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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bellodi, E., Riguzzi, F.: Structure learning of probabilistic logic programs by searching the clause space. Theor. Pract. Logic Program. 15(2), 169–212 (2015)
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_11
Blockeel, H., et al.: The ACE data mining system, user’s manual. Katholieke Universiteit Leuven, Belgium (2006)
Bromberger, S.: Why-questions. In: Colodny, R.G. (ed.) Mind and Cosmos: Essays in Contemporary Science and Philosophy. University of Pittsburgh Press, Pittsburgh (1966)
Clark, P., et al.: Think you have solved question answering? Try arc, the ai2 reasoning challenge (2018). arXiv preprint arXiv:1803.05457
De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Mach. Learn. 100(1), 5–47 (2015)
Franconi, E., Guagliardo, P., Tessaris, S., Trevisan, M.: Quelo: an ontology-driven query interface. Proc. DL 2011(745), 488–498 (2011)
Gunning, D.: Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA), nd Web (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Lipton, Z.C.: The mythos of model interpretability (2016). arXiv preprint arXiv:1606.03490
Mollá, D., Vicedo, J.L.: Question answering in restricted domains: an overview. Comput. Linguist. 33(1), 41–61 (2007)
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)
Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect. Basic Books, New York (2018)
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)
Riguzzi, F.: cplint Manual. SWI-Prolog Version (2016). http://ds.ing.unife.it/~friguzzi/software/cplint-swi/manual.pdf
Schwitter, R.: Specifying events and their effects in controlled natural language. Proc. Soc. Behav. Sci. 27, 12–21 (2011)
Sterling, L., Shapiro, E.Y.: The Art of Prolog: Advanced Programming Techniques. MIT Press, Cambridge (1994)
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_30
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Salam, A., Schwitter, R., Orgun, M.A. (2019). Answering Why-Questions Using Probabilistic Logic Programming. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-35288-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35287-5
Online ISBN: 978-3-030-35288-2
eBook Packages: Computer ScienceComputer Science (R0)