Enhancing the coverage of SemRep using a relation classification approach.

Journal: Journal of biomedical informatics
Published Date: Jul 1, 2024

Abstract

OBJECTIVE: Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The broad-coverage natural language processing (NLP) tool SemRep has established a solid baseline for extracting subject-predicate-object triples from biomedical text and has served as the backbone of the Semantic MEDLINE Database (SemMedDB), a PubMed-scale repository of semantic triples. While SemRep achieves reasonable precision (0.69), its recall is relatively low (0.42). In this study, we aimed to enhance SemRep using a relation classification approach, in order to eventually increase the size and the utility of SemMedDB.

Authors

  • Shufan Ming
    School of Information Sciences, University of Illinois Urbana-Champaign, 501 E Daniel St., Champaign, 61820, IL, USA.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Halil Kilicoglu
    School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, United States.

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