Investigating implicit knowledge in ontologies with application to the anatomical domain.
Knowledge in biomedical ontologies can be explicitly represented (often by means of semantic relations), but may also be implicit, i.e., embedded in the concept names and inferable from various combinations of semantic relations. This paper investigates implicit knowledge in two ontologies of anatomy: the Foundational Model of Anatomy and GALEN. The methods consist of extracting the knowledge explicitly represented, acquiring the implicit knowledge through augmentation and inference techniques, and identifying the origin of each semantic relation. The number of relations (12 million in FMA and 4.6 million in GALEN), broken down by source, is presented. Major findings include: each technique provides specific relations; and many relations can be generated by more than one technique. The application of these findings to ontology auditing, validation, and maintenance is discussed, as well as the application to ontology integration.
Zhang
S
S
U.S. National Library of Medicine, 8600 Rockville Pike, MS 43, Bethesda, Maryland 20894, USA. szhang@nlm.nih.gov
Bodenreider
O
O
D001185:Artificial Intelligence; D019295:Computational Biology; D008953:Models, Anatomic; D012660:Semantics