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Enhancing Magic Sets with an Application to Ontological Reasoning

Published online by Cambridge University Press:  20 September 2019

MARIO ALVIANO Open the ORCID record for MARIO ALVIANO [Opens in a new window] ,
NICOLA LEONE ,
PIERFRANCESCO VELTRI  and
JESSICA ZANGARI Open the ORCID record for JESSICA ZANGARI [Opens in a new window]
MARIO ALVIANO
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mails: alviano@mat.unical.it, leone@mat.unical.it, veltri@mat.unical.it, zangari@mat.unical.it)
NICOLA LEONE
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mails: alviano@mat.unical.it, leone@mat.unical.it, veltri@mat.unical.it, zangari@mat.unical.it)
PIERFRANCESCO VELTRI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mails: alviano@mat.unical.it, leone@mat.unical.it, veltri@mat.unical.it, zangari@mat.unical.it)
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mails: alviano@mat.unical.it, leone@mat.unical.it, veltri@mat.unical.it, zangari@mat.unical.it)
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Abstract

Magic sets are a Datalog to Datalog rewriting technique to optimize query answering. The rewritten program focuses on a portion of the stable model(s) of the input program which is sufficient to answer the given query. However, the rewriting may introduce new recursive definitions, which can involve even negation and aggregations, and may slow down program evaluation. This paper enhances the magic set technique by preventing the creation of (new) recursive definitions in the rewritten program. It turns out that the new version of magic sets is closed for Datalog programs with stratified negation and aggregations, which is very convenient to obtain efficient computation of the stable model of the rewritten program. Moreover, the rewritten program is further optimized by the elimination of subsumed rules and by the efficient handling of the cases where binding propagation is lost. The research was stimulated by a challenge on the exploitation of Datalog/dlv for efficient reasoning on large ontologies. All proposed techniques have been hence implemented in the dlv system, and tested for ontological reasoning, confirming their effectiveness.

Keywords

Datalogquery answeringmagic setsnonmonotonic reasoningaggregations.
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 654 - 670
Copyright
© Cambridge University Press 2019 

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