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Answering Why-Questions Using Probabilistic Logic Programming

  • Abdus SalamEmail author
  • Rolf Schwitter
  • Mehmet A. Orgun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11919)

Abstract

We present a novel architecture of a closed domain question answering system that learns to answer why-questions from a small number of example interpretations. We use a probabilistic logic programming framework that can learn probabilities for rules from positive and negative example interpretations. These rules are then used by a meta-interpreter to generate an explanation in the form of a proof for a why-question. The explanation is displayed as an answer to the question together with a probability. In certain contexts, follow-up questions can be asked that conditionally depend on these why-questions and have an effect on the probability of the subsequent answer. The presented approach is a contribution to explainable artificial intelligence that aims to take machine learning out of the black-box.

Keywords

why-questions Probabilistic logic programming Meta-interpreter Natural language processing 

References

  1. 1.
    Bellodi, E., Riguzzi, F.: Structure learning of probabilistic logic programs by searching the clause space. Theor. Pract. Logic Program. 15(2), 169–212 (2015)CrossRefGoogle Scholar
  2. 2.
    Bernstein, A., Kaufmann, E.: GINO – a guided input natural language ontology editor. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 144–157. Springer, Heidelberg (2006).  https://doi.org/10.1007/11926078_11CrossRefGoogle Scholar
  3. 3.
    Blockeel, H., et al.: The ACE data mining system, user’s manual. Katholieke Universiteit Leuven, Belgium (2006)Google Scholar
  4. 4.
    Bromberger, S.: Why-questions. In: Colodny, R.G. (ed.) Mind and Cosmos: Essays in Contemporary Science and Philosophy. University of Pittsburgh Press, Pittsburgh (1966)Google Scholar
  5. 5.
    Clark, P., et al.: Think you have solved question answering? Try arc, the ai2 reasoning challenge (2018). arXiv preprint arXiv:1803.05457
  6. 6.
    De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Mach. Learn. 100(1), 5–47 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Franconi, E., Guagliardo, P., Tessaris, S., Trevisan, M.: Quelo: an ontology-driven query interface. Proc. DL 2011(745), 488–498 (2011)Google Scholar
  8. 8.
    Gunning, D.: Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA), nd Web (2017)Google Scholar
  9. 9.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  10. 10.
    Lipton, Z.C.: The mythos of model interpretability (2016). arXiv preprint arXiv:1606.03490
  11. 11.
    Mollá, D., Vicedo, J.L.: Question answering in restricted domains: an overview. Comput. Linguist. 33(1), 41–61 (2007)CrossRefGoogle Scholar
  12. 12.
    Oh, J.H., et al.: Why question answering using sentiment analysis and word classes. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 368–378. Association for Computational Linguistics (2012)Google Scholar
  13. 13.
    Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect. Basic Books, New York (2018)zbMATHGoogle Scholar
  14. 14.
    Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM (2016)Google Scholar
  15. 15.
    Riguzzi, F.: cplint Manual. SWI-Prolog Version (2016). http://ds.ing.unife.it/~friguzzi/software/cplint-swi/manual.pdf
  16. 16.
    Schwitter, R.: Specifying events and their effects in controlled natural language. Proc. Soc. Behav. Sci. 27, 12–21 (2011)CrossRefGoogle Scholar
  17. 17.
    Sterling, L., Shapiro, E.Y.: The Art of Prolog: Advanced Programming Techniques. MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  18. 18.
    Vennekens, J., Verbaeten, S., Bruynooghe, M.: Logic programs with annotated disjunctions. In: Demoen, B., Lifschitz, V. (eds.) ICLP 2004. LNCS, vol. 3132, pp. 431–445. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27775-0_30CrossRefzbMATHGoogle Scholar
  19. 19.
    Yang, Y., Yih, W.T., Meek, C.: WIKIQA: a challenge dataset for open-domain question answering. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2013–2018 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of ComputingMacquarie UniversitySydneyAustralia

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