Research in Data Management and Artificial Intelligence has led to systems capable of efficiently searching vast quantities of data and to systems that use encoded expertise to solve problems in an "intelligent" fashion. This paper describes recent progress in developing systems that incorporate application specific expertise and apply that expertise in reasoning with and about data stored within a conventional data management system (DMS). We call such systems Knowledge Management Systems and we discuss the features and capabilities of a prototype Knowledge Manager: KM-1. KM-1 employs a logic-based "reasoning engine" (deductive processor) to derive implicit information from the explicit data stored within a "searching engine" (a relational data management system). Both rule based expertise and user queries are expressed in an Englishlike canonical form of first order predicate logic. Data access plans, derived from queries, and evidence chains that explain answers are presented to the user in an easy to interpret graphical form. The paper concludes with a brief discussion of current research in Logic Based Systems, AI, and database technology that is relevant to the development of future knowledge management systems.

This paper presents an overview of research in progress in which the principal aim is the achievement of more natural and expressive modes of on-line communication with complexly structured data bases. A natural-language compiler has been constructed that accepts sentences in a user-extendable English subset, produces surface and deep-structure syntactic analyses, and uses a network of concepts to construct semantic interpretations formalized as computable procedures. The procedures are evaluated by a data management system that updates, modifies, and searches data bases that can be formalized as finite models of states of affairs. The system has been designed and programmed to handle large vocabularies and large collections of facts efficiently. Plans for extending the research vehicle to interface with a deductive inference component and a voice input-output effort are briefly described.