Robin McEntire, SmithKline Beecham Pharmaceuticals; Peter Karp, SRI International; Neil Abernethy, InGenuity; David Benton, SmithKline Beecham Pharmaceuticals; Gregg Helt, University of California, Berkeley; Matt DeJongh, NetGenics; Robert Kent, Ontologos; Anthony Kosky, GeneLogic; Suzanna Lewis, University of California, Berkeley; Dan Hodnett, NetGenics; Eric Neumann, 3rd Millenium; Frank Olken, Lawrence Berkeley Livermore Laboratory; Dhiraj Pathak, SmithKline Beecham Pharmaceuticals; Peter Tarczy-Hornoch, University of Washington; Luca Toldo, Merck KgaA; and Thodoros Topaloglou, GeneLogic
Ontologies are specifications of the concepts in a given field, and of the relationships among those concepts. The development of ontologies for molecular-biology information and the sharing of those ontologies within the bioinformatics community are central problems in bioinformatics. If the bioinformatics community is to share ontologies effectively, ontologies must be exchanged in a form that uses standardized syntax and semantics. This paper reports on an effort among the authors to evaluate alternative ontology-exchange languages, and to recommend one or more languages for use within the larger bioinformatics community. The study selected a set of candidate languages, and defined a set of capabilities that the ideal ontology-exchange language should satisfy. The study scored the languages according to the degree to which they satisfied each capability. In addition, the authors performed several ontology-exchange experiments with the two languages that received the highest scores: OML and Ontolingua. The result of those experiments, and the main conclusion of this study, was that the frame-based semantic model of Ontolingua is preferable to the conceptual graph model of OML, but that the XML-based syntax of OML is preferable to the Lisp-based syntax of Ontolingua.