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Abstract：
Biomedical terminologies play a vital role in managing biomedical data. Missing IS-A relations in a biomedical terminology could be detrimental to its downstream usages. In this paper, we investigate an approach combining logical definitions and lexical features to discover missing IS-A relations in two biomedical terminologies: SNOMED CT and the National Cancer Institute (NCI) thesaurus. The method is applied to unrelated concept-pairs within non-lattice subgraphs: graph fragments within a terminology likely to contain various inconsistencies. Our approach first compares whether the logical definition of a concept is more general than that of the other concept. Then, we check whether the lexical features of the concept are contained in those of the other concept. If both constraints are satisfied, we suggest a potentially missing IS-A relation between the two concepts. The method identified 982 potential missing IS-A relations for SNOMED CT and 100 for NCI thesaurus. In order to assess the efficacy of our approach, a random sample of results belonging to the \"Clinical Findings\" and \"Procedure\" subhierarchies of SNOMED CT and results belonging to the \"Drug, Food, Chemical or Biomedical Material\" subhierarchy of the NCI thesaurus were evaluated by domain experts. The evaluation results revealed that 118 out of 150 suggestions are valid for SNOMED CT and 17 out of 20 are valid for NCI thesaurus.
摘要：
生物医学术语在管理生物医学数据中起着至关重要的作用。在生物医学术语中缺少IS-A关系可能不利于其下游用途。在本文中,我们研究了一种结合逻辑定义和词汇特征的方法,以发现两个生物医学术语中缺失的IS-A关系:SNOMEDCT和美国国家癌症研究所(NCI)同义词库.该方法适用于非晶格子图中不相关的概念对：术语中的图片段可能包含各种不一致。我们的方法首先比较一个概念的逻辑定义是否比另一个概念的逻辑定义更笼统。然后，我们检查该概念的词汇特征是否包含在其他概念的词汇特征中。如果两个约束都满足，我们建议这两个概念之间可能缺失的IS-A关系。该方法确定了SNOMEDCT的982个潜在缺失IS-A关系，NCI同义词库的100个。为了评估我们方法的有效性，属于SNOMEDCT的“临床发现”和“程序”子层次结构的结果的随机样本以及属于“药物”的结果，食物,NCI同义词库的化学或生物医学材料的子层次由领域专家评估。评估结果显示，150条建议中的118条对SNOMEDCT有效，20条中的17条对NCI词库有效。
