The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
A reference ontology for biomedical informatics: the foundational model of anatomy
Journal of Biomedical Informatics - Special issue: Unified medical language system
Artificial Intelligence in Medicine
Automated comparative auditing of NCIT genomic roles using NCBI
Journal of Biomedical Informatics
Structural group auditing of a UMLS semantic type's extent
Journal of Biomedical Informatics
Auditing concept categorizations in the UMLS
Artificial Intelligence in Medicine
Guest Editorial: Special Issue on Auditing of Terminologies
Journal of Biomedical Informatics
Development and evaluation of an ontology for guiding appropriate antibiotic prescribing
Journal of Biomedical Informatics
Validating the semantics of a medical iconic language using ontological reasoning
Journal of Biomedical Informatics
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The Foundational Model of Anatomy (FMA) ontology is a domain reference ontology based on a disciplined modeling approach. Due to its large size, semantic complexity and manual data entry process, errors and inconsistencies are unavoidable and might remain within the FMA structure without detection. In this paper, we present computable methods to highlight candidate concepts for various relationship assignment errors. The process starts with locating structures formed by transitive structural relationships (part_of, tributary_of, branch_of) and examine their assignments in the context of the IS-A hierarchy. The algorithms were designed to detect five major categories of possible incorrect relationship assignments: circular, mutually exclusive, redundant, inconsistent, and missed entries. A domain expert reviewed samples of these presumptive errors to confirm the findings. Seven thousand and fifty-two presumptive errors were detected, the largest proportion related to part_of relationship assignments. The results highlight the fact that errors are unavoidable in complex ontologies and that well designed algorithms can help domain experts to focus on concepts with high likelihood of errors and maximize their effort to ensure consistency and reliability. In the future similar methods might be integrated with data entry processes to offer real-time error detection.