Multi-Attribute Decision Making with Weighted Description Logics

E. Acar, Manuel Fink, Christian Meilicke, Camilo Thorne, Heiner Stuckenschmidt

Research output: Contribution to JournalSpecial issue (editing)Academicpeer-review

Abstract

We introduce a decision-theoretic framework based on Description Logics (DLs), which can be used to encode and solve single stage multi-attribute de- cision 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 representa- tion 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.
Original languageEnglish
Pages (from-to)1973 – 1995
JournalIFCoLog Journal of Logic and its Applications
Publication statusPublished - 2017

Fingerprint

Ontology
Decision making
Decision theory
Agglomeration

Cite this

Acar, E., Fink, M., Meilicke, C., Thorne, C., & Stuckenschmidt, H. (2017). Multi-Attribute Decision Making with Weighted Description Logics. IFCoLog Journal of Logic and its Applications, 1973 – 1995.
Acar, E. ; Fink, Manuel ; Meilicke, Christian ; Thorne, Camilo ; Stuckenschmidt, Heiner. / Multi-Attribute Decision Making with Weighted Description Logics. In: IFCoLog Journal of Logic and its Applications. 2017 ; pp. 1973 – 1995.
@article{41f43e7bf780483689e54d1e799a94f2,
title = "Multi-Attribute Decision Making with Weighted Description Logics",
abstract = "We introduce a decision-theoretic framework based on Description Logics (DLs), which can be used to encode and solve single stage multi-attribute de- cision 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 representa- tion 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.",
author = "E. Acar and Manuel Fink and Christian Meilicke and Camilo Thorne and Heiner Stuckenschmidt",
year = "2017",
language = "English",
pages = "1973 – 1995",
journal = "IFCoLog Journal of Logic and its Applications",

}

Acar, E, Fink, M, Meilicke, C, Thorne, C & Stuckenschmidt, H 2017, 'Multi-Attribute Decision Making with Weighted Description Logics' IFCoLog Journal of Logic and its Applications, pp. 1973 – 1995.

Multi-Attribute Decision Making with Weighted Description Logics. / Acar, E.; Fink, Manuel; Meilicke, Christian; Thorne, Camilo; Stuckenschmidt, Heiner.

In: IFCoLog Journal of Logic and its Applications, 2017, p. 1973 – 1995.

Research output: Contribution to JournalSpecial issue (editing)Academicpeer-review

TY - JOUR

T1 - Multi-Attribute Decision Making with Weighted Description Logics

AU - Acar, E.

AU - Fink, Manuel

AU - Meilicke, Christian

AU - Thorne, Camilo

AU - Stuckenschmidt, Heiner

PY - 2017

Y1 - 2017

N2 - We introduce a decision-theoretic framework based on Description Logics (DLs), which can be used to encode and solve single stage multi-attribute de- cision 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 representa- tion 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.

AB - We introduce a decision-theoretic framework based on Description Logics (DLs), which can be used to encode and solve single stage multi-attribute de- cision 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 representa- tion 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.

M3 - Special issue (editing)

SP - 1973

EP - 1995

JO - IFCoLog Journal of Logic and its Applications

JF - IFCoLog Journal of Logic and its Applications

ER -