Commonsense Reasoning in a Deeper Way: By Discovering Relations between Predicates

Wenguan Huang, Xudong Luo

2017

Abstract

One of the biggest drawbacks of nowadays AI reasoning systems is their lack of commonsense. To address the issue, some commonsense knowledge bases and a bunch of reasoning mechanisms with them have been developed to tackle this problem. However, most of them concentrate on the relation between entities (e.g., "cat" and "fish"), but few discuss the relation between predicates (e.g., "angry" and "shout"), which fall into a deeper level of commonsense. To the end, in this paper, we develop a commonsense reasoning framework, which focuses on this type of commonsense knowledge. More specifically, first we give a formal definition of this kind of commonsense. Then we construct a set of knowledge by extending the predicate set of ConceptNet, and apply information extraction technique to capture them from corpus. Finally, to evaluate our framework, we conduct experiments against a part of the Winograd Schema Challenge, which, its author claimed, is an alternative of Turing Test. The result of our experiments confirms the effectiveness of our framework.

References

  1. Angeli, G. and Manning, C. D. (2014). Naturalli: Natural logic inference for common sense reasoning. In EMNLP, pages 534-545.
  2. Berger-Wolf, T., Diochnos, D. I., London, A., Pluhár, A., Sloan, R. H., and Turán, G. (2013). Commonsense knowledge bases and network analysis. Commonsense.
  3. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. (2008). Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of data, pages 1247- 1250. ACM.
  4. Davis, E. and Marcus, G. (2015). Commonsense reasoning and commonsense knowledge in artificial intelligence. Communications of the ACM, 58(9):92-103.
  5. Hart, M. (1971). Project gutenberg. Project Gutenberg.
  6. Kazakov, Y. et al. (2009). Consequence-driven reasoning for horn shiq ontologies. In IJCAI, volume 9, pages 2040-2045.
  7. Rahman, A. and Ng, V. (2012). Resolving complex cases of definite pronouns: The winograd schema challenge. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 777-789.
  8. Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E., and Weikum, G. (2016). Yago: A multilingual knowledge base from Wikipedia, Wordnet, and Geonames. In The Semantic Web ISWC 2016, volume 9982 of Lecture Notes in Computer Science, pages 177-185.
  9. Schmitz, M., Bart, R., Soderland, S., Etzioni, O., et al. (2012). Open language learning for information extraction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 523-534. Association for Computational Linguistics.
  10. Sharma, A., Vo, N. H., Gaur, S., and Baral, C. (2015). An approach to solve winograd schema challenge using automatically extracted commonsense knowledge. In 2015 AAAI Spring Symposium Series. Citeseer.
  11. Socher, R., Bauer, J., Manning, C. D., and Ng, A. Y. (2013). Parsing with compositional vector grammars. In the Association for Computational Linguistics, pages 455-465.
  12. Soderland, S., Roof, B., Qin, B., Xu, S., Etzioni, O., et al. (2010). Adapting open information extraction to domain-specific relations. AI magazine, 31(3):93- 102.
  13. Speer, R. and Havasi, C. (2013). ConceptNet 5: A large semantic network for relational knowledge. In The Peoples Web Meets NLP, pages 161-176. Springer.
  14. Speer, R., Havasi, C., and Lieberman, H. (2008). Analogyspace: Reducing the dimensionality of common sense knowledge. In Proceedings of the 23rd National Conference on Artificial Intelligence , volume 1, pages 548-553.
  15. Tandon, N., De Melo, G., and Weikum, G. (2011). Deriving a web-scale common sense fact database. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pages 152-157.
  16. Toutanova, K., Klein, D., and Manning, C. D. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, volume 1, pages 252-259.
  17. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236):433-460.
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Paper Citation


in Harvard Style

Huang W. and Luo X. (2017). Commonsense Reasoning in a Deeper Way: By Discovering Relations between Predicates . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 407-414. DOI: 10.5220/0006120504070414


in Bibtex Style

@conference{icaart17,
author={Wenguan Huang and Xudong Luo},
title={Commonsense Reasoning in a Deeper Way: By Discovering Relations between Predicates},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={407-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006120504070414},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Commonsense Reasoning in a Deeper Way: By Discovering Relations between Predicates
SN - 978-989-758-220-2
AU - Huang W.
AU - Luo X.
PY - 2017
SP - 407
EP - 414
DO - 10.5220/0006120504070414