Syntactic Propositional Belief Bases Fusion with Removed Sets

Hué, Julien; Papini, Odile; Würbel, Eric

doi:10.1007/978-3-540-75256-1_9

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Hue et al [20], describe an implementation for belief fusion which covers two operators, one of which, Σ µ , we also examine in this paper. They cite preliminary experimental results based on a scheme of generating profiles which is similar to ours.…”

Section: Discussionmentioning

confidence: 99%

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Implementing semantic merging operators using binary decision diagrams

Gorogiannis

Hunter

2008

International Journal of Approximate Reasoning

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There is a well-recognised need in diverse applications for reasoning with multiple, potentially inconsistent sources of information. One approach is to represent each source of information by a set of formulae and then use a merging operator to produce a set of formulae as output. A valuable range of model-based operators have been proposed that conform to interesting and intuitive properties. However, the implementation of such operators has remained unaddressed, partly due to the considerable computational complexity of the proposals. To address this, we propose a methodology for implementing model-based merging operators using the notion of dilation and a type of data structure called a Binary Decision Diagram. We apply this method by implementing four merging operators from the literature and experimentally evaluating their average-case performance. The results indicate that while the complexity is indeed significant, problems of modest size can be treated using commodity hardware and short computation times.

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Section: Discussionmentioning

confidence: 99%

“…Since, to the authors' knowledge, the former does not exist in the field of knowledge merging, we will attempt the latter. The only other experimental assessment of algorithms for merging operators we know of ( [20]) also uses random knowledge bases as a way of evaluating performance.…”

Section: Test-case Generationmentioning

confidence: 99%

Implementing semantic merging operators using binary decision diagrams

Gorogiannis

Hunter

2008

International Journal of Approximate Reasoning

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Assertional Removed Sets Merging of DL-Lite Knowledge Bases

Benferhat

Bouraoui

Papini

et al. 2019

Lecture Notes in Computer Science

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DL-Lite is a tractable family of Description Logics that underlies the OWL-QL profile of the ontology web language, which is specifically tailored for query answering. In this paper, we consider the setting where the queried data are provided by several and potentially conflicting sources. We propose a merging approach, called "Assertional Removed Sets Fusion" (ARSF) for merging DL-Lite assertional bases. This approach stems from the inconsistency minimization principle and consists in determining the minimal subsets of assertions, called assertional removed sets, that need to be dropped from the original assertional bases in order to resolve conflicts between them. We give several merging strategies based on different definitions of minimality criteria, and we characterize the behaviour of these strategies with respect to rational properties. The last part of the paper shows how to use the notion of hitting sets for computing the assertional removed sets, and the merging outcome.

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