To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.

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.

For many years, researchers in knowledge-based systems have worked toward the development of sharable and reusable problem-solving methods and knowledge bases. The aim is to reduce development and maintenance costs, and to build flexible, component-based systems that can be adapted to changing environments. Unfortunately, despite conceptual progress in building and connecting components, there has been little success with large-scale, cross-platform implementations of sharable component libraries.

A central purpose of knowledge acquisition technology is to assist with the formulation of domain models that underlie knowledge systems. In this article we examine the model formulation process itself as a problem-solving task. Drawing from AI research in qualitative reasoning about physical systems, we characterize the model formulation task in terms of the inputs, the reasoning subtasks, and the knowledge needed to perform the problem solving. We describe the elements of a high-level representation of modeling knowledge, and techniques for providing intelligent assistance to the model builder. Applying the results from engineering modeling to knowledge acquisition in general, we identify properties of the representation that facilitate the construction of knowledge systems from libraries of reusable models. Contente 1. The model formulation task .................................................................................. 2 1.1 The Model Formulation Problem for Engineering .....