How Ontologies Can Improve Semantic Interoperability in Health Care

The main rationale of biomedical terminologies and formalized clinical information models is to provide semantic standards to improve the exchange of meaningful clinical information. Whereas terminologies should express context-independent meanings of domain terms, information models are built to represent the situational and epistemic contexts in which domain terms are used. In practice, semantic interoperability is encumbered by a plurality of different encodings of the same piece of clinical information. The same meaning can be represented by single codes in different terminologies, pre- and postcoordinated expressions in the same terminology, as well as by different combinations of (partly overlapping) terminologies and information models. Formal ontologies can support the automatically recognition and processing of such heterogeneous but isosemantic expressions. In the SemanticHealthNet Network of Excellence a semantic framework is being built which addresses the goal of semantic interoperability by proposing a generalized methodology of transforming existing resources into "semantically enhanced" ones. The semantic enhancements consist in annotations as OWL axioms which commit to an upper-level ontology that provides categories, relations, and constraints for both domain entities and informational entities. Prospects and the challenges of this approach — particularly human and computational limitations — are discussed.

[1]  Martin Boeker,et al.  BioTopLite: An Upper Level Ontology for the Life SciencesEvolution, Design and Application , 2013, GI-Jahrestagung.

[2]  C E Lipscomb,et al.  Medical Subject Headings (MeSH). , 2000, Bulletin of the Medical Library Association.

[3]  Olivier Bodenreider,et al.  The Ontology-Epistemology Divide: A Case Study in Medical Terminology. , 2004, Formal ontology in information systems : proceedings of the ... International Conference. FOIS.

[4]  A. McCray,et al.  Yearbook of Medical Informatics , 2013, Yearbook of Medical Informatics.

[5]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[6]  Martin Boeker,et al.  Proposed actions are no actions: re-modeling an ontology design pattern with a realist top-level ontology , 2012, Journal of Biomedical Semantics.

[7]  Nino B. Cocchiarella,et al.  Logic and Ontology , 2001 .

[8]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[9]  Catalina Martínez-Costa,et al.  Ontology-based reinterpretation of the SNOMED CT context model , 2013, ICBO.

[10]  Newton C. A. da Costa,et al.  Logic and Ontology , 2002 .

[11]  C. E. M. JOAD,et al.  Aristotle's Metaphysics. , 1925, Nature.

[12]  S Schulz,et al.  Formal Ontologies in Biomedical Knowledge Representation , 2013, Yearbook of Medical Informatics.

[13]  Jeff Heflin,et al.  Semantic Interoperability on the Web , 2000 .

[14]  W. Quine,et al.  On What There Will Be , 1948, Realisms Interlinked.

[15]  C. Allen,et al.  Stanford Encyclopedia of Philosophy , 2011 .

[16]  Robert A. Israel,et al.  International Classification of Diseases (ICD) , 2005 .

[17]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[18]  E. Prud hommeaux,et al.  SPARQL query language for RDF , 2011 .

[19]  Chris F. Taylor,et al.  Survey-based naming conventions for use in OBO Foundry ontology development , 2009, BMC Bioinformatics.