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A Marker Passing Approach to Winograd Schemas

  • Johannes FähndrichEmail author
  • Sabine Weber
  • Hannes Kanthak
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11341)

Abstract

This paper approaches a solution of Winograd Schemas with a marker passing algorithm which operates on an automatically generated semantic graph. The semantic graph contains common sense facts from data sources form the semantic web like domain ontologies e.g. from Linked Open Data (LOD), WordNet, Wikidata, and ConceptNet. Out of those facts, a semantic decomposition algorithm selects relevant facts for the concepts used in the Winograd Schema and adds them to the semantic graph. Markers are propagated through the graph and used to identify an answer to the Winograd Schema. Depending on the encoded knowledge in the graph (connectionist view of world knowledge) and the information encoded on the marker (for symbolic reasoning) our approach selects the answers. With this selection, the marker passing approach is able to beat the state-of-the-art approach by about 12%.

Keywords

Semantic web LOD Winograd Schema Common sense reasoning Symbolic connectionist AI 
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References

  1. 1.
    Arenas, M., Grau, B.C., Kharlamov, E., Marciuška, Š., Zheleznyakov, D.: Faceted search over RDF-based knowledge graphs. Web. Semant.: Sci. Serv. Agents World Wide Web 37–38, 55–74 (2016).  https://doi.org/10.1016/j.websem.2015.12.002. http://www.sciencedirect.com/science/article/pii/S1570826815001432CrossRefGoogle Scholar
  2. 2.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC 2007, ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52. (Chapter 52)CrossRefGoogle Scholar
  3. 3.
    Austin, J.: Distributed associative memories for high-speed symbolic reasoning. Fuzzy Sets Syst. 82(2), 223–233 (1996).  https://doi.org/10.1016/0165-0114(95)00258-8. http://eprints.whiterose.ac.uk/1871/1/austinj18.pdfCrossRefGoogle Scholar
  4. 4.
    Collins, A., Quillian, R.: Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8(2), 240–247 (1968).  https://doi.org/10.1016/S0022-5371(69)80069-1. http://linkinghub.elsevier.com/retrieve/pii/S0022537169800691CrossRefGoogle Scholar
  5. 5.
    Crestani, F.: Application of spreading activation techniques in information retrieval. Artif. Intell. Rev. 11(6), 453–482 (1997).  https://doi.org/10.1023/A:1006569829653CrossRefGoogle Scholar
  6. 6.
    Davis, E., Morgenstern, L., Ortiz, C.: The first Winograd schema challenge at IJCAI-16. AI Mag. 38(3), 97–98 (2017).  https://doi.org/10.1609/aimag.v38i4.2734. https://dblp.org/rec/journals/aim/DavisMO17CrossRefGoogle Scholar
  7. 7.
    Ecke, A., Peñaloza, R., Turhan, A.Y.: Similarity-based relaxed instance queries. J. Appl. Logic 13(1), 480–508 (2015).  https://doi.org/10.1016/j.jal.2015.01.002. http://www.sciencedirect.com/science/article/pii/S1570868315000038Workshop on Weighted Logics for AI - 2013MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Emami, A., Trischler, A., Suleman, K., Cheung, J.C.K.: A generalized knowledge hunting framework for the Winograd schema challenge. In: NAACL-HLT (2018). https://dblp.org/rec/conf/naacl/EmamiTSC18
  9. 9.
    Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017).  https://doi.org/10.1038/nature21056. http://www.nature.com/doifinder/10.1038/nature21056CrossRefGoogle Scholar
  10. 10.
    Fähndrich, J., Weber, S., Ahrndt, S.: Design and use of a semantic similarity measure for interoperability among agents. In: Klusch, M., Unland, R., Shehory, O., Pokahr, A., Ahrndt, S. (eds.) Multiagent System Technologies, vol. 9872, pp. 41–57. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45889-2_4CrossRefGoogle Scholar
  11. 11.
    Furbach, U., Schon, C.: Commonsense reasoning meets theorem proving. In: Klusch, M., Unland, R., Shehory, O., Pokahr, A., Ahrndt, S. (eds.) Multiagent System Technologies. LNCS, vol. 9872, pp. 3–17. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45889-2_1CrossRefGoogle Scholar
  12. 12.
    Ghallab, M., Nau, D., Traverso, P.: The actor’s view of automated planning and acting: a position paper. Artif. Intell. 208, 1–17 (2014).  https://doi.org/10.1016/j.artint.2013.11.002. http://linkinghub.elsevier.com/retrieve/pii/S0004370213001173CrossRefGoogle Scholar
  13. 13.
    Jones, M.N., Willits, J., Dennis, S.: Models of Semantic Memory, Models of Semantic Memory, vol. 1. Oxford University Press, Oxford (2015).  https://doi.org/10.1093/oxfordhb/9780199957996.013.11CrossRefGoogle Scholar
  14. 14.
    Kurzweil, R.: The Singularity is Near. Gerald Duckworth & Co, London (2005)Google Scholar
  15. 15.
    Lecue, F.: Applying machine reasoning and learning in real world applications. In: Pan, J.Z., et al. (eds.) Reasoning Web 2016. LNCS, vol. 9885, pp. 241–257. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-49493-7_7CrossRefGoogle Scholar
  16. 16.
    Levesque, H., Davis, E., Morgenstern, L.: The Winograd schema challenge. In: Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning, vol. 46, pp. 552–561 (2011)Google Scholar
  17. 17.
    Liu, Q., Jiang, H., Ling, Z.H., Zhu, X., Wei, S., Hu, Y.: Combing context and commonsense knowledge through neural networks for solving Winograd schema problems. Assoc. Adv. Artif. Intell. (2017). http://dblp.org/rec/journals/corr/LiuJLZWH16
  18. 18.
    Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: ACL (2014). http://dblp.org/rec/conf/acl/ManningSBFBM14
  19. 19.
    Morgenstern, L., Davis, E., Ortiz Jr, C.: Planning, executing, and evaluating the Winograd schema challenge. AI Mag. (2016). https://dblp.org/rec/journals/aim/MorgensternDO16
  20. 20.
    Neely, J.H.: Semantic priming and retrieval from lexical memory: roles of inhibitionless spreading activation and limited-capacity attention. J. Exp. Psychol.: Gen. 106(3), 226–254 (1977)CrossRefGoogle Scholar
  21. 21.
    Pace-Sigge, M.: Spreading Activation Lexical Priming and the Semantic Web. Early Psycholinguistic Theories, Corpus Linguistics and AI Applications. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-90719-2CrossRefGoogle Scholar
  22. 22.
    Peng, H., Khashabi, D., Roth, D.: Solving hard coreference problems. In: Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2015). http://dblp.org/rec/conf/naacl/PengKR15
  23. 23.
    Rahman, A., Ng, V.: 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, pp. 777–789 (2012)Google Scholar
  24. 24.
    Richard-Bollans, A., Álvarez, L.G., Cohn, A.G.: The role of pragmatics in solving the Winograd schema challenge. In: COMMONSENSE (2017). https://dblp.org/rec/conf/commonsense/Richard-Bollans17
  25. 25.
    Searle, J.: Minds, brains, and programs. Behav. Brain Sci. 3(3), 417–424 (1980).  https://doi.org/10.1017/S0140525X00005756. http://www.journals.cambridge.org/abstract_S0140525X00005756CrossRefGoogle Scholar
  26. 26.
    Sharma, A., Vo, N.H., Aditya, S., Baral, C.: Towards addressing the Winograd schema challenge-building and using a semantic parser and a knowledge hunting module. In: International Joint Conference on Artificial Intelligence, pp. 1319–1325 (2015)Google Scholar
  27. 27.
    Shastri, L., Ajjanagadde, V.: From simple associations to systematic reasoning: a connectionist representation of rules, variables and dynamic bindings using temporal synchrony. Behav. Brain Sci. 16(03), 417–451 (2010).  https://doi.org/10.1017/S0140525X00030910. http://www.journals.cambridge.org/abstract_S0140525X00030910CrossRefGoogle Scholar
  28. 28.
    Smith, E., Shoben, E., Rips, L.: Structure and process in semantic memory: a featural model for semantic decisions. Psychol. Rev. 81(3), 214–241 (1974).  https://doi.org/10.1037/h0036351CrossRefGoogle Scholar
  29. 29.
    Sun, R.: A connectionist model for commonsense reasoning incorporating rules and similarities. Knowl. Acquis. 4(3), 293–321 (1992).  https://doi.org/10.1016/1042-8143(92)90020-2CrossRefGoogle Scholar
  30. 30.
    Wang, F.Y., et al.: Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond. IEEE/CAA J. Autom. Sin. 3(2), 113–120 (2016).  https://doi.org/10.1109/JAS.2016.7471613CrossRefGoogle Scholar
  31. 31.
    de Winter, J., Dodou, D.: Why the Fitts list has persisted throughout the history of function allocation. Cognit. Technol. Work. 16, 1–11 (2014).  https://doi.org/10.1007/s10111-011-0188-1. http://dx.doi.org/10.1007/s10111-011-0188-1CrossRefGoogle Scholar
  32. 32.
    Yamaguchi, A., Kozaki, K., Yamamoto, Y., Masuya, H., Kobayashi, N.: Semantic graph analysis for federated LOD surfing in life sciences. JIST 10675(5), 268–276 (2017).  https://doi.org/10.1007/978-3-319-70682-5-18CrossRefGoogle Scholar
  33. 33.
    Yampolskiy, R.: AI-complete, AI-hard, or AI-easy - classification of problems in AI. In: Twenty-third Midwest Artificial Intelligence and Cognitive Science Conference, pp. 94–101 (2012). http://ceur-ws.org/Vol-841/submission_3.pdf

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Johannes Fähndrich
    • 1
    Email author
  • Sabine Weber
    • 1
  • Hannes Kanthak
    • 1
  1. 1.Technische Universität BerlinBerlinGermany

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