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Semantic Knowledge Representation

Diagram of semantic relationships
Project information
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Note: this web page will be archived on or around October 1 2019 and will be part of the NLM Lister Hill National Center for Biomedical Communications archive collection.

Semantic Knowledge Representation

In support of innovative information management applications in biomedicine as well as basic research, the Semantic Knowledge Representation project (SKR) efforts use symbolic natural language processing based on the UMLS knowledge sources. SKR research examples include developing and applying the literature-based discovery paradigm using semantic predications. One such project looked into the physiology of sleep and associated pathologies, such as declining sleep quality in aging men, restless legs syndrome, and obstructive sleep apnea; another project exploited predications and graph theory for automatic summarization of biomedical text.

SemRep

A core SKR resource is the SemRep program, which extracts semantic predications from text. Originally developed for biomedical research, SemRep was subsequently extended to genetic etiology and pharmacogenomics. Then, through a general methodology based on ontology and thesauri development, SemRep's application was extended to other fields, such as influenza epidemic preparedness and health promotion.  By applying natural language processing techniques, our research can better inform patients, health care providers, researchers, and the general public.

SemMedDB

The SKR project maintains SemMedDB, a repository of 96.3 million SemRep predications (subject-predicate-object triples) extracted our semantic interpreter SemRep from all MEDLINE citations up to Dec. 2018. This database supports the Semantic MEDLINE web application (SemMed), 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.

Downloads related to SemRep, SemMedDB, and SKR are available from our Access to SemRep/SemMedDB/SKR Resources page.

Publications/Tools: 
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.
Cohen T, Widdows D, Stephan C, Zinner R, Kim J, Rindflesch TC, Davies P. Predicting high-throughput screening results with scalable literature-based discovery methods. CPT Pharmacometrics Syst Pharmacol. 2014 Oct 8;3:e140. doi: 10.1038/psp.2014.37.

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