Enrichment of {OWL} Ontologies: A Method for Defining Axioms from Labels

M {Quesada-Mart{\'\i}nez}, J T {Fernández-Breis}, R Stevens, L Moss (Editor), D Sleeman (Editor)

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    An increasing interest in biomedical ontologies has provoked the development of a significant number of ontologies. Many such ontolo- gies have not been created by computer scientists or ontology engineers, but by domain experts. Some of these ontologies are rich in implicit knowledge, which is frequently within the labels of the classes. However, these ontologies are really just plain taxonomies and controlled vocabu- laries with little degree of axiomatization, being difficult to use for ma- chine processing. In previous work we proposed a process for enriching ontologies which included the analysis of such labels, the identification of lexical patterns and the design of knowledge patterns from them. How- ever, this process relied on manual intervention and it was apply in just one ontology. In this paper we refine the method and develop an auto- matic tool to guide the process of including axiomatization in ontologies through the automated study of labels. Additionally, the identification of the lexical patterns is support with information about external entities that could be re-used. Finally, such knowledge patterns are coded and executed using the Ontology Pre-Processor Language (OPPL), and our tool permits their definition without advanced knowledge of OPPL.
    Original languageEnglish
    Title of host publicationProceedings of the First International Workshop on Capturing and Refining Knowledge in the Medical Domain (K-MED'12) - Co-located with The 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2012)
    EditorsL Moss, D Sleeman
    Pages1-10
    Number of pages10
    Publication statusPublished - 2012

    Keywords

    • ontology engineering
    • ontology enrichment
    • OPPL
    • OWL

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