An Incremental Reasoning Algorithm for Large Scale Knowledge Graph

  • Yifei Wang
  • Jie LuoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)


Knowledge graphs usually contain much implicit semantic information, which needs to be further mined through semantic inference. Current algorithms can effectively accomplish such task, however they often require a full re-reasoning even when only a few new triples is added to expand the knowledge graph. In this paper, we propose an incremental reasoning algorithm which can effectively avoid re-reasoning over the entire knowledge graph while keeping the relative completeness of the final deduction results. Key to our approach is the filter algorithms which reduce the scale of data that need to be considered and a delay strategy which limit the number of time-consuming iterations while still preserve relative completeness. Extensive experiments and comprehensive evaluations are conducted and experimental results prove that our methods significantly outperform re-reasoning methods.


Knowledge reasoning Incremental reasoning Knowledge graph OWL2 RL 



This work was supported by National Natural Science Foundation of China (Grand No. 61502022) and State Key Laboratory of Software Development Environment (Grand No. SKLSDE-2017ZX-17).


  1. 1.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: EP-SPARQL: a unified language for event processing and stream reasoning. In: WWW 2011, pp. 635–644. ACM (2011)Google Scholar
  2. 2.
    Bazoobandi, H.R., Beck, H., Urbani, J.: Expressive stream reasoning with laser. CoRR abs/1707.08876 (2017)Google Scholar
  3. 3.
    Beck, H., Dao-Tran, M., Eiter, T., Fink, M.: LARS: a logic-based framework for analyzing reasoning over streams. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 1431–1438. AAAI Press (2015)Google Scholar
  4. 4.
    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). Scholar
  5. 5.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Operating Systems Design and Implementation, OSDI 2004, vol. 6, p. 10. USENIX Association, Berkeley (2004)Google Scholar
  6. 6.
    Gu, R., Wang, S., Wang, F., Yuan, C., Huang, Y.: Cichlid: efficient large scale RDFS/OWL reasoning with spark. In: IEEE International Parallel and Distributed Processing Symposium, pp. 700–709 (2015)Google Scholar
  7. 7.
    Guo, Y., Pan, Z., Heflin, J.: LUBM: a benchmark for OWL knowledge base systems. J. Web Sem. 3(2–3), 158–182 (2005)CrossRefGoogle Scholar
  8. 8.
    Kim, J., Park, Y.: Scalable OWL-horst ontology reasoning using SPARK. In: 2015 International Conference on Big Data and Smart Computing (BIGCOMP), pp. 79–86, February 2015Google Scholar
  9. 9.
    Kimmig, A., Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: A short introduction to probabilistic soft logic. In: Proceedings of the NIPS Workshop on Probabilistic Programming: Foundations and Applications, pp. 1–4 (2012)Google Scholar
  10. 10.
    Lehmann, J., et al.: DBpedia - a crystallization point for the web of data. J. Web Semant. 7(3), 154–165 (2009)CrossRefGoogle Scholar
  11. 11.
    McBride, B.: Jena: a semantic web toolkit. IEEE Internet Comput. 6(6), 55–59 (2002)CrossRefGoogle Scholar
  12. 12.
    Parsia, B., Matentzoglu, N., Gonçalves, R.S., Glimm, B., Steigmiller, A.: The OWL reasoner evaluation (ORE) 2015 competition report. J. Autom. Reason. 59(4), 455–482 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sirin, E., Parsia, B., Grau, B., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. Web Semant. Sci. Serv. Agents World Wide Web 5(2), 51–53 (2007)CrossRefGoogle Scholar
  14. 14.
    Tommasini, R., Della Valle, E., Mauri, A., Brambilla, M.: RSPLab: RDF stream processing benchmarking made easy. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 202–209. Springer, Cham (2017). Scholar
  15. 15.
    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., et al. (eds.) ESWC 2010. LNCS, vol. 6088, pp. 213–227. Springer, Heidelberg (2010). Scholar
  16. 16.
    Urbani, J., Kotoulas, S., Oren, E., van Harmelen, F.: Scalable distributed reasoning using MapReduce. In: Bernstein, A., et al. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 634–649. Springer, Heidelberg (2009). Scholar
  17. 17.
    Wei, Y., Luo, J., Xie, H.: KGRL: an OWL2 RL reasoning system for large scale knowledge graph. In: 12th International Conference on Semantics, Knowledge and Grids, SKG 2016, Beijing, China, 15–17 August 2016, pp. 83–89 (2016)Google Scholar
  18. 18.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud 2010, p. 10. USENIX Association, Berkeley (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development Environment, School of Computer Science and EngineeringBeihang UniversityBeijingChina

Personalised recommendations