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.

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Statistical disclosure control (SDC) and artificial intelligence (AI) use similar tools for di#erent purposes. This workdescribx the common elements ofb1q areas to increase their synergy.

Abstract. The call for a “focus on content ” in ontology research by Nicola Guarino and Mark Musen in their launching statement of the journal Applied Ontology has quite some implications and ramifications. We reflectively discuss ontology engineering as a scientific discipline, and we put this into the wider perspective of debates in other fields, including the methodology of social and natural sciences, and of Information Systems and design science research. We outline how ontologies provide us with a (new) scientific method for theory formation. This positioning allows for stronger concepts and techniques for theoretical, empirical and practical validation that in our view are now needed in the field. A prerequisite for this is an emphasis on ontology as a (domain) content oriented concept, rather than as primarily a computer representation notion. Taking application domain theories and the associated content reference of ontologies really seriously as first-class citizens will actually increase the contribution of ontology engineering to the development of scientific method in general. Next, ontologies should develop from the current static representations of relatively stable domain content into actionable theories-in-use, and a possible way forward is to build in capabilities for self-organization of ontologies as service-oriented knowledge utilities (SOKUs) that can be delivered over the Web.

The increased use of intelligent decision support systems has created a demand for efficient acquisition, implementation and maintenance of the knowledge required by such systems. The field of knowledge level modelling has developed as a means to this end. This has led to the construction of methodologies for KBS development that facilitate a generic approach to knowledge acquisition. Such generic approaches have achieved great success when applied to various domains, yet have thus far largely neglected the generic areas of planning, scheduling and resource allocation. In this paper we outline the development of such a generic approach within the domain of planning for Search and Rescue. Our generic approach makes a distinction between domain derived knowledge level models and those derived from systems. We describe how the combination of these two types of model can achieve definite benefits within the course of KBS development. Acknowledgements: Thanks to RCC Edinburgh for their co-...

We outline a rigorous approach that models how companies can electronically offer packages of independent services (service bundles). Its objective is to support prospective Website visitors in defining and buying service bundles that fit their specific needs and demands. The various services in the bundle may be offered by different suppliers. To enable this scenario, it is necessary that software can reason about customer needs and available service offerings. Our approach for tackling this issue is based on recent advances in computer and information science, where information about a domain at hand is conceptualized and formalized using ontologies and subsequently represented in machine-interpretable form. The substantive part from our ontology derives from broadly accepted service management and marketing concepts from business studies literature. In earlier work, we concentrated on the service bundling process itself. In the present chapter, we discuss how to ensure that the created bundles indeed meet customer demands. Experience of Norwegian energy utilities shows that severe financial losses can be caused when companies offer service bundles without a solid foundation for the bundle-creation process and without an in-depth understanding of customer needs and demands. We use a running case example from the Norwegian energy sector to demonstrate how we put theory into practice.