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.

information systems continue to grow in size and scope, advances in data management become more and more on the critical path for usability of these systems. This paper reports on the implementation and applicability of an important function - that of calculating the conceptual salience of knowledge or data in a knowledge base or database. Salience is calculated with a method based on Tversky's formulation of salience as composed of two factors: intensity and discriminability. The salience computation has been implemented and tested on a database and is independent of the particular knowledge area.

A traditional paradigm for retrieval from a conceptual knowledge base is to gather up indices or features used to discriminate among or locate items in memory, and then perform a retrieval operation to obtain matching items. These items may then be evaluated for their degree of match against the input. This type of approach to retrieval has some problems. It requires one to look explicitly for items in memory whenever the possibility exists that there might be something of interest there. Also,this approach does not easily tolerate discrepancies or omissions in the input features or indices. In a question-answering system, a user may make incorrect assumptions about the contents of the knowledge base. This makes a tolerant retrieval method even more necessary.An alternative, two-stage model of conceptual information retrieval is proposed.The first stage is a spontaneous retrieval that operates by a simple marker-passing scheme. It is spontaneous because items are retrieved a£ a by-product of the input understanding process. The second stage is a graph matching process that filters or evaluates items retrieved by the first stage. This scheme has been implemented in the SCISOR information retrieval system. It is successful in overcoming problems of retrieval failure due to omitted indices, and also facilitates the construction of appropriate responses to a broader range of inputs.