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Hits 21 – 40 of 196

21	Sentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patterns
	Hussain, Amir; Bisio, Federica; Gelbukh, Alexander. - : IEEE, 2015
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22	Semantic Annotations for Workflow Interoperability
	Salah, Hamri; Nacer, Boudjlida; Mahmoud, Boufaida
	In: ISSN: 0350-5596 ; Informatica ; https://hal.inria.fr/hal-01111453 ; Informatica, Slovene Society Informatika, Ljubljana, 2014, 38 (4), pp.347-366 (2014)
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23	Fuzzy modeling of a composite agronomical feature using FisPro: the case of vine vigor
	Coulon-Leroy, C.; Charnomordic, Brigitte; Thiollet-Scholtus, Marie...
	In: 15th International Conference, IPMU 2014 ; https://hal.inrae.fr/hal-02601052 ; 15th International Conference, IPMU 2014, Jul 2014, Montpellier, France. pp.127-137, ⟨10.1007/978-3-319-08795-5_14⟩ (2014)
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24	Natural Language Semantics using Probabilistic Logic
	Beltagy, Islam
	In: DTIC (2014)
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25	Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014
	Cano, Ignacio; Singh, Sameer; Guestrin, Carlos
	In: DTIC (2014)
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26	A systematic approach towards creative urban design
	Al-Sayed, K
	In: In: Gero, J, (ed.) Design Computing and Cognition '12. (pp. 133-150). Springer Netherlands: Netherlands. (2014) (2014)
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27	A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
	Cameron, Delroy H.; Bodenreider, Olivier; Yalamanchili, Himi...
	In: Krishnaprasad Thirunarayan (2014)
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28	Updating Relational Views Using Knowledge at View Definition and View Update Time
	Larson, James A.; Sheth, Amit P.
	In: Amit P. Sheth (2014)
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29	A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
	Cameron, Delroy H.; Bodenreider, Olivier; Yalamanchili, Himi; Danh, Tu Thien; Vallabhaneni, Sreeram; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Rindflesch, Thomas
	In: Amit P. Sheth (2014)
	Abstract: Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson have been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson, has been developed. Results Our methodology not only recovered the three associations commonly recognized as Swanson’s hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology to semi-automatically recover and decompose Swanson’s RS-DFO hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). Based on our observations, three critical aspects of LBD include: (1) the need for more expressive representations beyond Swanson’s ABC model; (2) an ability to accurately extract semantic information from text; and (3) the semantic integration of scientific literature and structured background knowledge.
	Keyword: Background Knowledge; Bioinformatics; Communication; Communication Technology and New Media; Computer Science and Engineering; Computer Sciences; Databases and Information Systems; Life Sciences; Literature-Based Discovery (LBD); OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Semantic Associations; Semantic Predications; Social and Behavioral Sciences; Subgraph Creation; Swanson’s Hypothesis
	URL: https://works.bepress.com/amit_sheth/81
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30	Accelerating Exploitation of Low-grade Intelligence through Semantic Text Processing of Social Media
	Kase, Sue E
	In: DTIC (2013)
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31	A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
	Cameron, Delroy H.; Bodenreider, Olivier; Yalamanchili, Himi...
	In: Kno.e.sis Publications (2013)
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32	Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications
	Altadmri, Amjad; Ahmed, Amr; Mohtasseb, Haytham. - : Springer Berlin Heidelberg, 2012
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33	A Framework for Modeling and Simulation of the Artificial
	Douglass, Scott A; Mittal, Saurabh
	In: DTIC (2012)
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34	Capturing Cultural Glossaries: Case-study I *
	Madiba, Matete; Mphahlele, Lorna; Kganyago, Matlakala
	In: Lexikos; Vol. 13 (2003) ; 2224-0039 (2011)
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35	PSYCHONET 2: contextualized and enriched psycholinguistic commonsense ontology
	Mohtasseb Billah, Haytham; Ahmed, Amr; Altadmri, Amjad. - 2011
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36	A Concept Map Knowledge Model of Intelligence Analysis
	Derbentseva, Natalia; Mandel, David R
	In: DTIC (2011)
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37	Capturing Cultural Glossaries: Case-study I *
	Matete Madiba; Lorna Mphahlele; Matlakala Kganyago
	In: Lexikos, Vol 13 (2011) (2011)
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38	Transfer Learning in Integrated Cognitive Systems
	Shapiro, Daniel; Langley, Pat; Stracuzzi, David J....
	In: DTIC (2010)
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39	Validation Workshop of the DRDC Concept Map Knowledge Model: Issues in Intelligence Analysis
	Gauthier, Michelle
	In: DTIC (2010)
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40	Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
	Marton, Yuval
	In: DTIC (2009)
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