Abstract
We present an analysis of some considerations involved in expressing the Gene Ontology (GO) as a machine-processible ontology, reflecting principles of formal ontology. GO is a controlled vocabulary that is intended to facilitate communication between biologists by standardizing usage of terms in database annotations. Making such controlled vocabularies maximally useful in support of bioinformatics applications requires explicating in machine-processible form the implicit background information that enables human users to interpret the meaning of the vocabulary terms. In the case of GO, this process would involve rendering the meanings of GO into a formal (logical) language with the help of domain experts, and adding additional information required to support the chosen formalization. A controlled vocabulary augmented in these ways is commonly called an ontology. In this paper, we make a modest exploration to determine the ontological requirements for this extended version of GO. Using the terms within the three GO hierarchies (molecular function, biological process and cellular component), we investigate the facility with which GO concepts can be ontologized, using available tools from the philosophical and ontological engineering literature.
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Selected References
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- Gene Ontology Consortium Creating the gene ontology resource: design and implementation. Genome Res. 2001 Aug;11(8):1425–1433. doi: 10.1101/gr.180801. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Karp P. D. Pathway databases: a case study in computational symbolic theories. Science. 2001 Sep 14;293(5537):2040–2044. doi: 10.1126/science.1064621. [DOI] [PubMed] [Google Scholar]
- Rison S. C., Hodgman T. C., Thornton J. M. Comparison of functional annotation schemes for genomes. Funct Integr Genomics. 2000 May;1(1):56–69. doi: 10.1007/s101420000005. [DOI] [PubMed] [Google Scholar]
- Schulze-Kremer S. Ontologies for molecular biology. Pac Symp Biocomput. 1998:695–706. [PubMed] [Google Scholar]
- Stevens R., Goble C. A., Bechhofer S. Ontology-based knowledge representation for bioinformatics. Brief Bioinform. 2000 Nov;1(4):398–414. doi: 10.1093/bib/1.4.398. [DOI] [PubMed] [Google Scholar]