Discovering evolving regions in life science ontologies

Authors:
Michael Hartung;Anika Gross;Toralf Kirsten;Erhard Rahm
Affiliations:
Interdisciplinary Centre for Bioinformatics, University of Leipzig and Department of Computer Science, University of Leipzig;Interdisciplinary Centre for Bioinformatics, University of Leipzig and Department of Computer Science, University of Leipzig;Interdisciplinary Centre for Bioinformatics, University of Leipzig and Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig;Interdisciplinary Centre for Bioinformatics, University of Leipzig and Department of Computer Science, University of Leipzig
Venue:
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
Year:
2010

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Abstract

Ontologies are heavily used in life sciences and evolve continuously to incorporate new or changed insights. Often ontology changes affect only specific parts (regions) of ontologies making it valuable for ontology users and applications to know the heavily changed regions on the one hand and stable regions on the other hand. However, the size and complexity of life science ontologies renders manual approaches to localize changing or stable regions impossible. We therefore propose an approach to automatically discover evolving or stable ontology regions. We evaluate the approach by studying evolving regions in the Gene Ontology and the NCI Thesaurus.