Efficient Transitive Closure Reasoning in a Combined Class/Part/Containment Hierarchy

Abstract.

Class hierarchies form the backbone of many implemented knowledge representation and reasoning systems. They are used for inheritance, classification and transitive closure reasoning. Part hierarchies are also important in artificial intelligence. Other hierarchies, e.g. containment hierarchies, have received less attention in artificial intelligence. This paper presents an architecture and an implementation of a hierarchy reasoner that integrates a class hierarchy, a part hierarchy, and a containment hierarchy into one structure. In order to make an implemented reasoner useful, it needs to operate at least at speeds comparable to human reasoning. As real-world hierarchies are always large, special techniques need to be used to achieve this. We have developed a set of parallel algorithms and a data representation called maximally reduced tree cover for that purpose. The maximally reduced tree cover is an improvement of a materialized transitive closure representation which has appeared in the literature. Our experiments with a medical vocabulary show that transitive closure reasoning for combined class/part/containment hierarchies in near constant time is possible for a fixed hardware configuration.