A Possibilistic Logic Approach to Conditional Preference Queries

Dubois, Didier; Prade, Henri; Touazi, Fayçal

doi:10.1007/978-3-642-40769-7_33

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The vector representing the satisfaction of each preference is recorded as a vector and assigned to each outcome. Based on these vectors, the end outcome is ranked in the same spirit of [18].…”

Section: Possibilistic Sparql Conceptmentioning

confidence: 99%

Handling qualitative conditional preferencequeries in SPARQL: Possibilistic logic approach

Touazi

Boustil

2023

Preprint

View full text Add to dashboard Cite

Because of the rise in data volume of knowledge bases that are being published as a result of Open Data initiatives, new approaches are required to assist users in locating the items that most closely matches their preference criteria. In many approaches, the user is called to supply quantitative weights that may not be known in advance to manage the ranking of results.Contrary to the quantitative technique, preference criteria are sometimes more intuitive and can be conveyed more readily under the qualitative approach. We are interested in this paper to the problem of evaluating SPARQL qualitative preference queries over user preferences in SPARQL. Many approaches address this problem based on different frameworks as CP-net, skyline, fuzzy set and top-k. This article outlines a novel approach for dealing with SPARQL preference queries, where preferences are represented through symbolic weights using the possibilistic logic framework. It is possible to manage symbolic weights without using numerical values where a partial ordering is used instead. This approach is compared to numerous other approaches, including those based on skylines, fuzzy sets, and CP-nets.

show abstract

Section: Possibilistic Sparql Conceptmentioning

confidence: 99%

Handling qualitative conditional preferencequeries in SPARQL: Possibilistic logic approach

Touazi

Boustil

2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Possibilistic Sparql Conceptmentioning

confidence: 99%

Handling qualitative conditional preference queries in SPARQL: possibilistic logic approach

Touazi,

Boustil

2023

IJWIS

View full text Add to dashboard Cite

Purpose The purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL. Design/methodology/approach The paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks. Findings The paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings. Originality/value The paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

show abstract

“…Possibilistic preference networks ( π -pref nets) belong to the family of methods for the modeling of preference and decision that stem from the fuzzy set and possibility theory literature, variants of which are fuzzy Markovian decision processes studied very early by Kacprzyk [2] . Just like CP-nets may be used for flexible querying [3] , it seems that π -pref nets might also serve this purpose (following ideas in [4] ), a topic that has been also much investigated by Kacprzyk (e.g., [5] ).…”

Section: Introductionmentioning

confidence: 99%

Possibilistic preference networks

Amor¹,

Dubois

Gouider³

et al. 2018

Information Sciences

Self Cite

View full text Add to dashboard Cite

This paper studies the use of product-based possibilistic networks for representing preferences in multidimensional decision problems. This approach uses symbolic possibility weights and defines a partial preference order among solutions to a set of conditional preference statements on the domains of discrete decision variables. In the case of Boolean decision variables, this partial ordering is shown to be consistent with the preference ordering induced by the ceteris paribus assumption adopted in CP-nets. Namely, by completng the possibilistic net ordering with suitable constraints between products of symbolic weights, all CP-net preferences can be recovered. Computing procedures for comparing solutions are provided. The flexibility and representational power of the approach is stressed.R Dedication : This paper is dedicated to Janusz Kacprzyk on the occasion of his jubilee. The second and the fourth author, who have a long and fruitful research companionship with him, are glad to offer him this new piece of work.

show abstract

A Possibilistic Logic Approach to Conditional Preference Queries

Cited by 4 publications

References 22 publications

Handling qualitative conditional preferencequeries in SPARQL: Possibilistic logic approach

Handling qualitative conditional preferencequeries in SPARQL: Possibilistic logic approach

Handling qualitative conditional preference queries in SPARQL: possibilistic logic approach

Possibilistic preference networks

Contact Info

Product

Resources

About