research-article

Combining RDF and SPARQL with CP-theories to reason about preferences in a Linked Data setting

Editor: Axel Polleres Authors: Vito Walter Anelli, Renato De Leone, Tommaso Di Noia, Thomas Lukasiewicz, Jessica RosatiAuthors Info & Claims

Semantic Web, Volume 11, Issue 3

Pages 391 - 419

https://doi.org/10.3233/SW-180339

Published: 01 January 2020 Publication History

Abstract

Preference representation and reasoning play a central role in supporting users with complex and multi-factorial decision processes. In fact, user tastes can be used to filter information and data in a personalized way, thus maximizing their expected utility. Over the years, many frameworks and languages have been proposed to deal with user preferences. Among them, one of the most prominent formalism to represent and reason with (qualitative) conditional preferences (CPs) are conditional preference theories (CP-theories). In this paper, we show how to combine them with Semantic Web technologies in order to encode in a standard SPARQL 1.1 query the semantics of a set of CP statements representing user preferences by means of RDF triples that refer to a “preference” OWL ontology. In particular, here we focus on context-uniform conditional (cuc) acyclic CP-theories [Artif. Intell. 175 2011, 1053–1091]. The framework that we propose allows a standard SPARQL client to query Linked Data datasets, and to order the results of such queries relative to a set of user preferences.

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Cited By

Ferrara AAnelli VMancino ADi Noia TDi Sciascio E(2023)KGFlex: Efficient Recommendation with Sparse Feature Factorization and Knowledge GraphsACM Transactions on Recommender Systems10.1145/35889011:4(1-30)Online publication date: 3-Apr-2023
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Combining RDF and SPARQL with CP-theories to reason about preferences in a Linked Data setting

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cover image Semantic Web

Semantic Web Volume 11, Issue 3

2020

174 pages

ISSN:1570-0844

EISSN:2210-4968

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© 2020 – IOS Press and the authors. All rights reserved.

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IOS Press

Netherlands

Publication History

Published: 01 January 2020

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Cited By

Ferrara AAnelli VMancino ADi Noia TDi Sciascio E(2023)KGFlex: Efficient Recommendation with Sparse Feature Factorization and Knowledge GraphsACM Transactions on Recommender Systems10.1145/35889011:4(1-30)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3588901
Anelli VBasile Pde Melo GDonini FFerrara AMusto CNarducci FRagone AZanker M(2022)Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Proceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547412(663-666)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547412
Anelli VDi Noia TDi Sciascio EFerrara AMancino A(2021)Sparse Feature Factorization for Recommender Systems with Knowledge GraphsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474243(154-165)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3474243
Anelli VBasile PDi Noia TDonini FMusto CNarducci FZanker M(2021)Third Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Proceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3470933(806-809)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3470933

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