CONNER: A Concurrent ILP Learner in Description Logic

  • Eyad AlgahtaniEmail author
  • Dimitar Kazakov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11770)


Machine Learning (ML) approaches can achieve impressive results, but many lack transparency or have difficulties handling data of high structural complexity. The class of ML known as Inductive Logic Programming (ILP) draws on the expressivity and rigour of subsets of First Order Logic to represent both data and models. When Description Logics (DL) are used, the approach can be applied directly to knowledge represented as ontologies. ILP output is a prime candidate for explainable artificial intelligence; the expense being computational complexity. We have recently demonstrated how a critical component of ILP learners in DL, namely, cover set testing, can be speeded up through the use of concurrent processing. Here we describe the first prototype of an ILP learner in DL that benefits from this use of concurrency. The result is a fast, scalable tool that can be applied directly to large ontologies.


Inductive logic programming Description logics Ontologies Parallel computing GPGPU 


  1. 1.
    Owens, J.D., et al.: A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 26(1), 80–113 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Algahtani, E., Kazakov, D.: GPU-accelerated hypothesis cover set testing for learning in logic. In: CEUR Proceedings of the 28th International Conference on Inductive Logic Programming. CEUR Workshop Proceedings (2018)Google Scholar
  3. 3.
    Fanizzi, N., d’Amato, C., Esposito, F.: DL-FOIL concept learning in description logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 107–121. Springer, Heidelberg (2008). Scholar
  4. 4.
    Quinlan, R.: Learning logical definitions from relations. Mach. Learn. 5, 239–266 (1990). Scholar
  5. 5.
    Bühmann, L., Lehmann, J., Westphal, P.: DL-learner - a framework for inductive learning on the semantic web. Web Semant. Sci. Serv. Agents World Wide Web 39, 15–24 (2016)CrossRefGoogle Scholar
  6. 6.
    Qomariyah, N., Kazakov, D.: Learning from ordinal data with inductive logic programming in description logic. In: Programming of the Late Breaking Papers of the 27th International Conference on Inductive Logic, pp. 38–50 (2017)Google Scholar
  7. 7.
    Qomariyah, N., Kazakov, D.: Learning binary preference relations: a comparison of logic-based and statistical approaches. In: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, Como, Italy (2017)Google Scholar
  8. 8.
    Wu, K., Haarslev, V.: A parallel reasoner for the description logic ALC. In: Proceedings of the 2012 International Workshop on Description Logics (DL 2012) (2012)Google Scholar
  9. 9.
    Meissner, A.: A simple parallel reasoning system for the \(\cal{ALC}\) description logic. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 413–424. Springer, Heidelberg (2009). Scholar
  10. 10.
    Chantrapornchai, C., Choksuchat, C.: TripleID-Q: RDF query processing framework using GPU. IEEE Trans. Parallel Distrib. Syst. 29(9), 2121–2135 (2018)CrossRefGoogle Scholar
  11. 11.
    Martínez-Angeles, C.A., Wu, H., Dutra, I., Costa, V.S., Buenabad-Chávez, J.: Relational learning with GPUs: accelerating rule coverage. Int. J. Parallel Prog. 44(3), 663–685 (2015). Scholar
  12. 12.
    Michalski, R.S.: Pattern recognition as rule-guided inductive inference. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2(4), 349–361 (1980)Google Scholar
  13. 13.
    Lehmann, J.: Learning OWL Class Expressions. IOS Press, Amsterdam (2010)zbMATHGoogle Scholar
  14. 14.
    Michie, D.: Memo functions and machine learning. Nature 218, 19–22 (1968)CrossRefGoogle Scholar
  15. 15.
    Lavrac, N., Zupanic, D., Weber, I., Kazakov, D., Stepankova, O., Dzeroski, S.: ILPNET repositories on WWW: inductive logic programming systems, datasets and bibliography. AI Commun. 9(4), 157–206 (1996)CrossRefGoogle Scholar
  16. 16.
    Fonseca, N.A., Silva, F., Camacho, R.: Strategies to parallelize ILP systems. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 136–153. Springer, Heidelberg (2005). Scholar
  17. 17.
    Fonseca, N.A., Srinivasan, A., Silva, F., Camacho, R.: Parallel ILP for distributed-memory architectures. Mach. Learn. 74(3), 257–279 (2009). Scholar
  18. 18.
    Fukunaga, A., Botea, A., Jinnai, Y., and Kishimoto, A.: A Survey of Parallel A*. arXiv:1708.05296 (2017)
  19. 19.
    Konstantopoulos, S.K.: A data-parallel version of Aleph. In: Proceedings of the Workshop on Parallel and Distributed Computing for Machine Learning (2007)Google Scholar
  20. 20.
    Nishiyama, H., Ohwada, H.: Yet another parallel hypothesis search for inverse entailment. In: 25th International Conference on ILP (2017)Google Scholar
  21. 21.
    Ohwada, H., Mizoguchi, F.: Parallel execution for speeding up inductive logic programming systems. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 277–286. Springer, Heidelberg (1999). Scholar
  22. 22.
    Ohwada, H., Nishiyama, H., and Mizoguchi, F.: Concurrent Execution of Optimal Hypothesis Search for Inverse Entailment. In: Cussens J., Frisch A. (eds) Inductive Logic Programming. ILP 2000. LNCS, vol. 1866, pp. 165-173. Heidelberg (2000).
  23. 23.
    Srinivasan, A., Faruquie, T.A., Joshi, S.: Exact data parallel computation for very large ILP datasets. In: The 20th International Conference on ILP (2010)Google Scholar
  24. 24.
    Zhou, Y. and Zeng, J.: Massively parallel A* search on a GPU. \(29^{th}\) AAAI Conference on Artificial Intelligence (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of YorkYorkUK

Personalised recommendations