Experience in aligning anatomical ontologies

被引:60
作者
Zhang, Songmao [1 ]
Bodenreider, Olivier
机构
[1] Chinese Acad Sci, Beijing 100864, Peoples R China
[2] US Natl Lib Med, Bethesda, MD 20894 USA
关键词
anatomy; artificial intelligence; knowledge models; medical knowledge; ontologies; semantic matching;
D O I
10.4018/jswis.2007040101
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
The objective of this article is to recapitulate our experience in aligning large anatomical ontologies (Foundational Model of Anatomy, GALEN, Adult Mouse Anatomical Dictionary and NCI Thesaurus) having different representation formalisms. Our approach to aligning concepts (directly) is automatic, rule-based, and operates at the schema level, generating mostly point-to-point mappings. It uses a combination of lexical, structural and semantic techniques. It also takes advantage of domain-specific knowledge (lexical knowledge from external resources, such ay the Unified Medical Language System, as well as knowledge augmentation and inference techniques). In addition to point-to-point mapping of concepts, we present the alignment of relationships and the mapping of concepts group-to-group. We have also successfully tested an indirect alignment through a reference ontology. We present an evaluation of our techniques, both against a gold standard and against a generic schema matching system. The advantages and limitations of our approach arc, analyzed and discussed throughout the article.
引用
收藏
页码:1 / 26
页数:26
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