基于医学本体的术语相似度算法研究

Research on Semantic Similarity Estimation Algorithm of Medical Terminology Based on Medical Ontology

【目的】借助大型的医学本体,提升医学术语相似度计算精度。【方法】依据SNOMED CT和MeS H两个医学本体的层级结构和语义关系,提取概念术语的深度、距离等语义参数,并用概念密度对其加权得到深度系数和距离系数,构造相似度函数进行术语相似度计算。【结果】该算法能在两个医学本体中进行术语相似度计算,较传统算法更加接近人工评分标准。【局限】该方法较为依赖本体结构。【结论】该方法能够提高以医学本体为基础的术语相似度计算精确度。

[Objective] Based on the comprehensive medical Ontologies, this paper proposes a new algorithm to enhance the precision of semantic similarity estimation of medical terminology. [Methods] On the basis of the hierarchy and semantic relationships of concepts of SNOMED CT and MESH, the semantic parameters such as depth and distance are extracted. Then the depth factor and the distance factor are obtained weighted by the concept density, and the function of semantic similarity is thus established. [Results] The algorithm is applicable to both distinctive medical Ontologies, and the experimental results demonstrate that this algorithm has higher correlation coefficient with manual scoring versus conventional algorithms. [Limitations] This algorithm is subject to hierarchy of Ontologies. [Conclusions] The new algorithm benefits the enhanced precision of semantic similarity estimation of medical terminology.