Kamdar Maulik R, Stanley Craig E, Carroll Michael, Wogulis Linda, Dowling William, Deus Helena F, Samarasinghe Mevan
Elsevier, Health and Commercial Markets, Philadelphia, PA.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:288-297. eCollection 2020.
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.
知识图谱已被证明能显著改善搜索结果。通常由主题专家填充，其中的关系需要与医学文献保持同步更新，以便搜索结果仍具相关性。动态识别文献中确认或否定知识图谱三元组的文本片段，正日益成为可信和不可信医学决策支持系统之间的区别所在。这项工作描述了我们将三元组映射到医学文本的方法。医学知识图谱被用作三元组的来源，用于在参考文献中查找匹配的句子。我们的无监督方法使用短语嵌入和余弦相似度度量，并在存在某些关键概念时增强候选文本片段。使用这种方法，我们可以将医学知识图谱中的语义关系准确地映射到文本片段，精确率为61.4%，召回率为86.3%。该方法未来将用于开发一种新颖的应用程序，以便在给定用户查询时从医学文本中检索医学关系和确证性片段。
