Towards a Broad-Coverage Biomedical Ontology Based on Description Logics

We describe an ontology engineering methodology by which conceptual knowledge is extracted from an informal medical thesaurus (UMLS) and automatically converted into a formal description logics system (LOOM). Our approach consists of four steps: concept definitions are automatically generated from the UMLS, integrity checking of taxonomic and partonomic hierarchies is performed by LOOM's terminological classifier, cycles and inconsistencies are eliminated, as well as incremental refinement of the evolving knowledge base is performed by a domain expert. We report on experiments with a very large knowledge base composed of 164,000 concepts and 76,000 relations.

[1]  A. Rector.  Clinical Terminology: Why Is it so Hard? , 1999, Methods of Information in Medicine.

[2]  Ian Horrocks,et al.  The GRAIL concept modelling language for medical terminology , 1997, Artif. Intell. Medicine.

[3]  Frank J. Oles,et al.  Scalable and expressive medical terminologies. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[4]  Gert Smolka,et al.  Attributive Concept Descriptions with Complements , 1991, Artif. Intell..

[5]  Nicola Guarino,et al.  Dwq : Esprit Long Term Research Project, No 22469 Part-whole Relations in Object-centered Systems: an Overview Part-whole Relations in Object-centered Systems: an Overview , 2022 .

[6]  Aldo Gangemi,et al.  An ontological analysis of the UMLS Metathesaurus , 1998, AMIA.

[7]  A T McCray,et al.  The Representation of Meaning in the UMLS , 1995, Methods of Information in Medicine.

[8]  M Fieschi,et al.  Review of biomedical knowledge and data representation with conceptual graphs. , 1998, Methods of information in medicine.

[9]  Kent A. Spackman,et al.  Normal forms for description logic expressions of clinical concepts in SNOMED RT , 2001, AMIA.

[10]  Martin Romacker,et al.  Creating Knowledge Repositories from Biomedical Reports: The MEDSYNDIKATE Text Mining System , 2001, Pacific Symposium on Biocomputing.

[11]  Olivier Bodenreider,et al.  Circular hierarchical relationships in the UMLS: etiology, diagnosis, treatment, complications and prevention , 2001, AMIA.

[12]  John D. Olson,et al.  College of American Pathologists , 2020, Definitions.

[13]  E. B Schulz,et al.  Application of Technology: Symbolic Anatomic Knowledge Representation in the Read Codes Version 3: Structure and Application , 1997, J. Am. Medical Informatics Assoc..

[14]  Kent A. Spackman,et al.  Compositional concept representation using SNOMED: towards further convergence of clinical terminologies , 1998, AMIA.

[15]  Udo Hahn,et al.  Part-Whole Reasoning: A Case Study in Medical Ontology Engineering , 1999, IEEE Intell. Syst..

[16]  Bernhard Nebel,et al.  An Empirical Analysis of Terminological Representation Systems , 1992, Artif. Intell..

[17]  Ian Horrocks,et al.  A Description Logic with Transitive and Inverse Roles and Role Hierarchies , 1999, J. Log. Comput..

[18]  Douglas Herrmann,et al.  A Taxonomy of Part-Whole Relations , 1987, Cogn. Sci..

[19]  Robert M. MacGregor,et al.  The Loom Knowledge Representation Language. , 1987 .

[20]  James G. Schmolze,et al.  The NIKL experience 1 , 1991, Comput. Intell..

[21]  Alan L. Rector.  Analysis of propagation along transitive roles: Formalisation of the GALEN experience with Medical Ontologies , 2002, Description Logics.

[22]  James J. Cimino,et al.  Distributed cognition and knowledge-based controlled medical terminologies , 1998, Artif. Intell. Medicine.

[23]  Stefan Schulz,et al.  Necessary Parts and Wholes in Bio-Ontologies , 2002, KR.

[24]  George Hripcsak,et al.  Research Paper: Knowledge-based Approaches to the Maintenance of a Large Controlled Medical Terminology , 1994, J. Am. Medical Informatics Assoc..

[25]  D. Cruse.  On the transitivity of the part-whole relation , 1979, Journal of Linguistics.