Wang Jian, Day Roger, Visweswaran Shyam, Hogan William
Department of Biomedical Informatics, University of Pittsburgh.
AMIA Annu Symp Proc. 2010 Nov 13;2010:842-6.
This study explored the possibility that semantic distance metrics can be used to develop methods for auditing biomedical ontologies. We developed and tested an approach using the Foundational Model of Anatomy (FMA) and the body-structure taxonomy of SNOMED CT. We evaluated 190 class pairs in human anatomical structures using three semantic distance metrics: simple edge count, normalized path length, and information content. We applied principal component analysis (PCA) to study relationships between the semantic distance measurements so produced in FMA and SNOMED CT. We found that our application of PCA could detect significant discrepancies, but not necessarily outright mistakes, in the two ontologies. A review of discrepancies revealed that they often relate to multiple design perspectives employed in ontological definitions.
本研究探讨了语义距离度量可用于开发生物医学本体审核方法的可能性。我们开发并测试了一种使用解剖学基础模型（FMA）和SNOMED CT的身体结构分类法的方法。我们使用三种语义距离度量对190个人体解剖结构类对进行了评估：简单边计数、归一化路径长度和信息内容。我们应用主成分分析（PCA）来研究FMA和SNOMED CT中产生的语义距离测量之间的关系。我们发现，我们对PCA的应用能够检测出这两种本体中的显著差异，但不一定能检测出明显的错误。对差异的审查表明，它们通常与本体定义中采用的多种设计观点有关。
