Efficient Model Construction for Horn Logic with VLog

System Description
  • Jacopo Urbani
  • Markus Krötzsch
  • Ceriel Jacobs
  • Irina Dragoste
  • David CarralEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10900)


We extend the Datalog engine VLog to develop a column-oriented implementation of the skolem and the restricted chase – two variants of a sound and complete algorithm used for model construction over theories of existential rules. We conduct an extensive evaluation over several data-intensive theories with millions of facts and thousands of rules, and show that VLog can compete with the state of the art, regarding runtime, scalability, and memory efficiency.


  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison Wesley, Boston (1994)Google Scholar
  2. 2.
    Aref, M., ten Cate, B., Green, T.J., Kimelfeld, B., Olteanu, D., Pasalic, E., Veldhuizen, T.L., Washburn, G.: Design and implementation of the LogicBlox system. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1371–1382. ACM (2015)Google Scholar
  3. 3.
    Baget, J.-F., Leclère, M., Mugnier, M.-L., Rocher, S., Sipieter, C.: Graal: a toolkit for query answering with existential rules. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 328–344. Springer, Cham (2015). Scholar
  4. 4.
    Benedikt, M., Konstantinidis, G., Mecca, G., Motik, B., Papotti, P., Santoro, D., Tsamoura, E.: Benchmarking the chase. In: Proceedings 36th Symposium on Principles of Database Systems (PODS 2017), pp. 37–52. ACM (2017)Google Scholar
  5. 5.
    Benedikt, M., Leblay, J., Tsamoura, E.: PDQ: proof-driven query answering over web-based data. PVLDB 7(13), 1553–1556 (2014)Google Scholar
  6. 6.
    Bonifati, A., Ileana, I., Linardi, M.: Functional dependencies unleashed for scalable data exchange. In: Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM 2016), pp. 2:1–2:12. ACM (2016)Google Scholar
  7. 7.
    Calì, A., Gottlob, G., Kifer, M.: Taming the infinite chase: query answering under expressive relational constraints. In: Proceedings 11th International Conference on Principles of Knowledge Representation and Reasoning (KR 2008), pp. 70–80. AAAI Press (2008)Google Scholar
  8. 8.
    Carral, D., Dragoste, I., Krötzsch, M.: Restricted chase (non)termination for existential rules with disjunctions. In: Sierra [19], pp. 922–928 (2017)Google Scholar
  9. 9.
    Cuenca Grau, B., Horrocks, I., Krötzsch, M., Kupke, C., Magka, D., Motik, B., Wang, Z.: Acyclicity notions for existential rules and their application to query answering in ontologies. J. Artif. Intell. Res. 47, 741–808 (2013)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Dantsin, E., Eiter, T., Gottlob, G., Voronkov, A.: Complexity and expressive power of logic programming. ACM Comput. Surv. 33(3), 374–425 (2001)CrossRefGoogle Scholar
  11. 11.
    Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data exchange: semantics and query answering. Theor. Comput. Sci. 336(1), 89–124 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Geerts, F., Mecca, G., Papotti, P., Santoro, D.: That’s all folks! LLUNATIC goes open source. PVLDB 7(13), 1565–1568 (2014)Google Scholar
  13. 13.
    Kazakov, Y.: Consequence-driven reasoning for Horn \(\cal{SHIQ}\) ontologies. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 2040–2045. IJCAI (2009)Google Scholar
  14. 14.
    Kazakov, Y., Krötzsch, M., Simančík, F.: The incredible ELK: from polynomial procedures to efficient reasoning with \(\cal{EL}\) ontologies. J. Autom. Reason. 53, 1–61 (2013)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Krötzsch, M., Thost, V.: Ontologies for knowledge graphs: breaking the rules. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 376–392. Springer, Cham (2016). Scholar
  16. 16.
    Marx, M., Krötzsch, M., Thost, V.: Logic on MARS: ontologies for generalised property graphs. In: Sierra [19], pp. 1188–1194 (2017)Google Scholar
  17. 17.
    Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., Banerjee, J.: RDFox: a highly-scalable RDF store. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 3–20. Springer, Cham (2015). Scholar
  18. 18.
    Seo, J., Guo, S., Lam, M.S.: SociaLite: an efficient graph query language based on Datalog. IEEE Trans. Knowl. Data Eng. 27(7), 1824–1837 (2015)CrossRefGoogle Scholar
  19. 19.
    Sierra, C. (ed.) Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017). IJCAI (2017)Google Scholar
  20. 20.
    Urbani, J., Jacobs, C., Krötzsch, M.: Column-oriented Datalog materialization for large knowledge graphs. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016), pp. 258–264. AAAI Press (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jacopo Urbani
    • 1
  • Markus Krötzsch
    • 2
  • Ceriel Jacobs
    • 1
  • Irina Dragoste
    • 2
  • David Carral
    • 2
    Email author
  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.cfaedTU DresdenDresdenGermany

Personalised recommendations