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Semantic Knowledge Representation

Diagram of semantic relationships
Project information
Research Area: 

The Semantic Knowledge Representation project conducts basic research in symbolic natural language processing based on the UMLS knowledge sources. A core resource is the SemRep program, which extracts semantic predications from text. SemRep was originally developed for biomedical research. A general methodology is being developed for extending its domain, currently to influenza epidemic preparedness, health promotion, and health effects of climate change.                                          

The SKR project maintains a database of 60 million SemRep  predications extracted from all MEDLINE citations. This database supports the Semantic MEDLINE Web application, which integrates PubMed searching, SemRep predications, automatic summarization, and data visualization. The application is intended to help users manage the results of PubMed searches. Output is visualized as an informative graph with links to the original MEDLINE citations. Convenient access is also provided to additional relevant knowledge resources, such as Entrez Gene, the Genetics Home Reference, and UMLS Metathesaurus.                                               

SKR efforts support innovative infor­mation management applications in biomedicine, as well as basic research. The project team is using semantic predications to find publications that support critical questions used during the creation of clinical practice guidelines with support from the National Heart, Lung, Blood Institute. The Semantic MEDLINE technology was adapted to analyzing NIH grants as SPA  (Semantic Portfolio Analyst), with the support of the Division of Program Coordination, Planning, and Strategic Initiatives in the NIH Office of the Director.

Significant research in SKR is being devoted to developing and applying the literature-based discovery paradigm using semantic predications. One such project is investigating the physiology of sleep and associated pathologies, such as declining sleep quality in aging men, restless legs syndrome, and obstructive sleep apnea; another exploits predications and graph theory for automatic summarization of biomedical text. Further, the SKR team is collaborating with academic researchers in using semantic predications to help interpret the results of microarray experiments, to investigate advanced statistical methods for enhanced information management, and to address the information needs of clinicians at point-of-care.

Bernhardt PJ, Rindflesch TC, Kilicoglu H, Tringali M. Identifying Anatomical Concepts in Biomedical Text for Automatic Selection of Images Medinfo. 2004 Sept.;2004: 1521.
Tringali M, Rindflesch TC, Kilicoglu H, Fiszman M, Bodenreider O. Strategies for Mapping Concepts in Gastrointestinal Endoscopy Reports to the UMLS Metathesaurus Medinfo. 2004 Sept.;2004: 1885.
Slaughter LA, Rindflesch TC. Development of a Test Collection of Manually Extracted Semantic Relationships in Health Consumer Texts MedInfo. 2004 Sept.;2004: 1865.
Fiszman M, Rindflesch TC, Kilicoglu H. Summarization of an Online Medical Encyclopedia MedInfo. 2004 Sept.;2004: 506-510.
Smith L, Rindflesch TC, Wilbure WJ. MedPost: A Part of Speech Tagger for BioMedical Text Bioinformatics. 2004 Sep 22;20(14):2320-1. Epub 2004 Apr 8
Libbus B, Kilicoglu H, Rindflesch TC, Mork JG, Aronson AR. Using Natural Languge Processing, Locus Link, and the Gene Ontology to Compare OMIM to MEDLINE Proceedings of the HLT-NAACL Workshop on Linking the Biological Literature, Ontologies and Databases: Tools for Users. 2004;:69-76.
Fiszman M, Rindflesch TC, Kilicoglu H. Abstraction Summarization for Managing the Biomedical Research Literature Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics. 2004;:76-83.
Rindflesch TC, Fiszman M. The Interaction of Domain Knowledge and Linguistic Structure in Natural Language Processing: Interpreting Hypernymic Propositions in Biomedical Text Journal of Biomedical Informatics. 2003;36(6):462-77.
Leroy G, Rindflesch TC, Libbus B, Kilicoglu H, Chen H. A Syntactic Parser with Semantic Filtering for Biomedical Text. Proceedings of Pacific Symposium on Biocomputing.
Fiszman M, Rindflesch TC, Kilicoglu H. Integrating a Hypernymic Proposition Interpreter into a Semantic Processor for Biomedical Texts AMIA Annu Symp Proc. 2003:239-43.