Abstract
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.
Highlights
It is often necessary to take both a set of formally structured requirements and individual preferences simultaneously into account. This requires an extension of the common knowledge bases, the so-called decision bases, which are initially based on the multi-attribute utility theory (MAUT) [3]
The following sections present our architecture for opinion and consensus mining Opinion Mining Architecture (OMA), classical description logic and two extensions, the weighted description logic and fuzzy description logic
In order to eliminate this problem, the knowledge base is extended by fuzzy description logic and combined with the decision base presented in the following chapters
Summary
It is often necessary to take both a set of formally structured requirements and individual preferences simultaneously into account. This requires an extension of the common knowledge bases, the so-called decision bases, which are initially based on the multi-attribute utility theory (MAUT) [3]. This paper provides a framework to model ambiguity and individual preferences at the same time It combines the fuzzy description logic with the weighted description logic. The demarcation to probabilistic settings is highlighted After combining these two approaches, the fuzzy decision base framework is introduced. We show how this framework can support the decision-making process within the respective architecture
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