The main goal of this paper is to evaluate knowledge base schemas, modeled as a set of entity types, each such type being associated with a set of properties, according to their focus. We intuitively model the notion of focus as ''the state or quality of being relevant in storing and retrieving information''. This definition of focus is adapted from the notion of ''categorization purpose'', as first defined in cognitive psychology, thus giving us a high level of understandability on the side of users. In turn, this notion is formalized based on a set of knowledge metrics that, for any given focus, rank knowledge base schemas according to their quality. We apply the proposed methodology to more than 200 state-of-the-art knowledge base schemas. The experimental results show the utility of our approach

Problem-solving techniques such as modeling, simulation, optimization, and network analysis have been used extensively to help agricultural scientists and practitioners understand and control biological systems. By their nature, most of these systems are difficult to quantitatively define. Many of the models and simulations that have been developed lack a user interface which enables people other than the developer to use them. As a result, several scientists are integrating knowledgebased-system (KBS) technology with conventional problem-solving techniques to increase the robustness and usability of their systems. To investigate the similarities and differences of leading scientists' approaches, a pioneer workshop, supported by the American Association for Artificial Intelligence (AAAI) and the Knowledge Systems Area of the American Society of Agricultural Engineers, was held in San Antonio, Texas, on 10-12 August 1988.

The Workshop on the Validation and Verification of Knowledge-Based Systems gathers researchers from government, industry, and academia to present the most recent information about this important development aspect of knowledge-based systems (KBSs). The 1995 workshop focused on nontraditional KBSs that are developed using more than just the simple rule-based paradigm. This new focus showed how researchers are adjusting to the shift in KBS technology from stand-alone rulebased expert systems to embedded systems that use object-oriented technology, uncertainty, and nonmonotonic reasoning. In "Specification Refinement of Object-Oriented KBSs," A. Vermesan (Foundation for Research in Economics and Business Administration, Norway) looks at KBSs that perform reasoning in a framework of structured objects. Her approach is to verify that as details are added to the specification of a KBS, these additions are consistent with the initial abstract specification.

The Workshop on the Validation and Verification of Knowledge-Based Systems gathers researchers from government, industry, and academia to present the most recent information about this important development aspect of knowledge-based systems (KBSs). The 1995 workshop focused on nontraditional KBSs that are developed using more than just the simple rule-based paradigm. This new focus showed how researchers are adjusting to the shift in KBS technology from stand-alone rule-based expert systems to embedded systems that use object-oriented technology, uncertainty, and nonmonotonic reasoning.

The first session aimed to set the component being tested. The stage for the day's discussion by focusing variation in all three of these contexts on the issues surrounding the will lead to different types of and Verification of Knowledge-use of formal specification techniques The first paper, by Formal Specifications to Design Intelligence (AAAI-94) in Seattle, Lance Miller of SAIC, was entitled Verifiable Hybrid KBS" by Rose Gamble, Washington, marked the seventh This paper provided a with its specification, and (2) the The 1994 workshop was significant basis for the comparison of validation refinement of formal specifications in that there was a definitive move in and verification techniques to for their implementation. O'Leary, from the lows the possibility of constraining techniques for validating certain University of Southern California, the experts' choices to ensure that properties of KBSs. A paper by presented a paper on the relationship any new knowledge added is valid Alun Preece, Cliff Gossner, and T. between errors and size in KBSs. This and that the knowledge base structure Radhakrishnan (all from the University paper is among the first to address ensures the knowledge is of Aberdeen, Scotland) considered this important issue.

Another issue concerned The survey of 80 KBS developers in Knowledge-Based Systems, Marc Ayel structural validation of KBS, given financial domains by Daniel O'Leary and Jean-Pierre Laurent, eds., John that the architecture of these systems opens the collection and raises some Wiley and Sons, Chichester, England, (having separate knowledge base and interesting points. VVT is revealed to 1991, 219 pp., $49.95, ISBN 0-471- inference engine components) was be a significant concern, with developers 93018-0 (paper). Testing his volume contains a selection First European Workshop on studies were launched to determine with real and contrived test cases was Verification, Validation, and Test of the applicability of software engineering found to account for about half the Knowledge-Based Systems, held evaluation techniques to overall VVT effort on average, with during the 1990 European Conference KBSs and to develop new techniques direct inspection of the knowledge on Artificial Intelligence (ECAI specific to KBSs. Several such studies base accounting for another 30 percent 90) in Stockholm, Sweden. In reviewing were initiated by organizations that in the survey.

Problem-solving techniques such as modeling, simulation, optimization, and network analysis have been used extensively to help agricultural scientists and practitioners understand and control biological systems. By their nature, most of these systems are difficult to quantitatively define. Many of the models and simulations that have been developed lack a user interface which enables people other than the developer to use them. As a result, several scientists are integrating knowledge-based- system (KBS) technology with conventional problem-solving techniques to increase the robustness and usability of their systems. To investigate the similarities and differences of leading scientists' approaches, a pioneer workshop, supported by the Association for the Advancement of Artificial Intelligence (AAAI) and the Knowledge Systems Area of the American Society of Agricultural Engineers, was held in San Antonio, Texas, on 10-12 August 1988. Part of the AAAI Applied Workshop Series, the meeting was intended to bring together researchers and practitioners active in applying AI concepts to agricultural problems.

A number of tools that support the development, execution, and maintenance of knowledge-based systems are marketed commercially. Many of these tools, however, are designed for applications that can be executed on personal computers and are not suitable for building large knowledge-based systems. The market for knowledge engineering tools designed for applications that require the computational power of a Lisp machine or an engineering workstation is dominated by a few vendors. This article is an assessment of the current state of tools used to build large knowledge-based systems. This assessment is based on the collective strengths and weaknesses of several tools that have been evaluated. In addition, an estimate is made of the features that will be required in the next generation of tools.