Towards Context Driven Modularization of Large Biomedical Ontologies
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- Siemens AG
- Document Type:
- Manuscript
- Date:
- Received 30 July 2009 16:26 UTC; Posted 30 July 2009
- Subjects:
- Bioinformatics
- Abstract:
Formal knowledge about human anatomy, radiology or diseases is necessary to support clinical applications such as medical image search. This machine processable knowledge can be acquired from biomedical domain ontologies, which however, are typically very large and complex models. Thus, their straightforward incorporation into the software applications becomes difficult. In this paper we discuss first ideas on a statistical approach for modularizing large medical ontologies and we prioritize the practical applicability aspect. The underlying assumption is that the application relevant ontology fragments, i.e. modules, can be identified by the statistical analysis of the ontology concepts in the domain corpus. Accordingly, we argue that most frequently occurring concepts in the domain corpus define the application context and can therefore potentially yield the relevant ontology modules. We illustrate our approach on an example case that involves a large ontology on human anatomy and report on our first manual experiments.
- Collection:
- International Conference on Biomedical Ontology
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- License:
- This document is licensed to the public under the Creative Commons Attribution 3.0 License
- How to cite this document:
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Wennerberg, Pinar and Zillner, Sonja. Towards Context Driven Modularization of Large Biomedical Ontologies. Available from Nature Precedings <http://dx.doi.org/10.1038/npre.2009.3522.1> (2009)
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