Proceedings of the Annual Meeting of the Cognitive Science Society

Since the advent of computers, information systems have grown in terms of the quantity of knowledge they deal with. Advances in data management are on the critical path for usability of these systems. This paper reports on a novel approach to an important problem; that of calculating the conceptual breadth of knowledge or data in a knowledge base or database. Breadth determination is useful in that ascribing meta-level knowledge of conceptual content can help to predict, for example, the validity of the closed-world assumption or the likelihood of encountering new information of a particular type. The point at which a system determines it is likely to have breadth in a given knowledge area may also serve as the trigger point for calculations that assume relatively complete knowledge in that area. The accurate determination of when a system has complete knowledge in an area is crucial for the accurate application of many AI algorithms.