Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-29T08:00:58.924Z Has data issue: false hasContentIssue false

DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries

Published online by Cambridge University Press:  22 September 2020

ALESSIO FIORENTINO
Affiliation:
Department of Mathematics and Computer Science (DeMaCS), University of Calabria, Rende, Italy (e-mail: lastname@mat.unical.it) - https://www.mat.unical.it
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science (DeMaCS), University of Calabria, Rende, Italy (e-mail: lastname@mat.unical.it) - https://www.mat.unical.it
MARCO MANNA
Affiliation:
Department of Mathematics and Computer Science (DeMaCS), University of Calabria, Rende, Italy (e-mail: lastname@mat.unical.it) - https://www.mat.unical.it

Abstract

The W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique name assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing, a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability.

Type
Original Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

This work has been partially supported by MISE under the project “S2BDW” (F/050389/01-03/X32) – Horizon 2020 PON I&C 2014-2020 and by Regione Calabria under the project “DLV LargeScale” (CUP J28C17000220006) – POR Calabria 2014-2020.

References

Allocca, C., Calimeri, F., Civili, C., Costabile, R., Cuteri, B., Fiorentino, A., Fuscà, D., Germano, S., Laboccetta, G., Manna, M., Perri, S., Reale, K., Ricca, F., Veltri, P., and Zangari, J. 2019. Large-scale reasoning on expressive horn ontologies. In Proceedings of Datalog 2.0. CEUR Workshop Proceedings, vol. 2368. CEUR-WS.org, 10–21.Google Scholar
Alviano, M., Leone, N., Veltri, P., and Zangari, J. 2019. Enhancing magic sets with an application to ontological reasoning. Theory Pract. Log. Program. 19, 5-6, 654670.Google Scholar
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., and Ives, Z. G. 2007. Dbpedia: A nucleus for a web of open data. In Proceedings of ISWC’07. LNCS, vol. 4825. Springer, 722–735.Google Scholar
Baader, F., Horrocks, I., and Sattler, U. 2008. Description logics. In Handbook of Knowledge Representation. Foundations of Artificial Intelligence, vol. 3. Elsevier, 135–179.Google Scholar
Baget, J., Leclère, M., Mugnier, M., Rocher, S., and Sipieter, C. 2015. Graal: A toolkit for query answering with existential rules. In Proceedings of RuleML’15. LNCS, vol. 9202. Springer, 328–344.Google Scholar
Bienvenu, M. 2016. Ontology-mediated query answering: Harnessing knowledge to get more from data. In Proceedings of IJCAI’16. IJCAI/AAAI Press, 40584061.Google Scholar
Calimeri, F., Fuscà, D., Perri, S., and Zangari, J. 2016. I-DLV: The new intelligent grounder of DLV. In Proceedings of AIIA’16. LNCS, vol. 10037. Springer, 192–207.Google Scholar
Calvanese, D., Cogrel, B., Komla-Ebri, S., Kontchakov, R., Lanti, D., Rezk, M., Rodriguez-Muro, M., and Xiao, G. 2017. Ontop: Answering SPARQL queries over relational databases. Semantic Web 8, 3, 471487.Google Scholar
Calvanese, D., Giacomo, G. D., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., Rosati, R., Ruzzi, M., and Savo, D. F. 2011. The MASTRO system for ontology-based data access. Semantic Web 2, 1, 4353.CrossRefGoogle Scholar
Carral, D., Dragoste, I., González, L., Jacobs, C. J. H., Krötzsch, M., and Urbani, J. 2019. Vlog: A rule engine for knowledge graphs. In Proceedings of ISWC’19. LNCS, vol. 11779. Springer, 19–35.Google Scholar
Ceri, S., Gottlob, G., and Tanca, L. 1989. What you always wanted to know about datalog (and never dared to ask). IEEE Trans. Knowl. Data Eng. 1, 1, 146166.Google Scholar
Eiter, T., Ortiz, M., Simkus, M., Tran, T., and Xiao, G. 2012. Query rewriting for horn-shiq plus rules. In Proceedings of AAAI’12. AAAI Press.Google Scholar
Faruqui, R. U. and MacCaull, W. 2012. O wl O nt DB: A scalable reasoning system for OWL 2 RL ontologies with large aboxes. In Proceedings of FHIES’12. LNCS, vol. 7789. Springer, 105–123.Google Scholar
Grau, B. C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P. F., and Sattler, U. 2008. OWL 2: The next step for OWL. J. Web Semant. 6, 4, 309322.CrossRefGoogle Scholar
Guo, Y., Pan, Z., and Heflin, J. 2005. LUBM: A benchmark for OWL knowledge base systems. J. Web Sem. 3, 2-3, 158182.CrossRefGoogle Scholar
Harris, S. and Seaborne, A. 2018. SPARQL 1.1 Query Language. W3C Recommendation. World Wide Web Consortium.Google Scholar
Horridge, M. and Bechhofer, S. 2009. The OWL API: A java API for working with OWL 2 ontologies. In Proceedings of OWLED’09. CEUR Workshop Proceedings, vol. 529. CEUR-WS.org.Google Scholar
Kazakov, Y. 2009. Consequence-driven reasoning for horn SHIQ ontologies. In Proceedings of IJCAI’09. 2040–2045.Google Scholar
Krötzsch, M., Mehdi, A., and Rudolph, S. 2010. Orel: Database-driven reasoning for OWL 2 profiles. In Proceedings of DL’10. CEUR Workshop Proceedings, vol. 573. CEUR-WS.org.Google Scholar
Leone, N., Allocca, C., Alviano, M., Calimeri, F., Civili, C., Costabile, R., Fiorentino, A., Fuscà, D., Germano, S., Laboccetta, G., Cuteri, B., Manna, M., Perri, S., Reale, K., Ricca, F., Veltri, P., and Zangari, J. 2019. Enhancing DLV for large-scale reasoning. In Proceedings of LPNMR’19. LNCS, vol. 11481. Springer, 312–325.Google Scholar
Leone, N., Manna, M., Terracina, G., and Veltri, P. 2019. Fast query answering over existential rules. ACM Trans. Comput. Log. 20, 2, 12:1–12:48.Google Scholar
Motik, B., Cuenca Grau, B., Horrocks, I., Wu, Z., Fokoue, A., and Lutz, C. 2012. OWL 2 Web Ontology Language Profiles (Second Edition). W3C Recommendation. World Wide Web Consortium.Google Scholar
Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., and Banerjee, J. 2015. Rdfox: A highly-scalable RDF store. In Proceedings of ISWC’15. LNCS, vol. 9367. Springer, 3–20.Google Scholar
Sirin, E. and Parsia, B. 2007. SPARQL-DL: SPARQL query for OWL-DL. In Proceedings of OWLED’07. CEUR Workshop Proceedings, vol. 258. CEUR-WS.org.Google Scholar
Smith, M. K., Welty, C., and McGuinness, D. L. 2004. OWL Web Ontology Language Guide. W3C Recommendation. World Wide Web Consortium.Google Scholar
W3C OWL Working Group. 2012. OWL 2 Web Ontology Language Document Overview (Second Edition). W3C Recommendation. World Wide Web Consortium.Google Scholar
Xiao, G., Eiter, T., and Heymans, S. 2012. The drew system for nonmonotonic dl-programs. In Proceedings of CSWS’12. Springer, 383390.Google Scholar