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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.

Fiszman M, Rindflesch TC, Kilicoglu H. Interpreting Hypernymic Propositions in an Online Medical Encyclopedia AMIA Annu Symp Proc. 2003:840.
Rindflesch TC, Libbus B, Hristovski D, Aronson AR, Kilicoglu H. Semantic Relations Asserting the Etiology of Genetic Diseases AMIA Annu Symp Proc. 2003:554-8.
Kashyap V. The UMLS Semantic Network and the Semantic Web. AMIA Annu Symp Proc. 2003:351-5.
Kashyap V, Ramakrishan C, Rindflesch TC. Towards Semi-Automatic Generation of Biomedical Ontologies AMIA Annu Symp Proc. 2003 Nov;:886.
Rindflesch TC, Aronson AR. Semantic Processing for Enhanced Access to Biomedical Knowledge Real World Semantic Web Applications, IOS Press. 2002;:157-72.
Rindflesch TC, Rajan J, Hunter L. Extracting Molecular Binding Relationships from Biomedical Text Proc. of the 6th Applied Natural Language Processing Conference. 2000;: 188-95.
Rindflesch TC, Fiszman M, Kilicoglu H, Libbus B. Semantic Knowledge Representation Project: A Report to the Board of Scientific Counselors September 2003 Technical Report to the LHNCBC Board of Scientific Counselors.
Sarkar IN, Rindflesch TC. Discovering Protein Similarity Using Natural Language Processing Proc AMIA Symp. 2002:677-81.
Libbus B, Rindflesch TC. NLP-Based Information Extraction for Managing the Molecular Biology Literature Proc AMIA Symp. 2002:445-9.
Srinivasan P, Rindflesch T. Exploring Text Mining from MEDLINE Proc AMIA Symp. 2002:722-6.