论文标题
语义搜索大规模临床本体论
Semantic Search for Large Scale Clinical Ontologies
论文作者
论文摘要
当查询使用不同的词汇时，在大型临床本体论中寻找概念可能会具有挑战性。克服此问题的搜索算法在诸如概念归一化和本体匹配之类的应用程序中很有用，在该应用程序中，可以使用不同的同义词以不同的方式参考概念。在本文中，我们提出了一种基于深度学习的方法，以建立大型临床本体论的语义搜索系统。我们提出了一个三胞胎 - 伯特模型和一种直接从本体论生成培训数据的方法。使用五个真实的基准数据集评估了该模型，结果表明，我们的方法在自由文本到概念和概念概念方面都能获得很高的成果，从而超过所有基线方法。
Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies. A search algorithm that overcomes this problem is useful in applications such as concept normalisation and ontology matching, where concepts can be referred to in different ways, using different synonyms. In this paper, we present a deep learning based approach to build a semantic search system for large clinical ontologies. We propose a Triplet-BERT model and a method that generates training data directly from the ontologies. The model is evaluated using five real benchmark data sets and the results show that our approach achieves high results on both free text to concept and concept to concept searching tasks, and outperforms all baseline methods.
