Specifying and Reasoning with Underspecified Knowledge Bases Using Answer Set Programming

A large and complex knowledge base that models some aspect of the real world can rarely be fully specified. Two examples of such underspecification are that (i) some of the cardinality constraints are omitted; (ii) some properties of all individual instances of a class are specialized across a class hierarchy, but specific references to which particular values are specialized are omitted. Such knowledge bases are of great practical interest as they are the basis of an empirically tested knowledge acquisition system that has been used to construct a knowledge base from a significant portion of a biology textbook. In this paper, we formalize an underspecified knowledge base using answer set programming, and give a set of rules called UMAP that support inheritance reasoning in such a knowledge base.