Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes. Ianni, G., Krennwallner, T., Martello, A., & Polleres, A. In Bernstein, A., Karger, D. R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., & Thirunarayan, K., editors, Proceedings of the 8th International Semantic Web Conference (ISWC 2009), volume 5823, of Lecture Notes in Computer Science (LNCS), pages 310–327, Washington D, CUSA, October, 2009. Springer. ![Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex RDF Schema (RDFS) as a lightweight ontology language is gaining popularity and, consequently, tools for scalable RDFS inference and querying are needed. SPARQL has become recently a W3C standard for querying RDF data, but it mostly provides means for querying simple RDF graphs only, whereas querying with respect to RDFS or other entailment regimes is left outside the current specification. In this paper, we show that SPARQL faces certain unwanted ramifications when querying ontologies in conjunction with RDF datasets that comprise multiple named graphs, and we provide an extension for SPARQL that remedies these effects. Moreover, since RDFS inference has a close relationship with logic rules, we generalize our approach to select a custom ruleset for specifying inferences to be taken into account in a SPARQL query. We show that our extensions are technically feasible by providing benchmark results for RDFS querying in our prototype system \giabata, which uses Datalog coupled with a persistent Relational Database as a back-end for implementing SPARQL with dynamic rule-based inference. By employing different optimization techniques like magic set rewriting our system remains competitive with state-of-the-art RDFS querying systems.
@inproceedings{iann-etal-2009iswc,
Abstract = {RDF Schema (RDFS) as a lightweight ontology language is gaining popularity and, consequently, tools for scalable RDFS inference and querying are needed. SPARQL has become recently a W3C standard for querying RDF data, but it mostly provides means for querying simple RDF graphs only, whereas querying with respect to RDFS or other entailment regimes is left outside the current specification. In this paper, we show that SPARQL faces certain unwanted ramifications when querying ontologies in conjunction with RDF datasets that comprise multiple named graphs, and we provide an extension for SPARQL that remedies these effects. Moreover, since RDFS inference has a close relationship with logic rules, we generalize our approach to select a custom ruleset for specifying inferences to be taken into account in a SPARQL query. We show that our extensions are technically feasible by providing benchmark results for RDFS querying in our prototype system \giabata{}, which uses Datalog coupled with a persistent Relational Database as a back-end for implementing SPARQL with dynamic rule-based inference. By employing different optimization techniques like magic set rewriting our system remains competitive with state-of-the-art RDFS querying systems.},
Address = {Washington D, CUSA},
Author = {Giovambattista Ianni and Thomas Krennwallner and Alessandra Martello and Axel Polleres},
Booktitle = {Proceedings of the 8th International Semantic Web Conference (ISWC 2009)},
Day = {25--29},
Editor = {Abraham Bernstein and David R. Karger and Tom Heath and Lee Feigenbaum and Diana Maynard and Enrico Motta and Krishnaprasad Thirunarayan},
Month = oct,
Pages = {310--327},
Project = {lion},
Publisher = {Springer},
Series = LNCS,
Title = {Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes},
Type = CONF,
Url = {http://www.polleres.net/publications/iann-etal-2009iswc.pdf},
Volume = {5823},
Year = 2009,
Bdsk-Url-1 = {http://www.polleres.net/publications/iann-etal-2009iswc.pdf}}
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In this paper, we show that SPARQL faces certain unwanted ramifications when querying ontologies in conjunction with RDF datasets that comprise multiple named graphs, and we provide an extension for SPARQL that remedies these effects. Moreover, since RDFS inference has a close relationship with logic rules, we generalize our approach to select a custom ruleset for specifying inferences to be taken into account in a SPARQL query. We show that our extensions are technically feasible by providing benchmark results for RDFS querying in our prototype system \\giabata, which uses Datalog coupled with a persistent Relational Database as a back-end for implementing SPARQL with dynamic rule-based inference. 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