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User-Configurable Semantic Data Stream Reasoning Using SPARQL Update

  • Original Article
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Journal on Data Semantics

Abstract

Stream reasoning is one of the building blocks giving semantic web an advantage in the race for the real-time web. This paper demonstrates implementation of materialisation-based reasoning using an event processor supporting networks of specification-compliant SPARQL Update rules. Collections of rules coded in SPARQL leave the rule implementation exposed for selection and modification by the platform user using the same query language for both the queries and entailment rules. Observations on the differences of SPARQL and rule semantics are made. The entailment-category tests of the SPARQL 1.1 conformance test set are thoroughly reviewed. New rules are constructed to improve platform pass rate, and the test results are measured. An event-based memory handling solution to the accumulation of data in stream processing scenarios through separation of static data (e.g. the ontology) from dynamic event data is presented and tested. This implementation extends the reasoning support available in an RDF stream processor from RDF(S) to \(\rho \hbox {df}\), D*, P-entailment and OWL 2 RL. The performance of the Instans platform is measured using a well-known benchmark requiring reasoning, comparing complete sets of entailment rules against the necessary subset to complete each test. Performance is also compared to non-streaming SPARQL query processors with reasoning support.

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Notes

  1. RDF and SPARQL as version 1.1, OWL as “OWL 2”.

  2. quantitatively demonstrated in Sect. 6.

  3. e.g. rdfD2, which states that all predicates are properties and rdfs4, which states that all subjects and objects are resources.

  4. Incremental eNgine for STANding Sparql, http://instans.org.

  5. https://github.com/SeijiKoide/SWCLOS/tree/master/Manual.

  6. http://topbraid.org/spin/owlrl-all.html.

  7. http://spinrdf.org/.

  8. https://jena.apache.org/.

  9. http://www.topquadrant.com/tools/IDE-topbraid-composer-maestro-edition/.

  10. http://streamreasoning.org/resources/c-sparql.

  11. http://www.swi-prolog.org/pldoc/package/semweb.html.

  12. http://stardog.com/.

  13. https://jena.apache.org/documentation/inference/

  14. http://www.w3.org/Submission/SWRL/.

  15. https://github.com/aaltodsg/instans-reasoning.

  16. Rule gl not implemented.

  17. http://www.w3.org/TR/sparql11-query/#propertypath-examples.

  18. http://www.w3.org/2009/sparql/docs/tests/.

  19. http://www.w3.org/2009/sparql/implementations/.

  20. https://www.w3.org/TR/rif-overview/.

  21. https://github.com/stardog-union/pellet.

  22. the results page does not include information on the program versions used to obtain the results.

  23. http://swat.cse.lehigh.edu/projects/lubm/.

  24. seed 0, index 0.

  25. http://www.w3.org/TR/turtle/.

  26. http://www.l3s.de/~minack/rdf2rdf/.

  27. https://github.com/aaltodsg/reasoner-jena.

  28. http://www.scala-lang.org/.

  29. \({\sim }2.2\,\hbox {s}\) with reasoning, \({\sim }1.5\,\hbox {s}\) without.

  30. Instans and Jena also re-start completely for each execution.

  31. in addition to the 40.9% passable without entailments.

References

  1. Anicic D, Fodor P, Rudolph S, Stojanovic N (2011) EP-SPARQL: a unified language for event processing and stream reasoning. In: WWW ’11 proceedings of the 20th international conference on World wide web, ACM, Hyderabad, India, WWW ’11, pp 635–644. doi:10.1145/1963405.1963495

  2. Barbieri DF, Braga D, Ceri S, Della Valle E, Grossniklaus M (2010a) Incremental reasoning on streams and rich background knowledge. In: Proceedings of the 7th Extended Semantic Web Conference, vol 6088 LNCS, pp 1–15. doi:10.1007/978-3-642-13486-9_1

  3. Barbieri DF, Braga D, Ceri S, Grossniklaus M (2010b) An execution environment for C-SPARQL queries. In: Proceedings of the 13th international conference on extending database technology—EDBT ’10, Lausanne, Switzerland, p 441. doi:10.1145/1739041.1739095

  4. Barbieri DF, Braga D, Ceri S, Valle ED, Grossniklaus M (2010c) C-SPARQL: a continuous query language for RDF data streams. Int J Semant Comput 04:3. doi:10.1142/S1793351X10000936

    Article  MATH  Google Scholar 

  5. Bischof S, Krötzsch M, Polleres A, Rudolph S (2014) Schema-agnostic query rewriting in SPARQL 1.1. In: ISWC 2014, vol 8796, pp 584–600. doi:10.1007/978-3-319-11964-9_37

  6. Forgy CL (1982) Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif Intell 19(1):17–37. doi:10.1016/0004-3702(82)90020-0

    Article  Google Scholar 

  7. Glimm B, Ogbuji C (2013) SPARQL 1.1 entailment regimes W3C recommendation 21 March 2013. http://www.w3.org/TR/2013/REC-sparql11-entailment-20130321/

  8. Guo Y, Pan Z, Heflin J (2004) An evaluation of knowledge base systems for large OWL datasets. In: ISWC 2004, vol 3, pp 274–288. doi:10.1007/978-3-540-30475-3_20

  9. ter Horst HJ (2004) Extending the RDFS entailment lemma. ISWC 2004:77–91. doi:10.1007/978-3-540-30475-3_7

    Google Scholar 

  10. ter Horst HJ (2005a) Combining RDF and part of OWL with rules: semantics, decidability, complexity. In: Gil Y, Motta E, Benjamins VR, Musen MA (eds) ISWC 2005. Springer Berlin Heidelberg, pp 668–684. doi:10.1007/11574620_48

  11. ter Horst HJ (2005) Completeness, decidability and complexity of entailment for RDF schema and a semantic extension involving the OWL vocabulary. Web Semant Sci Serv Agents World Wide Web 3(2–3):79–115. doi:10.1016/j.websem.2005.06.001

    Article  Google Scholar 

  12. Imprialou M, Stoilos G, Grau BC (2012) Benchmarking ontology-based query rewriting systems. In: Twenty-sixth AAAI conference on artificial intelligence, AAAI Press, pp 779–785

  13. Komazec S, Cerri D, (2011) Towards efficient schema-enhanced pattern matching over RDF data streams. In: Workshop on ordering and reasoning (ORDRING, 2011). Springer, Bonn, Germany

  14. Komazec S, Cerri D, Fensel D (2012) Sparkwave : Continuous schema-enhanced pattern matching over RDF data streams. In: Proceedings of the 6th ACM international conference on distributed event-based systems, ACM, pp 58–68. doi:10.1145/2335484.2335491

  15. Margara A, Urbani J, Van Harmelen F, Bal H (2014) Streaming the web: reasoning over dynamic data. J Web Semant 25:24–44. doi:10.1016/j.websem.2014.02.001

    Article  Google Scholar 

  16. Muñoz S, Pérez J, Gutiérrez C (2007) Minimal deductive systems for RDF. In: The semantic web: research and applications, 4th European semantic web conference, ESWC 2007, vol 4519, pp 53–67. doi:10.1007/978-3-540-72667-8_6

  17. Muñoz S, Pérez J, Gutierrez C (2009) Simple and efficient minimal RDFS? Web Semant Sci Serv Agents World Wide Web 7(3):220–234. doi:10.1016/j.websem.2009.07.003

    Article  Google Scholar 

  18. Nenov Y, Piro R, Motik B, Horrocks I, Wu Z, Banerjee J (2015) RDFox : a highly-scalable RDF Store. In: The Semantic Web—ISWC 2015—part 2, Springer International Publishing, pp 3–20. doi:10.1007/978-3-319-25010-6_1

  19. Pérez-Urbina H, Rodríguez-Díaz E, Grove M, Konstantinidis G, Sirin E (2012) Evaluation of query rewriting approaches for OWL 2. In: Proceedings of the joint workshop on scalable and high-performance semantic web systems—SSWS + HPCSW 2012, CEUR Workshop Proceedings, vol 943, pp 32–44

  20. Polleres A, Hogan A, Delbru R, Umbrich J (2013) RDFS and OWL reasoning for linked data, vol 8067. Springer, Berlin Heidelberg, LNAI. doi:10.1007/978-3-642-39784-4_2

  21. Rinne M, Nuutila E (2014) Constructing event processing systems of layered and heterogeneous events with SPARQL. In: Meersman R, Panetto H, Dillon T, Missikoff M, Liu L, Pastor O, Cuzzocrea A, Sellis T (eds) On the move to meaningful internet systems: OTM 2014 conferences, Springer Berlin Heidelberg, pp 682–699. doi:10.1007/978-3-662-45563-0_42

  22. Singh S, Karwayun R (2010) A comparative study of inference engines. In: 2010 seventh international conference on information technology: new generations, IEEE, pp 53–57. doi:10.1109/ITNG.2010.198

  23. Valle ED, Schlobach S, Krötzsch M, Bozzon A, Ceri S, Horrocks I (2013) Order matters! harnessing a world of orderings for reasoning over massive data. Semant Web J 4(2):219–231. doi:10.3233/SW-2012-0085

    Google Scholar 

  24. Volz R, Staab S, Motik B (2005) Incrementally maintaining materializations of ontologies stored in logic databases. J Data Semant 2:1–34. doi:10.1007/978-3-540-30567-5_1

    MATH  Google Scholar 

  25. W3C (2012) OWL 2 web ontology language profiles (second edition) W3C recommendation 11 December 2012. http://www.w3.org/TR/owl2-profiles/

  26. W3C (2013a) SPARQL 1.1 query language W3C recommendation 21.3.2013. http://www.w3.org/TR/sparql11-query/

  27. W3C (2013b) SPARQL 1.1 update W3C recommendation 21 March 2013. http://www.w3.org/TR/sparql11-update/

  28. W3C (2014a) RDF 1.1 concepts and abstract syntax W3C recommendation 25 February 2014. http://www.w3.org/TR/rdf11-concepts/

  29. W3C (2014b) RDF 1.1 semantics W3C recommendation 25 February 2014. http://www.w3.org/TR/rdf11-mt

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Acknowledgements

This work has been carried out in the TrafficSense project funded by Aalto University.

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  1. Aalto University School of Science, PO Box 15400, 00076, Aalto, Finland

    Mikko Rinne & Esko Nuutila

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  1. Mikko Rinne
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Correspondence to Mikko Rinne.

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Rinne, M., Nuutila, E. User-Configurable Semantic Data Stream Reasoning Using SPARQL Update. J Data Semant 6, 125–138 (2017). https://doi.org/10.1007/s13740-017-0076-9

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