人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
ISSN-L : 1346-0714
原著論文
オントロジー構築におけるPart-of 記述とその実践-ロール理論に基づく部分構造表現モデル
古崎 晃司溝口 理一郎
著者情報
ジャーナル フリー

2019 年 34 巻 1 号 p. C-I52_1-13

詳細
抄録

Ontological considerations about part-of relations have been extensively investigated because they are basic and important relationships for ontology building. Although there are various discussions on kinds of part-of and their ontological characteristics, there remains some room for discussing a couple of fundamental issues such as “What is a part?” and “When is a part-of relation composed?” This paper discusses ontology patterns of descriptions of part-of relationships on the basis of ontological theories in order to provide practitioners with useful guidelines for descriptions of part-of structurers. This paper focuses on ontology patterns which capture commonality and special characteristics of parts so that complicated structures of physical objects are described appropriately. We discuss four problems related to descriptions of parts. 1) interdependence between the whole and its parts, 2) kinds of parts such as components, portions and materials, 3) multiple inheritance according to substance and properties of parts, 4) the commonality and specificity of parts. To cope with these problems, this paper introduces a part representation model based on ontological theory of roles. The main idea of the part representation model is to distinguish between a part dependent on its whole and the context-independent properties of the part. The former is defined as the role-holder which plays roles and the latter is defined as the player of the role. The role defines properties of the part which is dependent on its whole. These three kinds of definitions enable to describe differences of various properties of parts according to their context dependence. We show how this model is used to describe various parts through practical examples of the anatomical structure of human body developed in the medical ontology project in Japan.

著者関連情報
© 人工知能学会 2019
前の記事 次の記事
feedback
Top