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  1.  38
    On the Application of Formal Principles to Life Science Data: A Case Study in the Gene Ontology.Jacob Köhler, Anand Kumar & Barry Smith - 2004 - In Proceedings of DILS 2004 (Data Integration in the Life Sciences), (Lecture Notes in Bioinformatics 2994). Berlin: Springer. pp. 79-94.
    Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such as ontology-based (...)
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  2.  81
    Quality Control for Terms and Definitions in Ontologies and Taxonomies.Jacob Köhler, Katherine Munn, Alexander Rüegg, Andre Skusa & Barry Smith - 2006 - BMC Bioinformatics 7 (212):1-12.
    Background: Ontologies and taxonomies are among the most important computational resources for molecular biology and bioinformatics. A series of recent papers has shown that the Gene Ontology (GO), the most prominent taxonomic resource in these fields, is marked by flaws of certain characteristic types, which flow from a failure to address basic ontological principles. As yet, no methods have been proposed which would allow ontology curators to pinpoint flawed terms or definitions in ontologies in a systematic way. Results: We present (...)
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  3.  17
    Relations in Biomedical Ontologies.Barry Smith, Werner Ceusters, Bert Klagges, Jacob Köhler, Anand Kuma, Jane Lomax, Chris Mungall, , Fabian Neuhaus, Alan Rector & Cornelius Rosse - 2005 - Genome Biology 6 (5):R46.
    To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.
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