Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making
International Journal of Intelligent Information Systems
Volume 8, Issue 3, June 2019, Pages: 58-64
Received: May 15, 2019; Accepted: Jun. 10, 2019; Published: Jul. 30, 2019
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Nadine Mueller, Baden-Wuerttemberg Cooperative State University, Loerrach, Germany
Klemens Schnattinger, Baden-Wuerttemberg Cooperative State University, Loerrach, Germany
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A well-known family of logics for managing structured knowledge is Description logics (DLs). They form the basis for a wide variety of ontology languages. Experience with the use of DLs in applications has, however, shown that their capabilities are insufficient for some domains. In particular, the decision-making process requires the assessment of two, possibly contradictory, influences on decision factors. First, there are items belonging to certain classes or fulfillling certain roles within complex logical constructs, but these memberships are to some extent vague. Secondly, individual preferences may change depending on the person who controls the decision-making process. Therefore, the challenge in building a decision making framework is to appropriately account for these variable influences by depicting and incorporating both aspects. This paper shows how these influences can be best modeled using a combination of fuzzy description logic and weighted description logic. Fuzzy logic is used to represent vagueness and ambiguity in ontologies, weighted description logic expresses individual preferences. In addition, the paper shows how to engineer an appropriate architecture for the suggested model.
Ontology Learning, Weighted Description Logic, Fuzzy Logic, Decision Making
To cite this article
Nadine Mueller, Klemens Schnattinger, Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making, International Journal of Intelligent Information Systems. Vol. 8, No. 3, 2019, pp. 58-64. doi: 10.11648/j.ijiis.20190803.11
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