Advertisement

Reasoning at Scale (Tutorial)

  • Jacopo UrbaniEmail author
Chapter
  • 402 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11078)

Abstract

This tutorial gives an overview of current methods for performing reasoning on very large knowledge bases. The first part of the lectures is dedicated to an introduction of the problem and of related technologies. Then, the tutorial continues discussing the state-of-the-art for reasoning on very large inputs with particular emphasis on the strengths and weaknesses of current approaches. Finally, the tutorial concludes with an outline of some of the most important research directions in this field.

References

  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of databases, vol. 8. Addison-Wesley Reading, Boston (1995)zbMATHGoogle Scholar
  2. 2.
    Antoniou, G., Van Harmelen, F.: A Semantic Web Primer. MIT press, Cambridge (2004)Google Scholar
  3. 3.
    Bancilhon, F., Maier, D., Sagiv, Y., Ullman, J.D.: Magic sets and other strange ways to implement logic programs. In: Proceedings of the Fifth ACM SIGACT-SIGMOD Symposium on Principles of Database Systems, pp. 1–15 (1985)Google Scholar
  4. 4.
    Benedikt, M., et al.: Benchmarking the chase. In: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 37–52. ACM (2017)Google Scholar
  5. 5.
    Grau, B.C., et al.: Acyclicity notions for existential rules and their application to query answering in ontologies. J. Artif. Intell. Res. 47, 741–808 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hayes, P.: RDF Semantics. W3C Recommendation (2004)Google Scholar
  7. 7.
    Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., Banerjee, J.: RDFox: A highly-scalable RDF store. In: Arenas, M. (ed.) ISWC 2015. LNCS, vol. 9367, pp. 3–20. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25010-6_1CrossRefGoogle Scholar
  8. 8.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016)CrossRefGoogle Scholar
  9. 9.
    Prud’hommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. W3C Recommendation (2008)Google Scholar
  10. 10.
    Urbani, J., Dutta, S., Gurajada, S., Weikum, G.: KOGNAC: efficient encoding of large knowledge graphs. In: Proceedings of IJCAI, pp. 3896–3902 (2016)Google Scholar
  11. 11.
    Urbani, J., Jacobs, C., Krötzsch, M.: Column-oriented datalog materialization for large knowledge graphs. In: Proceedings of AAAI (2016)Google Scholar
  12. 12.
    Urbani, J., Kotoulas, S., Maassen, J., Van Harmelen, F., Bal, H.: WebPIE: a web-scale parallel inference engine using MapReduce. Web Semant.: Sci. Serv. Agents World Wide Web 10, 59–75 (2012)CrossRefGoogle Scholar
  13. 13.
    Urbani, J., Krötzsch, M., Jacobs, C., Dragoste, I., Carral, D.: Efficient model construction for horn logic with VLog. In: Galmiche, D., Schulz, S., Sebastiani, R. (eds.) IJCAR 2018. LNCS, vol. 10900, pp. 680–688. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-94205-6_44CrossRefGoogle Scholar
  14. 14.
    Urbani, J., Piro, R., van Harmelen, F., Bal, H.: Hybrid reasoning on OWL RL. Semant. Web 5(6), 423–447 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands

Personalised recommendations