基于概念名称词法特征的逻辑定义中缺失层次关系识别。

CEUR workshop proceedings Pub Date : 2016-08-01
Olivier Bodenreider
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引用次数: 0

摘要

目的:基于概念名称的词法特征,从逻辑定义中识别SNOMED CT中缺失的层次关系。方法:我们首先根据概念名称的词法特征创建逻辑定义,并在OWL EL中表示。我们使用ELK推理器推断这些概念之间的层次(subClassOf)关系。最后,我们将从词汇特征得到的层次结构与原始的SNOMED CT层次结构进行比较。为了评估目的,我们手动检查差异。结果:应用于15,833个无序和程序概念,我们的方法确定了559个潜在缺失的层次关系,其中78%被认为是有效的。结论:这种词法质量保证方法易于实施,高效且可扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identifying Missing Hierarchical Relations in SNOMED CT from Logical Definitions Based on the Lexical Features of Concept Names.

Identifying Missing Hierarchical Relations in SNOMED CT from Logical Definitions Based on the Lexical Features of Concept Names.

Identifying Missing Hierarchical Relations in SNOMED CT from Logical Definitions Based on the Lexical Features of Concept Names.

Identifying Missing Hierarchical Relations in SNOMED CT from Logical Definitions Based on the Lexical Features of Concept Names.

Objectives: To identify missing hierarchical relations in SNOMED CT from logical definitions based on the lexical features of concept names.

Methods: We first create logical definitions from the lexical features of concept names, which we represent in OWL EL. We infer hierarchical (subClassOf) relations among these concepts using the ELK reasoner. Finally, we compare the hierarchy obtained from lexical features to the original SNOMED CT hierarchy. We review the differences manually for evaluation purposes.

Results: Applied to 15,833 disorder and procedure concepts, our approach identified 559 potentially missing hierarchical relations, of which 78% were deemed valid.

Conclusions: This lexical approach to quality assurance is easy to implement, efficient and scalable.

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