著者

増田 壮志 古崎 晃司

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.32, no.2, pp.E-G71_1-10, 2017-03-01 (Released:2017-03-01)

参考文献数

17

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Ontologies are currently constructed in various fields, such as life sciences, medical information, and sustainability science. These ontologies are used as knowledge bases and knowledge models for application systems. However, it is difficult to build high quality ontologies due to the necessity of having both knowledge of ontology and expertise in the target domain. Therefore, ontology construction and maintenance costs considerable time and effort. To reduce such costs, we developed an ontology refinement support method. To test and confirm this refinement method, we focused on the guideline for building well-organized ontologies that“ Each subclass of a super class is distinguished by the values of exactly one attribute of the super class. ”Then, we discovered that there is a similarity between is-a hierarchies when an ontology is built following this guideline and made the hypothesis that, if subclasses are not classified by one attribute, there are consistency errors in the ontology that can be automatically fixed by a comparison method of is-a hierarchies. To test this hypothesis, we conducted an experiment to evaluate the refinement method. We asked nine experienced evaluators to build the ontology and used 150 refinement proposals. As a result, we found that at most 90% of the refinement candidates could be further refined and that at most approximately 50% of the refinement proposals are appropriate to apply to ontologies.