Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21878
Title: On the satisfiability problem for SPARQL patterns
Authors: ZHANG, Xiaowang 
VAN DEN BUSSCHE, Jan 
Picalausa, François
Issue Date: 2016
Source: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 56, p. 403-428
Abstract: The satisfiability problem for SPARQL 1.0 patterns is undecidable in general, since the relational algebra can be emulated using such patterns. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound- constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is decidable by syntactic checks on bound variables and on the use of literals. Although the problem is shown to be NP-complete, it is experimentally shown that the checks can be implemented efficiently in practice. The paper also points out that satisfiability for the so-called ‘well-designed’ patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.
Notes: Zhang, XW (reprint author), Tianjin Univ, Sch Comp Sci & Technol, Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China. xiaowangzhang@tju.edu.cn; jan.vandenbussche@uhasselt.be; fpicalausa@gmail.com
Document URI: http://hdl.handle.net/1942/21878
Link to publication/dataset: http://www.jair.org/media/5028/live-5028-9408-jair.pdf
ISSN: 1076-9757
e-ISSN: 1943-5037
DOI: 10.1613/jair.5028
ISI #: 000380245700001
Rights: © 2016 AI Access Foundation. All rights reserved
Category: A1
Type: Journal Contribution
Validations: ecoom 2017
Appears in Collections:Research publications

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