Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making

Abstract

Description logics (DLs) are a well-known family of logics for managing structured knowledge. They are the basis for widely used ontology languages. Experience with the use of DLs in applications has shown that their capabilities are not sufficient for every domain. In particular, the decision-making process requires the assessment of two different, sometimes even contradictory influences on decision factors. On the one hand, there are items that belong to certain classes or fulfill certain roles within logically complex constructs, but these memberships are to some extent vague. On the other hand, individual preferences can change depending on the person who drives the decision-making process. Therefore, the challenge when building a framework of decision making, is to take these influencing variables adequately into account by depicting and incorporating both aspects. The paper shows how these requirements can best been modelled by combining fuzzy description logic and weighted description logic. Whereas the first meets the requirement to represent vagueness and ambiguity in ontologies, the second is able to express individual preferences. In addition, the paper shows how to engineer an appropriate and suitable architecture for this purpose.