research-article

Reformulation-based query answering in RDF: alternatives and performance

Editors: Chen Li, Volker Markl Authors:

Damian Bursztyn,

François Goasdoué,

Ioana ManolescuAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 8, Issue 12

Pages 1888 - 1891

https://doi.org/10.14778/2824032.2824093

Published: 01 August 2015 Publication History

Abstract

Answering queries over Semantic Web data, i.e., RDF graphs, must account for both explicit data and implicit data, entailed by the explicit data and the semantic constraints holding on them. Two main query answering techniques have been devised, namely Saturation-based (Sat) which precomputes and adds to the graph all implicit information, and Reformulation-based (Ref) which reformulates the query based on the graph constraints, so that evaluating the reformulated query directly against the explicit data (i.e., without considering the constraints) produces the query answer.

While Sat is well known, Ref has received less attention so far. In particular, reformulated queries often perform poorly if the query is complex. Our demonstration showcases a large set of Ref techniques, including but not limited to one we proposed recently. The audience will be able to 1: test them against different datasets, constraints and queries, as well as different well-established systems, 2: analyze and understand the performance challenges they raise, and 3: alter the scenarios to visualize the impact on performance. In particular, we show how a cost-based Ref approach allows avoiding reformulation performance pitfalls.

References

[1]

S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, 1995.

[2]

M. Arenas, C. Gutierrez, and J. Pérez. Foundations of rdf databases. In Reasoning Web, 2009.

[3]

F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, and P. F. Patel-Schneider, editors. The Description Logic Handbook: Theory, Implem., and Applications, 2003.

[4]

M. A. Bornea, J. Dolby, A. Kementsietsidis, K. Srinivas, P. Dantressangle, O. Udrea, and B. Bhattacharjee. Building an efficient RDF store over a relational database. In SIGMOD, 2013.

[5]

D. Bursztyn, F. Goasdoué, and I. Manolescu. Optimizing reformulation-based query answering in RDF. In EDBT, 2015.

[6]

D. Bursztyn, F. Goasdoué, I. Manolescu, and A. Roatis. Reasoning on web data: Algorithms and performance. In ICDE, 2015.

[7]

D. Calvanese, G. Giacomo, D. Lembo, M. Lenzerini, and R. Rosati. Tractable reasoning and efficient query answering in description logics: The DL-Lite family. J. Autom. Reason., 2007.

[8]

G. D. Giacomo, D. Lembo, M. Lenzerini, A. Poggi, R. Rosati, M. Ruzzi, and D. Savo. MASTRO: A reasoner for effective ontology-based data access. In ORE, 2012.

[9]

F. Goasdoué, I. Manolescu, and A. Roatiş. Efficient query answering against dynamic RDF databases. In EDBT, 2013.

[10]

G. Gottlob, G. Orsi, and A. Pieris. Query rewriting and optimization for ontological databases. ACM TODS, 2014.

[11]

Y. Guo, Z. Pan, and J. Heflin. LUBM: A benchmark for OWL knowledge base systems. Web Semant., 2005.

[12]

Z. Kaoudi, I. Miliaraki, and M. Koubarakis. RDFS reasoning and query answering on top of DHTs. In ISWC, 2008.

[13]

D. Lanti, M. Rezk, G. Xiao, and D. Calvanese. The NPD benchmark: Reality check for OBDA systems. In EDBT, 2015.

[14]

T. Neumann and G. Weikum. The RDF-3X engine for scalable management of RDF data. VLDBJ, 2010.

[15]

M. Thomazo. Compact rewriting for existential rules. IJCAI, 2013.

[16]

J. Urbani, F. van Harmelen, S. Schlobach, and H. Bal. QueryPIE: Backward reasoning for OWL Horst over very large knowledge bases. In ISWC, 2011.

Cited By

Gu ZLanti DMosca AXiao GXiong JCalvanese D(2022)Ontology-based Data FederationProceedings of the 11th International Joint Conference on Knowledge Graphs10.1145/3579051.3579070(10-19)Online publication date: 27-Oct-2022
https://dl.acm.org/doi/10.1145/3579051.3579070
Xiao GCalvanese DKontchakov RLembo DPoggi ARosati RZakharyaschev M(2018)Ontology-based data accessProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304791(5511-5519)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304791
Delanaux RBonifati ARousset MThion R(2018)Query-Based Linked Data AnonymizationThe Semantic Web – ISWC 201810.1007/978-3-030-00671-6_31(530-546)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-00671-6_31
Show More Cited By

Reformulation-based query answering in RDF: alternatives and performance
1. Information systems
  1. Data management systems
    1. Database management system engines
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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Information & Contributors

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 8, Issue 12

Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii

August 2015

728 pages

ISSN:2150-8097

Editors:
Chen Li
University of California, Irvine
,
Volker Markl
TU Berlin

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VLDB Endowment

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Published: 01 August 2015

Published in PVLDB Volume 8, Issue 12

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Cited By

Gu ZLanti DMosca AXiao GXiong JCalvanese D(2022)Ontology-based Data FederationProceedings of the 11th International Joint Conference on Knowledge Graphs10.1145/3579051.3579070(10-19)Online publication date: 27-Oct-2022
https://dl.acm.org/doi/10.1145/3579051.3579070
Xiao GCalvanese DKontchakov RLembo DPoggi ARosati RZakharyaschev M(2018)Ontology-based data accessProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304791(5511-5519)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304791
Delanaux RBonifati ARousset MThion R(2018)Query-Based Linked Data AnonymizationThe Semantic Web – ISWC 201810.1007/978-3-030-00671-6_31(530-546)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-00671-6_31
He LShao BLi YXia HXiao YChen EChen L(2017)StylusProceedings of the VLDB Endowment10.14778/3149193.314920011:2(203-216)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.14778/3149193.3149200
Lanti DXiao GCalvanese D(2017)Cost-Driven Ontology-Based Data AccessThe Semantic Web – ISWC 201710.1007/978-3-319-68288-4_27(452-470)Online publication date: 21-Oct-2017
https://dl.acm.org/doi/10.1007/978-3-319-68288-4_27

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