Authors:
Manuel Möller
1
;
Michael Sintek
1
;
Ralf Biedert
1
;
Patrick Ernst
1
;
Andreas Dengel
1
and
Daniel Sonntag
2
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI) and University of Kaiserslautern, Germany
;
2
German Research Center for Artificial Intelligence (DFKI), Germany
Keyword(s):
Formal knowledge representation, Automatic ontology generation, Medical ontologies, International classification of diseases.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
Data Engineering
;
Domain Analysis and Modeling
;
e-Business
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
Abstract:
Current efforts in the biomedical ontology community focus on establishing interoperability and data integration. In covering human diseases, one of the major international standards in clinical practice is the International Classification for Diseases (ICD), maintained by the World Health Organization (WHO). Several country- and language-specific adaptations exist which share the general structure of the WHO version but differ in certain details. This complicates the exchange of patient records and hampers data integration across language borders. We present our approach for modeling the hierarchy of the ICD-10 using theWeb Ontology Language (OWL). Our model captures the hierarchical information of the ICD-10 as well as comprehensive class labels for English and German. Specialties such as “Exclusion” statements, which make statements about the disjointness of certain ICD-10 categories, are modeled in a formal way. For properties which exceed the expressivity of OWL-DL, we provide a
separate OWL-Full component which allows us to use the hierarchical knowledge and class labels with existing OWL-DL reasoners and capture the additional information in a machine-interpretable way.
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