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Semantic Knowledge Representation

Diagram of semantic relationships
Project information
Researchers: 

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: 
Kilicoglu H, Rosemblat G, Malicki M, Ter Riet G. Automatic recognition of self-acknowledged limitations in clinical research literature. J Am Med Inform Assoc. 2018 Jul 1;25(7):855-861. doi: 10.1093/jamia/ocy038.
Edinger T, Demner-Fushman D, Cohen AM, Bedrick S, Hersh W. Evaluation of Clinical Text Segmentation to Facilitate Cohort Retrieval. AMIA Annu Symp Proc. 2018 Apr 16;2017:660-669. eCollection 2017.
Roberts K, Gururaj A, Chen X, Pournejati S, Cohen T, Hersh WR, Demner-Fushman D. Information Retrieval for Biomedical Datasets. 2016 bioCADDIE Challenge. AMIA 2017.
Ben Abacha A, Long LR, Seco de Herrera AG, Antani SK, Wang K, Demner-Fushman D. Named Entity Recognition in Functional Neuroimaging Literature. BIBM 2017
Blake C, Rindflesch TC. Leveraging syntax to better capture the semantics of elliptical coordinated compound noun phrases. J Biomed Inform. 2017 Aug;72:120-131. doi: 10.1016/j.jbi.2017.07.001. Epub 2017 Jul 4.
Kilicoglu H, Rosemblat G, Rindflesch TC. Assigning factuality values to semantic relations extracted from biomedical research literature. PLoS One. 2017 Jul 5;12(7):e0179926. doi: 10.1371/journal.pone.0179926. eCollection 2017.
Rindflesch TC, Blake CL, Fiszman M, Kilicoglu H, Rosemblat G, Schneider J, Zeiss CJ. Informatics Support for Basic Research in Biomedicine. ILAR J. 2017 Jul 1;58(1):80-89. doi: 10.1093/ilar/ilx004.
Kilicoglu H. Biomedical text mining for research rigor and integrity: tasks, challenges, directions. Brief Bioinform. 2017 Jun 13. doi: 10.1093/bib/bbx057.
Kilicoglu H. Inferring Implicit Causal Relationships in Biomedical Literature. Proc 15th Workshop on Biomedical Natural Language Processing. Pages 46-55. 2016.
Hristovski D, Kastrin A, Dinevski D, Burgun A, Ziberna L, Rindflesch TC. Using Literature-Based Discovery to Explain Adverse Drug Effects. J Med Syst. 2016 Aug;40(8):185. doi: 10.1007/s10916-016-0544-z. Epub 2016 Jun 18.

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