Text Snippets to Corroborate Medical Relations: An Unsupervised Approach using a Knowledge Graph and Embeddings

Maulik Kamdar , Craig Stanley, Michael Carroll, Linda Wogulis, Will Dowling, Helena F. Deus, Mevan Samarasinghe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Knowledge graphs have been shown to significantly improve search results. Usually populated by subject matter experts, relations therein need to keep up to date with medical literature in order for search to remain relevant. Dynamically identifying text snippets in literature that confirm or deny knowledge graph triples is increasingly becoming the differentiator between trusted and untrusted medical decision support systems. This work describes our approach to mapping triples to medical text. A medical knowledge graph is used as a source of triples that are used to find matching sentences in reference text. Our unsupervised approach uses phrase embeddings and cosine similarity measures, and boosts candidate text snippets when certain key concepts exist. Using this approach, we can accurately map semantic relations within the medical knowledge graph to text snippets with a precision of 61.4% and recall of 86.3%. This method will be used to develop a novel application in the future to retrieve medical relations and corroborating snippets from medical text given a user query.
Original languageAmerican English
Title of host publicationAMIA Summits on Translational Science Proceedings
Number of pages10
StatePublished - May 30 2020
EventAMIA Informatics Summit - Virtual
Duration: Mar 23 2020Mar 26 2020


ConferenceAMIA Informatics Summit
Internet address


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