Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.

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 novel 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. 1

During the last years, a number of formal specification languages for knowledge-based systems has been developed. Characteristic for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide means to specify a complex and large amount of knowledge and they have to provide means to specify the dynamic reasoning behaviour of a knowledge-based system. This paper focuses on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behaviour in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modelling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic), and the generic specification framework of Abstract State Machines. Keywords...

MoMo is a language for operationalizing KADS-type models of expertise. It is embedded in Common Lisp such that systems described in MoMo can be developed and run in the Lisp environment. MoMo relies on the Common Lisp Object System (CLOS) and its integration into the Common Lisp type system to describe problem solving methods in a domain-independent way. To connect such a problem-solving method to a particular domain,the abstract types and their generic operations have to be mapped to concrete ones. The semantics of inference structures was to some extent influenced by ideas from colored Petri nets [ Jensen, 1987 ] . This report contains a larger example, the syntax, its interpretation, and the design rationale for MoMo. Contents 1 Background 3 2 A short glimpse on MoMo 3 3 An example: Diagnosis of home stereo equipment 5 3.1 The domain knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Knowledge about inferences . . . . . . . . . . . . . . . . . . . . . . ...

To close the gap between knowledge level and symbol level, we present MODEL-K, a language that allows both, to specify KADS conceptual models and to extend them to operational systems. This leads to systems that correctly reflect their underlying conceptual model by retaining structural correspondence between model and implementation. The resulting systems are more perspicuous, easier to communicate and to explain to the user, and better to maintain. Since both, modelling and operationalizing may be interleaved, MODEL-K supports the integration of prototyping in model-based methodologies like KADS as early as possible and on the highest level possible. We illustrate the MODEL-K language with a conceptual model for assignment tasks. We demonstrate how the model, originally developed for the domain of o#ce room allocation, can be reused for an application assigning gates to airplanes 1 . 1 Motivation In the development of knowledge-based systems there is a recognizable sh...