Subsumption-Based Matching: Bringing Semantics to Goals

Yolanda Gil and Pedro A. González

Matching a posted goal against a library of rules is a task common in many AI systems. There are matching algorithms that can perform this process with reasonable efficiency. However, they are based on the syntactic features of goals instead of their semantic meaning. Representing the semantic meaning of goals can support additional features in matching algorithms and further reasoning about goals. This paper presents a matching algorithm that uses a semantic goal representation based on description logic. Each goal is translated into a description, and matching relies on the reasoning performed by a classifier to determine which rules unify with the posted goal. An extension of the matcher relaxes the posted goal to retrieve rules that almost match the goal based on the subsumption hierarchies of the domain ontology and the goals. The matching algorithm has been implemented using LOOM as the underlying description logic, and is used routinely as a component of the EXPECT problem-solving architecture. We show how it classifies and retrieves the methods in EXPECT’s domains, and how the relaxed matching mode can be used to support knowledge acquisition.


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