analogy for illustrative volume visualization☆

    PubMed Central

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

    2012-01-01

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

  20. Vowelling and semantic priming effects in Arabic.

    PubMed

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

    2015-01-01

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

  1. Semantic framework for mapping object-oriented model to semantic web languages

    PubMed Central

    Ježek, Petr; Mouček, Roman

    2015-01-01

    The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework. PMID:25762923

  2. Semantic framework for mapping object-oriented model to semantic web languages.

    PubMed

    Ježek, Petr; Mouček, Roman

    2015-01-01

    The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework.

  3. 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

  4. Associating semantic grammars with the SNOMED: processing medical language and representing clinical facts into a language-independent frame.

    PubMed

    Do Amaral Marcio, B; Satomura, Y

    1995-01-01

    We describe one approach for natural language processing of medical texts that associates a semantic grammar with the SNOMED (Systematized Nomenclature of Medicine). Our research hypothesis is that the combination of the nomenclature's declarative knowledge with a formal grammar would create a scientific sublanguage embedded with medical knowledge that could be used for analyzing and formatting medical texts. This combination permitted the abstraction of templates we call "semantic patterns." These patterns represent both linguistic and medical knowledge, packed into a hybrid information format. We analyzed manually case reports described in the New England Journal of Medicine (NEJM) from 1985 to 1988 and extracted empirically a semantic grammar. Over 2,000 sentences were analyzed. About 160 structural semantic patterns were abstracted and included in the database of one parser. We tested the parser using reports from 1989 to 1990. Results show that this approach is efficient for processing, indexing, and structuring diverse parts of case reports narrative. The analyzed medical sentences are structured into a language-independent semantic frame format. We conclude that the association of semantic grammars with the SNOMED enabled the construction of a formal system for analysis and representation of clinical facts. The transformation of the structured information from its frame format into other representational schemes, like conceptual graphs, is straightforward. Another application includes the use of the formatted language-independent frame for telegraphic English-Japanese translations of medical sentences.

  5. Dimensionality of the Latent Structure and Item Selection via Latent Class Multidimensional IRT Models

    ERIC Educational Resources Information Center

    Bartolucci, F.; Montanari, G. E.; Pandolfi, S.

    2012-01-01

    With reference to a questionnaire aimed at assessing the performance of Italian nursing homes on the basis of the health conditions of their patients, we investigate two relevant issues: dimensionality of the latent structure and discriminating power of the items composing the questionnaire. The approach is based on a multidimensional item…

  6. Bayesian Analysis of Multivariate Latent Curve Models with Nonlinear Longitudinal Latent Effects

    ERIC Educational Resources Information Center

    Song, Xin-Yuan; Lee, Sik-Yum; Hser, Yih-Ing

    2009-01-01

    In longitudinal studies, investigators often measure multiple variables at multiple time points and are interested in investigating individual differences in patterns of change on those variables. Furthermore, in behavioral, social, psychological, and medical research, investigators often deal with latent variables that cannot be observed directly…

  7. Determination of the Latent Heats and Triple Point of Perfluorocyclobutane

    ERIC Educational Resources Information Center

    Briggs, A. G.; Strachan, A. N.

    1977-01-01

    Proposes the use of Perfluorocyclobutane in physical chemistry courses to conduct experiments on latent heat, triple point temperatures and pressures, boiling points, and entropy of vaporization. (SL)

  8. Semantic retrieval and navigation in clinical document collections.

    PubMed

    Kreuzthaler, Markus; Daumke, Philipp; Schulz, Stefan

    2015-01-01

    Patients with chronic diseases undergo numerous in- and outpatient treatment periods, and therefore many documents accumulate in their electronic records. We report on an on-going project focussing on the semantic enrichment of medical texts, in order to support recall-oriented navigation across a patient's complete documentation. A document pool of 1,696 de-identified discharge summaries was used for prototyping. A natural language processing toolset for document annotation (based on the text-mining framework UIMA) and indexing (Solr) was used to support a browser-based platform for document import, search and navigation. The integrated search engine combines free text and concept-based querying, supported by dynamically generated facets (diagnoses, procedures, medications, lab values, and body parts). The prototype demonstrates the feasibility of semantic document enrichment within document collections of a single patient. Originally conceived as an add-on for the clinical workplace, this technology could also be adapted to support personalised health record platforms, as well as cross-patient search for cohort building and other secondary use scenarios.

  9. Wernicke's Aphasia Reflects a Combination of Acoustic-Phonological and Semantic Control Deficits: A Case-Series Comparison of Wernicke's Aphasia, Semantic Dementia and Semantic Aphasia

    ERIC Educational Resources Information Center

    Robson, Holly; Sage, Karen; Lambon Ralph, Matthew A.

    2012-01-01

    Wernicke's aphasia (WA) is the classical neurological model of comprehension impairment and, as a result, the posterior temporal lobe is assumed to be critical to semantic cognition. This conclusion is potentially confused by (a) the existence of patient groups with semantic impairment following damage to other brain regions (semantic dementia and…

  10. Semantic Preview Benefit in English: Individual Differences in the Extraction and Use of Parafoveal Semantic Information

    ERIC Educational Resources Information Center

    Veldre, Aaron; Andrews, Sally

    2016-01-01

    Although there is robust evidence that skilled readers of English extract and use orthographic and phonological information from the parafovea to facilitate word identification, semantic preview benefits have been elusive. We sought to establish whether individual differences in the extraction and/or use of parafoveal semantic information could…

  11. Sub-Lexical Phonological and Semantic Processing of Semantic Radicals: A Primed Naming Study

    ERIC Educational Resources Information Center

    Zhou, Lin; Peng, Gang; Zheng, Hong-Ying; Su, I-Fan; Wang, William S.-Y.

    2013-01-01

    Most sinograms (i.e., Chinese characters) are phonograms (phonetic compounds). A phonogram is composed of a semantic radical and a phonetic radical, with the former usually implying the meaning of the phonogram, and the latter providing cues to its pronunciation. This study focused on the sub-lexical processing of semantic radicals which are…

  12. List Context Fosters Semantic Processing: Parallels between Semantic and Morphological Facilitation when Primes Are Forward Masked

    ERIC Educational Resources Information Center

    Feldman, Laurie Beth; Basnight-Brown, Dana M.

    2008-01-01

    The authors examined patterns of facilitation under forward-masked priming conditions across 3 list contexts (Experiments 1-3) that varied with respect to properties of filler trials--(a) mixed (morphological, orthographic, semantic), (b) identity, and (c) semantic--but held the relatedness proportion constant (75%). Facilitation for targets that…

  13. Ambiguity and Relatedness Effects in Semantic Tasks: Are They Due to Semantic Coding?

    ERIC Educational Resources Information Center

    Hino, Yasushi; Pexman, Penny M.; Lupker, Stephen J.

    2006-01-01

    According to parallel distributed processing (PDP) models of visual word recognition, the speed of semantic coding is modulated by the nature of the orthographic-to-semantic mappings. Consistent with this idea, an ambiguity disadvantage and a relatedness-of-meaning (ROM) advantage have been reported in some word recognition tasks in which semantic…

  14. Is more always better? Effects of semantic richness on lexical decision, speeded pronunciation, and semantic classification.

    PubMed

    Yap, Melvin J; Tan, Sarah E; Pexman, Penny M; Hargreaves, Ian S

    2011-08-01

    Evidence from large-scale studies (Pexman, Hargreaves, Siakaluk, Bodner, & Pope, 2008) suggests that semantic richness, a multidimensional construct reflecting the extent of variability in the information associated with a word's meaning, facilitates visual word recognition. Specifically, recognition is better for words that (1) have more semantic neighbors, (2) possess referents with more features, and (3) are associated with more contexts. The present study extends Pexman et al. (2008) by examining how two additional measures of semantic richness, number of senses and number of associates (Pexman, Hargreaves, Edwards, Henry, & Goodyear, 2007), influence lexical decision, speeded pronunciation, and semantic classification performance, after controlling for an array of lexical and semantic variables. We found that number of features and contexts consistently facilitated word recognition but that the effects of semantic neighborhood density and number of associates were less robust. Words with more senses also elicited faster lexical decisions but less accurate semantic classifications. These findings point to how the effects of different semantic dimensions are selectively and adaptively modulated by task-specific demands.

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

    ERIC Educational Resources Information Center

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

    2006-01-01

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

  16. On the Existence of Semantic Working Memory: Evidence for Direct Semantic Maintenance

    ERIC Educational Resources Information Center

    Shivde, Geeta; Anderson, Michael C.

    2011-01-01

    Despite widespread acknowledgment of the importance of online semantic maintenance, there has been astonishingly little work that clearly establishes this construct. We review the extant work relevant to short-term retention of meaning and show that, although consistent with semantic working memory, most data can be accommodated in other ways.…

  17. Semantic Signature: Comparative Interpretation of Gene Expression on a Semantic Space.

    PubMed

    Kim, Jihun; Kim, Keewon; Kim, Ju Han

    2016-01-01

    Background. Interpretation of microarray data remains challenging because biological meaning should be extracted from enormous numeric matrices and be presented explicitly. Moreover, huge public repositories of microarray dataset are ready to be exploited for comparative analysis. This study aimed to provide a platform where essential implication of a microarray experiment could be visually expressed and various microarray datasets could be intuitively compared. Results. On the semantic space, gene sets from Molecular Signature Database (MSigDB) were plotted as landmarks and their relative distances were calculated by Lin's semantic similarity measure. By formal concept analysis, a microarray dataset was transformed into a concept lattice with gene clusters as objects and Gene Ontology terms as attributes. Concepts of a lattice were located on the semantic space reflecting semantic distance from landmarks and edges between concepts were drawn; consequently, a specific geographic pattern could be observed from a microarray dataset. We termed a distinctive geography shared by microarray datasets of the same category as "semantic signature." Conclusions. "Semantic space," a map of biological entities, could serve as a universal platform for comparative microarray analysis. When microarray data were displayed on the semantic space as concept lattices, "semantic signature," characteristic geography for a microarray experiment, could be discovered.

  18. Explaining Semantic Short-Term Memory Deficits: Evidence for the Critical Role of Semantic Control

    ERIC Educational Resources Information Center

    Hoffman, Paul; Jefferies, Elizabeth; Lambon Ralph, Matthew A.

    2011-01-01

    Patients with apparently selective short-term memory (STM) deficits for semantic information have played an important role in developing multi-store theories of STM and challenge the idea that verbal STM is supported by maintaining activation in the language system. We propose that semantic STM deficits are not as selective as previously thought…

  19. Revisiting intracellular calcium signaling semantics.

    PubMed

    Haiech, Jacques; Audran, Emilie; Fève, Marie; Ranjeva, Raoul; Kilhoffer, Marie-Claude

    2011-12-01

    Cells use intracellular free calcium concentration changes for signaling. Signal encoding occurs through both spatial and temporal modulation of the free calcium concentration. The encoded message is detected by an ensemble of intracellular sensors forming the family of calcium-binding proteins (CaBPs) which must faithfully translate the message using a new syntax that is recognized by the cell. The cell is home to a significant although limited number of genes coding for proteins involved in the signal encoding and decoding processes. In a cell, only a subset of this ensemble of genes is expressed, leading to a genetic regulation of the calcium signal pathways. Calmodulin (CaM), the most ubiquitous expressed intracellular calcium-binding protein, plays a major role in calcium signal translation. Similar to a hub, it is central to a large and finely tuned network, receiving information, integrating it and dispatching the cognate response. In this review, we examine the different steps starting with an external stimulus up to a cellular response, with special emphasis on CaM and the mechanism by which it decodes calcium signals and translates it into exquisitely coordinated cellular events. By this means, we will revisit the calcium signaling semantics, hoping that we will ease communication between scientists dealing with calcium signals in different biological systems and different domains.

  20. The ongoing challenge of latent tuberculosis

    PubMed Central

    Esmail, H.; Barry, C. E.; Young, D. B.; Wilkinson, R. J.

    2014-01-01

    The global health community has set itself the task of eliminating tuberculosis (TB) as a public health problem by 2050. Although progress has been made in global TB control, the current decline in incidence of 2% yr−1 is far from the rate needed to achieve this. If we are to succeed in this endeavour, new strategies to reduce the reservoir of latently infected persons (from which new cases arise) would be advantageous. However, ascertainment of the extent and risk posed by this group is poor. The current diagnostics tests (tuberculin skin test and interferon-gamma release assays) poorly predict who will develop active disease and the therapeutic options available are not optimal for the scale of the intervention that may be required. In this article, we outline a basis for our current understanding of latent TB and highlight areas where innovation leading to development of novel diagnostic tests, drug regimens and vaccines may assist progress. We argue that the pool of individuals at high risk of progression may be significantly smaller than the 2.33 billion thought to be immune sensitized by Mycobacterium tuberculosis and that identifying and targeting this group will be an important strategy in the road to elimination. PMID:24821923

  1. Latent TGF-[beta] structure and activation

    SciTech Connect

    Shi, Minlong; Zhu, Jianghai; Wang, Rui; Chen, Xing; Mi, Lizhi; Walz, Thomas; Springer, Timothy A.

    2011-09-16

    Transforming growth factor (TGF)-{beta} is stored in the extracellular matrix as a latent complex with its prodomain. Activation of TGF-{beta}1 requires the binding of {alpha}v integrin to an RGD sequence in the prodomain and exertion of force on this domain, which is held in the extracellular matrix by latent TGF-{beta} binding proteins. Crystals of dimeric porcine proTGF-{beta}1 reveal a ring-shaped complex, a novel fold for the prodomain, and show how the prodomain shields the growth factor from recognition by receptors and alters its conformation. Complex formation between {alpha}v{beta}6 integrin and the prodomain is insufficient for TGF-{beta}1 release. Force-dependent activation requires unfastening of a 'straitjacket' that encircles each growth-factor monomer at a position that can be locked by a disulphide bond. Sequences of all 33 TGF-{beta} family members indicate a similar prodomain fold. The structure provides insights into the regulation of a family of growth and differentiation factors of fundamental importance in morphogenesis and homeostasis.

  2. Nonparametric Bayes Stochastically Ordered Latent Class Models

    PubMed Central

    Yang, Hongxia; O’Brien, Sean; Dunson, David B.

    2012-01-01

    Latent class models (LCMs) are used increasingly for addressing a broad variety of problems, including sparse modeling of multivariate and longitudinal data, model-based clustering, and flexible inferences on predictor effects. Typical frequentist LCMs require estimation of a single finite number of classes, which does not increase with the sample size, and have a well-known sensitivity to parametric assumptions on the distributions within a class. Bayesian nonparametric methods have been developed to allow an infinite number of classes in the general population, with the number represented in a sample increasing with sample size. In this article, we propose a new nonparametric Bayes model that allows predictors to flexibly impact the allocation to latent classes, while limiting sensitivity to parametric assumptions by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. An efficient MCMC algorithm is developed for posterior computation. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity. PMID:22505787

  3. A Framework for Reproducible Latent Fingerprint Enhancements.

    PubMed

    Carasso, Alfred S

    2014-01-01

    Photoshop processing of latent fingerprints is the preferred methodology among law enforcement forensic experts, but that appproach is not fully reproducible and may lead to questionable enhancements. Alternative, independent, fully reproducible enhancements, using IDL Histogram Equalization and IDL Adaptive Histogram Equalization, can produce better-defined ridge structures, along with considerable background information. Applying a systematic slow motion smoothing procedure to such IDL enhancements, based on the rapid FFT solution of a Lévy stable fractional diffusion equation, can attenuate background detail while preserving ridge information. The resulting smoothed latent print enhancements are comparable to, but distinct from, forensic Photoshop images suitable for input into automated fingerprint identification systems, (AFIS). In addition, this progressive smoothing procedure can be reexamined by displaying the suite of progressively smoother IDL images. That suite can be stored, providing an audit trail that allows monitoring for possible loss of useful information, in transit to the user-selected optimal image. Such independent and fully reproducible enhancements provide a valuable frame of reference that may be helpful in informing, complementing, and possibly validating the forensic Photoshop methodology.

  4. Representing Documents via Latent Keyphrase Inference

    PubMed Central

    Liu, Jialu; Ren, Xiang; Shang, Jingbo; Cassidy, Taylor; Voss, Clare R.; Han, Jiawei

    2017-01-01

    Many text mining approaches adopt bag-of-words or n-grams models to represent documents. Looking beyond just the words, i.e., the explicit surface forms, in a document can improve a computer’s understanding of text. Being aware of this, researchers have proposed concept-based models that rely on a human-curated knowledge base to incorporate other related concepts in the document representation. But these methods are not desirable when applied to vertical domains (e.g., literature, enterprise, etc.) due to low coverage of in-domain concepts in the general knowledge base and interference from out-of-domain concepts. In this paper, we propose a data-driven model named Latent Keyphrase Inference (LAKI) that represents documents with a vector of closely related domain keyphrases instead of single words or existing concepts in the knowledge base. We show that given a corpus of in-domain documents, topical content units can be learned for each domain keyphrase, which enables a computer to do smart inference to discover latent document keyphrases, going beyond just explicit mentions. Compared with the state-of-art document representation approaches, LAKI fills the gap between bag-of-words and concept-based models by using domain keyphrases as the basic representation unit. It removes dependency on a knowledge base while providing, with keyphrases, readily interpretable representations. When evaluated against 8 other methods on two text mining tasks over two corpora, LAKI outperformed all. PMID:28229132

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

    PubMed

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

    2012-07-01

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

  6. SSWAP: A Simple Semantic Web Architecture and Protocol for semantic web services

    PubMed Central

    Gessler, Damian DG; Schiltz, Gary S; May, Greg D; Avraham, Shulamit; Town, Christopher D; Grant, David; Nelson, Rex T

    2009-01-01

    Background SSWAP (Simple Semantic Web Architecture and Protocol; pronounced "swap") is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP was developed as a hybrid semantic web services technology to overcome limitations found in both pure web service technologies and pure semantic web technologies. Results There are currently over 2400 resources published in SSWAP. Approximately two dozen are custom-written services for QTL (Quantitative Trait Loci) and mapping data for legumes and grasses (grains). The remaining are wrappers to Nucleic Acids Research Database and Web Server entries. As an architecture, SSWAP establishes how clients (users of data, services, and ontologies), providers (suppliers of data, services, and ontologies), and discovery servers (semantic search engines) interact to allow for the description, querying, discovery, invocation, and response of semantic web services. As a protocol, SSWAP provides the vocabulary and semantics to allow clients, providers, and discovery servers to engage in semantic web services. The protocol is based on the W3C-sanctioned first-order description logic language OWL DL. As an open source platform, a discovery server running at (as in to "swap info") uses the description logic reasoner Pellet to integrate semantic resources. The platform hosts an interactive guide to the protocol at , developer tools at , and a portal to third-party ontologies at (a "swap meet"). Conclusion SSWAP addresses the three basic requirements of a semantic web services architecture (i.e., a common syntax, shared semantic, and semantic discovery) while addressing three technology limitations common in distributed service systems: i.e., i) the fatal mutability of traditional interfaces, ii) the rigidity and fragility of static subsumption hierarchies, and iii) the confounding of content, structure, and presentation. SSWAP is novel by establishing

  7. Semantic Visualization Mapping for Illustrative Volume Visualization

    NASA Astrophysics Data System (ADS)

    Rautek, P.; Bruckner, S.; Gröller, M. E.

    2009-04-01

    Measured and simulated data is usually divided into several meaningful intervals that are relevant to the domain expert. Examples from medicine are the specific semantics for different measuring modalities. A PET scan of a brain measures brain activity. It shows regions of homogeneous activity that are labeled by experts with semantic values such as low brain activity or high brain activity. Diffusion MRI data provides information about the healthiness of tissue regions and is classified by experts with semantic values like healthy, diseased, or necrotic. Medical CT data encode the measured density values in Hounsfield units. Specific intervals of the Hounsfield scale refer to different tissue types like air, soft tissue, bone, contrast enhanced vessels, etc. However, the semantic parameters from expert domains are not necessarily used to describe a mapping between the volume attributes and visual appearance. Volume rendering techniques commonly map attributes of the underlying data on visual appearance via a transfer function. Transfer functions are a powerful tool to achieve various visualization mappings. The specification of transfer functions is a complex task. The user has to have expert knowledge about the underlying rendering technique to achieve the desired results. Especially the specification of higher-dimensional transfer functions is challenging. Common user interfaces provide methods to brush in two dimensions. While brushing is an intuitive method to select regions of interest or to specify features, user interfaces for higher-dimensions are more challenging and often non-intuitive. For seismic data the situation is even more difficult since the data typically consists of many more volumetric attributes than for example medical datasets. Scientific illustrators are experts in conveying information by visual means. They also make use of semantics in a natural way describing visual abstractions such as shading, tone, rendering style, saturation

  8. Quantifying diagnostic uncertainty using item response theory: the Posterior Probability of Diagnosis Index.

    PubMed

    Lindhiem, Oliver; Kolko, David J; Yu, Lan

    2013-06-01

    Using traditional Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (American Psychiatric Association, 2000) diagnostic criteria, clinicians are forced to make categorical decisions (diagnosis vs. no diagnosis). This forced choice implies that mental and behavioral health disorders are categorical and does not fully characterize varying degrees of uncertainty associated with a particular diagnosis. Using an item response theory (latent trait model) framework, we describe the development of the Posterior Probability of Diagnosis (PPOD) Index, which answers the question: What is the likelihood that a patient meets or exceeds the latent trait threshold for a diagnosis? The PPOD Index is based on the posterior distribution of θ (latent trait score) for each patient's profile of symptoms. The PPOD Index allows clinicians to quantify and communicate the degree of uncertainty associated with each diagnosis in probabilistic terms. We illustrate the advantages of the PPOD Index in a clinical sample (N = 321) of children and adolescents with oppositional defiant disorder.

  9. Semantic features and semantic categories: differences in rapid activation of the lexicon.

    PubMed

    Frenck-Mestre, C; Bueno, S

    Robust priming was shown in a semantic categorization task for prime-target pairs which shared semantic features (e.g., pumpkin-squash). Priming facilitation for these pairs was demonstrated at extremely rapid prime exposures (28 and 43 ms) and increased with prime duration. The onset and amount of facilitation differed significantly for these semantic, nonassociative pairs and for associative-semantic prime-target pairs (e.g., cow-bull). The latter pairs produced facilitation, but later (at prime-target SOAs of 70 and 200 ms) and of lesser magnitude. These results are discussed in relation to three current models of semantic memory: spreading activation, compound cue, and distributed models.

  10. Scientific Datasets: Discovery and Aggregation for Semantic Interpretation.

    NASA Astrophysics Data System (ADS)

    Lopez, L. A.; Scott, S.; Khalsa, S. J. S.; Duerr, R.

    2015-12-01

    One of the biggest challenges that interdisciplinary researchers face is finding suitable datasets in order to advance their science; this problem remains consistent across multiple disciplines. A surprising number of scientists, when asked what tool they use for data discovery, reply "Google", which is an acceptable solution in some cases but not even Google can find -or cares to compile- all the data that's relevant for science and particularly geo sciences. If a dataset is not discoverable through a well known search provider it will remain dark data to the scientific world.For the past year, BCube, an EarthCube Building Block project, has been developing, testing and deploying a technology stack capable of data discovery at web-scale using the ultimate dataset: The Internet. This stack has 2 principal components, a web-scale crawling infrastructure and a semantic aggregator. The web-crawler is a modified version of Apache Nutch (the originator of Hadoop and other big data technologies) that has been improved and tailored for data and data service discovery. The second component is semantic aggregation, carried out by a python-based workflow that extracts valuable metadata and stores it in the form of triples through the use semantic technologies.While implementing the BCube stack we have run into several challenges such as a) scaling the project to cover big portions of the Internet at a reasonable cost, b) making sense of very diverse and non-homogeneous data, and lastly, c) extracting facts about these datasets using semantic technologies in order to make them usable for the geosciences community. Despite all these challenges we have proven that we can discover and characterize data that otherwise would have remained in the dark corners of the Internet. Having all this data indexed and 'triplelized' will enable scientists to access a trove of information relevant to their work in a more natural way. An important characteristic of the BCube stack is that all

  11. Concealed semantic and episodic autobiographical memory electrified

    PubMed Central

    Ganis, Giorgio; Schendan, Haline E.

    2013-01-01

    Electrophysiology-based concealed information tests (CIT) try to determine whether somebody possesses concealed information about a crime-related item (probe) by comparing event-related potentials (ERPs) between this item and comparison items (irrelevants). Although the broader field is sometimes referred to as “memory detection,” little attention has been paid to the precise type of underlying memory involved. This study begins addressing this issue by examining the key distinction between semantic and episodic memory in the autobiographical domain within a CIT paradigm. This study also addresses the issue of whether multiple repetitions of the items over the course of the session habituate the brain responses. Participants were tested in a 3-stimulus CIT with semantic autobiographical probes (their own date of birth) and episodic autobiographical probes (a secret date learned just before the study). Results dissociated these two memory conditions on several ERP components. Semantic probes elicited a smaller frontal N2 than episodic probes, consistent with the idea that the frontal N2 decreases with greater pre-existing knowledge about the item. Likewise, semantic probes elicited a smaller central N400 than episodic probes. Semantic probes also elicited a larger P3b than episodic probes because of their richer meaning. In contrast, episodic probes elicited a larger late positive complex (LPC) than semantic probes, because of the recent episodic memory associated with them. All these ERPs showed a difference between probes and irrelevants in both memory conditions, except for the N400, which showed a difference only in the semantic condition. Finally, although repetition affected the ERPs, it did not reduce the difference between probes and irrelevants. These findings show that the type of memory associated with a probe has both theoretical and practical importance for CIT research. PMID:23355816

  12. Internet Gamblers Differ on Social Variables: A Latent Class Analysis.

    PubMed

    Khazaal, Yasser; Chatton, Anne; Achab, Sophia; Monney, Gregoire; Thorens, Gabriel; Dufour, Magali; Zullino, Daniele; Rothen, Stephane

    2016-12-27

    Online gambling has gained popularity in the last decade, leading to an important shift in how consumers engage in gambling and in the factors related to problem gambling and prevention. Indebtedness and loneliness have previously been associated with problem gambling. The current study aimed to characterize online gamblers in relation to indebtedness, loneliness, and several in-game social behaviors. The data set was obtained from 584 Internet gamblers recruited online through gambling websites and forums. Of these gamblers, 372 participants completed all study assessments and were included in the analyses. Questionnaires included those on sociodemographics and social variables (indebtedness, loneliness, in-game social behaviors), as well as the Gambling Motives Questionnaire, Gambling Related Cognitions Scale, Internet Addiction Test, Problem Gambling Severity Index, Short Depression-Happiness Scale, and UPPS-P Impulsive Behavior Scale. Social variables were explored with a latent class model. The clusters obtained were compared for psychological measures and three clusters were found: lonely indebted gamblers (cluster 1: 6.5%), not lonely not indebted gamblers (cluster 2: 75.4%), and not lonely indebted gamblers (cluster 3: 18%). Participants in clusters 1 and 3 (particularly in cluster 1) were at higher risk of problem gambling than were those in cluster 2. The three groups differed on most assessed variables, including the Problem Gambling Severity Index, the Short Depression-Happiness Scale, and the UPPS-P subscales (except the sensation seeking subscore). Results highlight significant between-group differences, suggesting that Internet gamblers are not a homogeneous group. Specific intervention strategies could be implemented for groups at risk.

  13. Semantic Mediation via Access Broker: the OWS-9 experiment

    NASA Astrophysics Data System (ADS)

    Santoro, Mattia; Papeschi, Fabrizio; Craglia, Massimo; Nativi, Stefano

    2013-04-01

    , format, etc.) are available for client applications in a transparent way. Notwithstanding the encouraging results of this experiment, some issues (e.g. the automatic discovery of semantic mediation services to be invoked) still need to be solved. Future work will consider new semantic mediation services to broker, and compliance tests with the INSPIRE transformation service. References: Nativi S., Craglia M. and Pearlman J. 2012. The Brokering Approach for Multidisciplinary Interoperability: A Position Paper. International Journal of Spatial Data Infrastructures Research, Vol. 7, 1-15. http://ijsdir.jrc.ec.europa.eu/index.php/ijsdir/article/view/281/319 Vaccari L., Craglia M., Fugazza C. Nativi S. and Santoro M. 2012. Integrative Research: The EuroGEOSS Experience. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5 (6) 1603-1611. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6187671&contentType=Journals+%26+Magazines&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6383184%29

  14. Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure

    NASA Astrophysics Data System (ADS)

    Li, W.; Yang, C.; Nebert, D.; Raskin, R.; Houser, P.; Wu, H.; Li, Z.

    2011-11-01

    Increasing interests in a global environment and climate change have led to studies focused on the changes in the multinational Arctic region. To facilitate Arctic research, a spatial data infrastructure (SDI), where Arctic data, information, and services are shared and integrated in a seamless manner, particularly in light of today's climate change scenarios, is urgently needed. In this paper, we utilize the knowledge-based approach and the spatial web portal technology to prototype an Arctic SDI (ASDI) by proposing (1) a hybrid approach for efficient service discovery from distributed web catalogs and the dynamic Internet; (2) a domain knowledge base to model the latent semantic relationships among scientific data and services; and (3) an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining. A study of the influence of solid water dynamics to the bio-habitat of the Arctic region is used as an example to demonstrate the prototype.

  15. Synergistic Activation of Latent HIV-1 Expression by Novel Histone Deacetylase Inhibitors and Bryostatin-1

    PubMed Central

    Martínez-Bonet, Marta; Isabel Clemente, Maria; Jesús Serramía, Maria; Muñoz, Eduardo; Moreno, Santiago; Ángeles Muñoz-Fernández, Maria

    2015-01-01

    Viral reactivation from latently infected cells has become a promising therapeutic approach to eradicate HIV. Due to the complexity of the viral latency, combinations of efficient and available drugs targeting different pathways of latency are needed. In this work, we evaluated the effect of various combinations of bryostatin-1 (BRY) and novel histone deacetylase inhibitors (HDACIs) on HIV-reactivation and on cellular phenotype. The lymphocyte (J89GFP) or monocyte/macrophage (THP89GFP) latently infected cell lines were treated with BRY, panobinostat (PNB) and romidepsin (RMD) either alone or in combination. Thus, the effect on the viral reactivation was evaluated. We calculated the combination index for each drug combination; the BRY/HDACIs showed a synergistic HIV-reactivation profile in the majority of the combinations tested, whereas non-synergistic effects were observed when PNB was mixed with RMD. Indeed, the 75% effective concentrations of BRY, PNB and RMD were reduced in these combinations. Moreover, primary CD4 T cells treated with such drug combinations presented similar activation and proliferation profiles in comparison with single drug treated cells. Summing up, combinations between BRY, PNB and/or RMD presented a synergistic profile by inducing virus expression in HIV-latently infected cells, rendering these combinations an attractive novel and safe option for future clinical trials. PMID:26563568

  16. Mapping the semantic homunculus: a functional and behavioural analysis of overt semantic generation.

    PubMed

    Esopenko, Carrie; Borowsky, Ron; Cummine, Jacqueline; Sarty, Gordon

    2008-09-01

    Previous neuroanatomical research has shown that semantic processing of action-related language activates the premotor, motor, and sensory cortices somatotopically (e.g., Tettamanti et al., J Cognitive Neurosci. 2005;17(2): 273-281, using a listening task, and Hauk et al., Neuron. 2004;41:301-307 and Pulvermuller et al., Eur J Neurosci 2005;21:793-797; J Cognitive Neurosci 2005;17(6):884-892 using a silent reading task). We examined this somatotopic semantics hypothesis using an overt semantic generation task (i.e., participants generated aloud their own personal description of how they would interact with target object words), rather than semantic comprehension as examined in previous research, so as to provide a stronger test of the hypothesis under conditions that tap one's own semantic knowledge about interacting with objects. Experiment 1 used functional Magnetic Resonance Imaging (fMRI) to examine somatotopically organized activation in the premotor cortex for an overt semantic generation task, using targets that naturally involve either arm interactions or leg interactions. Consistent with previous research, our results showed that semantic processing related to object interaction involves the motor, premotor and sensory cortices in a somatotopic fashion. Previous behavioural research has shown a response advantage in lexical decision for words with multiple meanings or features, which diminishes with tasks that decrease semantic involvement (e.g., Borowsky and Masson, J Exp Psychol: Learn Memory Cognit 1996; 22(1):63-85; Pexman et al., Psychon Bull Rev 2002;9(3): 542-549). Experiment 2 evaluated whether semantic generation response times (total duration of response) display a complexity advantage (i.e., faster response times for more complex objects), and whether complexity ratings were related to the volume of brain activation during the task. Results from this behavioural experiment revealed a significant negative relationship between the total duration

  17. Semantic segmentation of multispectral overhead imagery

    NASA Astrophysics Data System (ADS)

    Prasad, Lakshman; Pope, Paul A.; Sentz, Kari

    2016-05-01

    Land cover classification uses multispectral pixel information to separate image regions into categories. Image segmentation seeks to separate image regions into objects and features based on spectral and spatial image properties. However, making sense of complex imagery typically requires identifying image regions that are often a heterogeneous mixture of categories and features that constitute functional semantic units such as industrial, residential, or commercial areas. This requires leveraging both spectral classification and spatial feature extraction synergistically to synthesize such complex but meaningful image units. We present an efficient graphical model for extracting such semantically cohesive regions. We employ an initial hierarchical segmentation of images into features represented as nodes of an attributed graph that represents feature properties as well as their adjacency relations with other features. This provides a framework to group spectrally and structurally diverse features, which are nevertheless semantically cohesive, based on user-driven identifications of features and their contextual relationships in the graph. We propose an efficient method to construct, store, and search an augmented graph that captures nonadjacent vicinity relationships of features. This graph can be used to query for semantic notional units consisting of ontologically diverse features by constraining it to specific query node types and their indicated/desired spatial interaction characteristics. User interaction with, and labeling of, initially segmented and categorized image feature graph can then be used to learn feature (node) and regional (subgraph) ontologies as constraints, and to identify other similar semantic units as connected components of the constraint-pruned augmented graph of a query image.

  18. Developmental changes in semantic knowledge organization.

    PubMed

    Unger, Layla; Fisher, Anna V; Nugent, Rebecca; Ventura, Samuel L; MacLellan, Christopher J

    2016-06-01

    Semantic knowledge is a crucial aspect of higher cognition. Theoretical accounts of semantic knowledge posit that relations between concepts provide organizational structure that converts information known about individual entities into an interconnected network in which concepts can be linked by many types of relations (e.g., taxonomic, thematic). The goal of the current research was to address several methodological shortcomings of prior studies on the development of semantic organization, by using a variant of the spatial arrangement method (SpAM) to collect graded judgments of relatedness for a set of entities that can be cross-classified into either taxonomic or thematic groups. In Experiment 1, we used the cross-classify SpAM (CC-SpAM) to obtain graded relatedness judgments and derive a representation of developmental changes in the organization of semantic knowledge. In Experiment 2, we validated the findings of Experiment 1 by using a more traditional pairwise similarity judgment paradigm. Across both experiments, we found that an early recognition of links between entities that are both taxonomically and thematically related preceded an increasing recognition of links based on a single type of relation. The utility of CC-SpAM for evaluating theoretical accounts of semantic development is discussed.

  19. Deep Aesthetic Quality Assessment With Semantic Information.

    PubMed

    Kao, Yueying; He, Ran; Huang, Kaiqi

    2017-03-01

    Human beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of aesthetic and semantic labels. A correlation item between these two tasks is further introduced to the framework by incorporating the inter-task relationship learning. This item not only provides some useful insight about the correlation but also improves assessment accuracy of the aesthetic task. In particular, an effective strategy is developed to keep a balance between the two tasks, which facilitates to optimize the parameters of the framework. Extensive experiments on the challenging Aesthetic Visual Analysis dataset and Photo.net dataset validate the importance of semantic recognition in aesthetic quality assessment, and demonstrate that multitask deep models can discover an effective aesthetic representation to achieve the state-of-the-art results.

  20. Semantic transparency affects memory conjunction errors

    PubMed Central

    Wong, Mungchen; Rotello, Caren M.

    2009-01-01

    Memory conjunction errors occur when aspects of two different events are falsely recognized or recalled as having occurred as parts of the same event. One theoretical account of conjunction errors is rooted in traditional dual-process models of recognition judgments, in which responses are based on an item’s familiarity or the retrieval of recollected details associated with the encoding of that item. We manipulated the familiarity of test probes by varying their semantic overlap with studied items, taking advantage of the inherent semantic transparency of compound words. Transparent compounds are those whose component parts (lexemes) are semantically related to the meaning of the entire word. In contrast, opaque compounds’ lexemes do not contribute directly to the meaning of the compound. We showed that the familiarity of semantically transparent assembly lures created from their lexemes (study dog and house, test on doghouse) is greater than the familiarity of opaque assembly lures (study back and draw, test on drawback). A response-signal experiment revealed no evidence for the use of a recall-to-reject process for either semantically transparent or opaque lures. PMID:19966238