Ontobroker applies Artificial Intelligence techniques to improve access to heterogeneous, distributed and semi-structured information sources as they are presented in the World Wide Web or organization-wide intranets. It relies on the use of ontologies to annotate web pages, formulate queries and derive answers. In this paper we will briefly sketch Ontobroker. Then we will discuss its main shortcomings, i.e. we will share the lessons we learned from our exercise. We will also show how On2broker overcomes these limitations. Most important is the separation of the query and inference engines and the integration of new web standards like XML and RDF.

A central research topic in the area of knowledge engineering is the reuse of problem-solving methods for developing knowledge based systems. For being able to reuse a problem-solving method it is important to know under which restrictions a problem-solving method is appropriate to solve a given problem. This paper describes the problem-solving method propose-and-revise as well as the way this problem-solving method searches in its problem space for a solution. A quantitative analysis of the efficiency of this search process is given. Additionally, task and domain specific properties and restrictions and their influence on the efficiency of the search process are considered. For these purposes an instance of the problem-solving method is transformed to a corresponding instance of a Stanford Research Institute Problem Solver (STRIPS) planning system. Then the problem-solving method is considered as an additional control strategy for such a planning system. By this way the various insights and analysis results which are available in the area of planning systems may be exploited for the analysis of problem-solving methods. ©1999 John Wiley & Sons, Inc.

The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge based system at the conceptual level (a so called model of expertise) with a description at a formal and executable level. Thus, KARL allows the precise and unique specification of the functionality of a knowledge based system independent of any implementation details. A KARL model of expertise contains the description of domain knowledge, inference knowledge, and procedural control knowledge. For capturing these different types of knowledge, KARL provides corresponding modeling primitives based on Frame Logic and Dynamic Logic. A declarative semantics for a complete KARL model of expertise is given by a combination of these two types of logic. In addition, an operational definition of this semantics, which relies on a fixpoint approach, is given. This operational semantics defines the basis for the implementation of the KARL interpreter, which includes appropriate algorithms for efficiently executing KARL specifications. This enables the evaluation of KARL specifications by means of testing.