Multi-attribute decision making with weighted description logics


Acar, Erman ; Fink, Manuel ; Meilicke, Christian ; Thorne, Camilo ; Stuckenschmidt, Heiner


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URL: https://madoc.bib.uni-mannheim.de/42340
Additional URL: http://www.collegepublications.co.uk/downloads/ifc...
URN: urn:nbn:de:bsz:180-madoc-423409
Document Type: Article
Year of publication: 2017
The title of a journal, publication series: IfCoLog Journal of Logics and its Applications
Volume: 4
Issue number: 7
Page range: 1973-1996
Place of publication: London
Publishing house: College Publications
ISBN: 978-1-84890-252-7
ISSN: 2055-3706 , 2055-3714
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): Decision Making , Description Logics , Protege
Abstract: 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.




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