Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Scaling Parallel Rule-Based Reasoning

  • Conference paper

Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 8465))

Included in the following conference series:

  • 2607Accesses

Abstract

Using semantic technologies the materialization of implicit given facts that can be derived from a dataset is an important task performed by a reasoner. With respect to the answering time for queries and the growing amount of available data, scaleable solutions that are able to process large datasets are needed. In previous work we described a rule-based reasoner implementation that uses massively parallel hardware to derive new facts based on a given set of rules. This implementation was limited by the size of processable input data as well as on the number of used parallel hardware devices. In this paper we introduce further concepts for a workload partitioning and distribution to overcome this limitations. Based on the introduced concepts, additional levels of parallelization can be proposed that benefit from the use of multiple parallel devices. Furthermore, we introduce a concept to reduce the amount of invalid triple derivations like duplicates. We evaluate our concepts by applying different rulesets to the real-world DBPedia dataset as well as to the synthetic Lehigh University benchmark ontology (LUBM) with up to 1.1 billion triples. The evaluation shows that our implementation scales in a linear way and outperforms current state of the art reasoner with respect to the throughput achieved on a single computing node.

Similar content being viewed by others

Keywords

References

  1. Peters, M., Brink, C., Sachweh, S., Zündorf, A.: Rule-based reasoning on massively parallel hardware. In: 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, pp. 33–49 (2013)

    Google Scholar 

  2. Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F., Bal, H.: OWL reasoning with webPIE: Calculating the closure of 100 billion triples. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 213–227. Springer, Heidelberg (2010)

    Google Scholar 

  3. Liu, C., Qi, G., Wang, H., Yu, Y.: Reasoning with large scale ontologies in fuzzy pD* using MapReduce. IEEE Computational Intelligence Magazine 7(2) (2012)

    Google Scholar 

  4. Weaver, J., Hendler, J.A.: Parallel materialization of the finite RDFS closure for hundreds of millions of triples. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 682–697. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. Forgy, C.L.: Rete: a fast algorithm for the many pattern/many object pattern match problem. In: Raeth, P.G. (ed.) Expert Systems, pp. 324–341 (1990)

    Google Scholar 

  6. ter Horst, H.J.: Completeness, decidability and complexity of entailment for RDF Schema and a semantic extension involving the OWL vocabulary. Web Semantics: Science, Services and Agents on the World Wide Web 3(2-3), 79–115 (2005)

    Article MathSciNet  Google Scholar 

  7. Muñoz, S., Pérez, J., Gutierrez, C.: Minimal deductive systems for RDF. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 53–67. Springer, Heidelberg (2007)

    Google Scholar 

  8. DBPedia,http://wiki.dbpedia.org/

  9. Guo, Y., Pan, Z., Heflin, J.: Lubm: A benchmark for OWL knowledge base systems. Web Semant., 158–182 (October 2005)

    Google Scholar 

  10. Soma, R., Prasanna, V.: Parallel inferencing for OWL knowledge bases. In: 37th International Conference on Parallel Processing, ICPP 2008, pp. 75–82 (2008)

    Google Scholar 

  11. Urbani, J., Kotoulas, S., Oren, E., van Harmelen, F.: Scalable distributed reasoning using mapReduce. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 634–649. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  12. Urbani, J., Margara, A., Jacobs, C., van Harmelen, F., Bal, H.: DynamiTE: Parallel materialization of dynamic RDF data. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 657–672. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Wu, Z., Eadon, G., Das, S., Chong, E.I., Kolovski, V., Annamalai, M., Srinivasan, J.: Implementing an inference engine for RDFS/OWL constructs and user-defined rules in Oracle. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 1239–1248 (2008)

    Google Scholar 

  14. Heino, N., Pan, J.Z.: RDFS reasoning on massively parallel hardware. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 133–148. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  15. Urbani, J., Kotoulas, S., Massen, J., van Harmelen, F., Bal, H.: WebPIE: A web-scale parallel inference engine using MapReduce. Web Semantics: Science, Services and Agents on the World Wide Web (2012)

    Google Scholar 

  16. Maier, F., Mutharaju, R., Hitzler, P.: Distributed reasoning with EL++ using MapReduce. Technical report, Kno.e.sis Center, Wright State University, Dayton, Ohio (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Applied Sciences Dortmund, Germany

    Martin Peters, Christopher Brink & Sabine Sachweh

  2. Software Engineering Research Group, Department of Computer Science and Electrical Engineering, University of Kassel, Germany

    Albert Zündorf

Authors
  1. Martin Peters

    You can also search for this author inPubMed Google Scholar

  2. Christopher Brink

    You can also search for this author inPubMed Google Scholar

  3. Sabine Sachweh

    You can also search for this author inPubMed Google Scholar

  4. Albert Zündorf

    You can also search for this author inPubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Cognitive Sciences and Technologies, Semantic Technology Laboratory, ISTC-CNR, Via Nomentana 56, 00161, Rome, Italy

    Valentina Presutti

  2. Department of Compter Science, University of Bari, Via Orabona, 4, 70125, Bari, Italia

    Claudia d’Amato

  3. Wimmics Research Team at Inria, University of Nice - Sophia Antipolis, Route des Lucioles, BP 93, 06902, Sophia Antipolis, France

    Fabien Gandon

  4. Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK

    Mathieu d’Aquin

  5. Institute for Web Science and Technologies, University of Koblenz, Universitätsstraße 1, 56016, Koblenz, Germany

    Steffen Staab

  6. Elsevier B.V., Radarweg 29, 1043 NX, Amsterdam, The Netherlands

    Anna Tordai

Rights and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Peters, M., Brink, C., Sachweh, S., Zündorf, A. (2014). Scaling Parallel Rule-Based Reasoning. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds) The Semantic Web: Trends and Challenges. ESWC 2014. Lecture Notes in Computer Science, vol 8465. Springer, Cham. https://doi.org/10.1007/978-3-319-07443-6_19

Download citation

Publish with us

[8]ページ先頭
©2009-2025 Movatter.jp