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Semantic Knowledge Representation

Diagram of semantic relationships
Project information

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.

Publications/Tools: 
Morid MA, Fiszman M, Raja K, Jonnalagadda SR, Del Fiol G. Classification of clinically useful sentences in clinical evidence resources. J Biomed Inform. 2016 Apr;60:14-22. doi: 10.1016/j.jbi.2016.01.003. Epub 2016 Jan 13.
Kilicoglu H, Rosemblat G, Cairelli M, Rindflesch TC. A Compositional Interpretation of Biomedical Event Factuality. Proc of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015).
Cairelli MJ, Fiszman M, Zhang H, Rindflesch TC. Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury. J Biomed Semantics. 2015 May 18;6:25. doi: 10.1186/s13326-015-0022-4. eCollection 2015.
Cameron D, Kavuluru R, Rindflesch TC, Sheth AP, Thirunarayan K, Bodenreider O. Context-driven automatic subgraph creation for literature-based discovery. J Biomed Inform. 2015 Apr;54:141-57. doi: 10.1016/j.jbi.2015.01.014. Epub 2015 Feb 7.
Zhang R, Adam TJ, Simon G, Cairelli MJ, Rindflesch T, Pakhomov S, Melton GB. Mining Biomedical Literature to Explore Interactions between Cancer Drugs and Dietary Supplements. AMIA Jt Summits Transl Sci Proc. 2015 Mar 23;2015:69-73. eCollection 2015.
Hristovski D, Dinevski D, Kastrin A, Rindflesch TC. Biomedical question answering using semantic relations. BMC Bioinformatics. 2015 Jan 16;16:6. doi: 10.1186/s12859-014-0365-3.
Shang N, Xu H, Rindflesch TC, Cohen T. Identifying plausible adverse drug reactions using knowledge extracted from the literature. J Biomed Inform. 2014 Dec;52:293-310. doi: 10.1016/j.jbi.2014.07.011. Epub 2014 Jul 19.
Mishra R, Bian J, Fiszman M, Weir CR, Mostafa J, Fiol GD. Text summarization in the biomedical domain: a systematic review of recent research. J Biomed Inform. 2014 Dec;52:457-67. doi: 10.1016/j.jbi.2014.06.009. Epub 2014 Jul 10.
Culbertson A, Fiszman M, Shin D, Rindflesch TC. Semantic processing to identify adverse drug event information from black box warnings. AMIA Annu Symp Proc. 2014 Nov 14;2014:442-8. eCollection 2014.
Zhang R, Cairelli MJ, Fiszman M, Kilicoglu H, Rindflesch TC, Pakhomov SV, Melton GB. Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs. Cancer Inform. 2014 Oct 14;13(Suppl 1):103-11. doi: 10.4137/CIN.S13889. eCollection 2014.

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