Minerva: A Scalable OWL Ontology Storage and Inference System

  • Jian Zhou
  • Li Ma
  • Qiaoling Liu
  • Lei Zhang
  • Yong Yu
  • Yue Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4185)


With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable and efficient ontology management systems. In this paper, we present Minerva, a storage and inference system for large-scale OWL ontologies on top of relational databases. It aims to meet scalability requirements of real applications and provide practical reasoning capability as well as high query performance. The method combines Description Logic reasoners for the TBox inference with logic rules for the ABox inference. Furthermore, it customizes the database schema based on inference requirements. User queries are answered by directly retrieving materialized results from the back-end database. The effective integration of ontology inference and storage is expected to improve reasoning efficiency, while querying without runtime inference guarantees satisfactory response time. Extensive experiments on University Ontology Benchmark show the high efficiency and scalability of Minerva system.


Inference System Description Logic SPARQL Query Triple Pattern Query Response Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Brickley, D., Guha, R. (eds.): RDF Vocabulary Description Language 1.0: RDF Schema. W3C Recommendation (2004)Google Scholar
  2. 2.
    Bechhofer, S., van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F., Stein, L.A. (eds.): OWL Web Ontology Language Reference. W3C Recommendation (2004)Google Scholar
  3. 3.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  4. 4.
    Haarslev, V., Möller, R.: High Performance Reasoning with Very Large Knowledge Bases. In: DL (2000)Google Scholar
  5. 5.
    Horrocks, I.: FaCT and iFaCT. In: DL (1999)Google Scholar
  6. 6.
    IBM’s Integrate Ontology Development Toolkit,
  7. 7.
    Prud’hommeaux, E., Seaborne, A. (eds.): SPARQL Query Language for RDF. W3C Working Draft (2005)Google Scholar
  8. 8.
    Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description logic programs: combining logic programs with description logic. In: WWW, pp. 48–57 (2003)Google Scholar
  9. 9.
    Ma, L., Yang, Y., Qiu, Z., Xie, G., Pan, Y., Liu, S.: Towards A Complete OWL Ontology Benchmark. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Haarslev, V., Möller, R.: RACER System Description. In: Goré, R.P., Leitsch, A., Nipkow, T. (eds.) IJCAR 2001. LNCS (LNAI), vol. 2083, Springer, Heidelberg (2001)Google Scholar
  11. 11.
    Sirin, E., Parsia, B.: Pellet: An OWL DL Reasoner. In: DL (2004)Google Scholar
  12. 12.
    Weithöner, T., Liebig, T., Specht, G.: Storing and Querying Ontologies in Logic Databases. In: Proceedings of SWDB 2003, The first International Workshop on Semantic Web and Databases, Co-located with VLDB 2003 (2003)Google Scholar
  13. 13.
    Beeri, C.: Logic Programming and Databases. In: ICLP (1990)Google Scholar
  14. 14.
    Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. In: Alternate Track Papers & Posters WWW (2004)Google Scholar
  15. 15.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 54–68. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Das, S., Chong, E.I., Eadon, G., Srinivasan, J.: Supporting Ontology-Based Semantic matching in RDBMS. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases (2004)Google Scholar
  17. 17.
    Agrawal, R., Somani, A., Xu, Y.: Storage and Querying of E-Commerce Data. In: VLDB 2001, Proceedings of 27th International Conference on Very Large Data Bases (2001)Google Scholar
  18. 18.
    Kiryakov, A., Ognyanov, D., Manov, D.: OWLIM- a pragmatic semantic repository for OWL. In: Proceedings of the 2005 International Workshop on Scalable Semantic Web Knowledge Base Systems, SSWS 2005 (2005)Google Scholar
  19. 19.
    Pan, Z., Heflin, J.: DLDB: Extending Relational Databases to Support Semantic Web Queries. In: PSSS1 - Proceedings of the First International Workshop on Practical and Scalable Semantic Systems (2003)Google Scholar
  20. 20.
    Guo, Y., Pan, Z., Heflin, J.: An Evaluation of Knowledge Base Systems for Large OWL Datasets. In: ISWC (2004)Google Scholar
  21. 21.
    Horrocks, I., Li, L., Turi, D., Bechhofer, S.: The Instance Store: DL Reasoning with Large Numbers of Individuals. In: DL (2004)Google Scholar
  22. 22.
    Motik, B., Sattler, U.: Practical DL reasoning over large A Boxes with KAON2 (2006), available at,
  23. 23.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Zhou
    • 1
  • Li Ma
    • 2
  • Qiaoling Liu
    • 1
  • Lei Zhang
    • 2
  • Yong Yu
    • 1
  • Yue Pan
    • 2
  1. 1.APEX Data and Knowledge Management Lab, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.IBM China Research LabBeijingChina

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