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Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes

by: Giovambattista Ianni, Thomas Krennwallner, Alessandra Martello, and Axel Polleres

In: The Semantic Web - ISWC 2009, Vol. 5823Springer (2009) , p. 310--327.

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.

BibTeX record

@inproceedings{ianni2009,
  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.},
  added-at = {2009-11-03T13:41:58.000+0100},
  author = {Ianni, Giovambattista and Krennwallner, Thomas and Martello, Alessandra and Polleres, Axel},
  biburl = {http://www.bibsonomy.org/bibtex/21006e7ff526a40a5bb05978af90ae3e7/ivan_herman},
  booktitle = {The Semantic Web - ISWC 2009},
  editor = {Bernstein, Abraham and Karger, David R. and Heath, Tom and Feigenbaum, Lee and Maynard, Diana and Motta, Enrico and Thirunarayan, Krishnaprasad},
  interhash = {8b3a4e3698d3cdd97e84255bbbe542af},
  intrahash = {1006e7ff526a40a5bb05978af90ae3e7},
  isbn = {978-3-642-04929-3},
  keywords = {rdf rdfs rules semanticweb sparql},
  location = {Heidelberg},
  pages = {310--327},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2009-11-03T13:41:58.000+0100},
  title = {Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes},
  volume = 5823,
  year = 2009
}

Endnote record

%0 Conference Paper
%1 ianni2009
%A Ianni, Giovambattista
%A Krennwallner, Thomas
%A Martello, Alessandra
%A Polleres, Axel
%B The Semantic Web - ISWC 2009
%@ 978-3-642-04929-3
%D 2009
%E Bernstein, Abraham
%E Karger, David R.
%E Heath, Tom
%E Feigenbaum, Lee
%E Maynard, Diana
%E Motta, Enrico
%E Thirunarayan, Krishnaprasad
%I Springer
%K 
%P 310--327
%T Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes
%V 5823
%X 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.