We introduce a decision-theoretic framework based on Description Logics
(DLs), which can be used to encode and solve single stage multi-attribute decision problems. In particular, we consider the background knowledge as a DL
knowledge base where each attribute is represented by a concept, weighted by
a utility value which is asserted by the user. This yields a compact representation of preferences over attributes. Moreover, we represent choices as knowledge
base individuals, and induce a ranking via the aggregation of attributes that
they satisfy. We discuss the benefits of the approach from a decision theory
point of view. Furthermore, we introduce an implementation of the framework
as a Protégé plugin called uDecide. The plugin takes as input an ontology as
background knowledge, and returns the choices consistent with the user’s (the
knowledge base) preferences. We describe a use case with data from DBpedia.
We also provide empirical results for its performance in the size of the ontology
using the reasoner Konclude.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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