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Exploiting Wikipedia-Based Information-Rich Taxonomy for Extracting Location, Creator and Membership Related Information for ConceptNet Expansion

  • Marek Krawczyk
  • Rafal RzepkaEmail author
  • Kenji Araki
Conference paper
  • 293 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10930)

Abstract

In this paper we present a method for extracting IsA assertions (hyponymy relations), AtLocation assertions (informing of the location of an object or place), LocatedNear assertions (informing of neighboring locations), CreatedBy assertions (informing of the creator of an object) and MemberOf assertions (informing of group membership) automatically from Japanese Wikipedia XML dump files. We use the Hyponymy extraction tool v1.0, which analyses definition, category and hierarchy structures of Wikipedia articles to extract IsA assertions and produce information-rich taxonomy. From this taxonomy we extract additional information, in this case AtLocation, LocatedNear, CreatedBy and MemberOf types of assertions, using our original method. The presented experiments prove that both methods produce satisfactory results: we were able to acquire 5,866,680 IsA assertions with 96.0% reliability, 131,760 AtLocation assertion pairs with 93.5% reliability, 6,217 LocatedNear assertion pairs with 98.5% reliability, 270,230 CreatedBy assertion pairs with 78.5% reliability and 21,053 MemberOf assertions with 87.0% reliability. Our method surpassed the baseline system in terms of both precision and the number of acquired assertions.

Keywords

Common sense knowledge Knowledge extraction ConceptNet 

References

  1. 1.
    Lenat, D.B.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)CrossRefGoogle Scholar
  2. 2.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)Google Scholar
  3. 3.
    Liu, H., Singh, P.: ConceptNet? A practical commonsense reasoning tool-kit. BT Technol. J. 22(4), 211–226 (2004)CrossRefGoogle Scholar
  4. 4.
    Singh, P., Lin, T., Mueller, E.T., Lim, G., Perkins, T., Zhu, W.L.: Open mind common sense: knowledge acquisition from the general public. In: On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE, pp. 1223–1237 (2002)CrossRefGoogle Scholar
  5. 5.
    Speer, R.H., Havasi, C., Treadway, K.N., Lieberman, H.: Finding your way in a multi-dimensional semantic space with Luminoso. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 385–388 (2010)Google Scholar
  6. 6.
    Cambria, E., Hussain, A., Havasi, C., Eckl, C.: SenticSpace: visualizing opinions and sentiments in a multi-dimensional vector space. In: Knowledge-Based and Intelligent Information and Engineering Systems, pp. 385–393 (2010)CrossRefGoogle Scholar
  7. 7.
    Korner, S.J., Brumm, T.: RESI - a natural language specification improver. In: IEEE International Conference on Semantic Computing, pp. 1–8 (2009)Google Scholar
  8. 8.
    Nakahara, K., Yamada, S.: Development and evaluation of a web-based game for common-sense knowledge acquisition in Japan. Unisys Technol. Rev. 107, 295–305 (2011)Google Scholar
  9. 9.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI, vol. 5, p. 3 (2010)Google Scholar
  10. 10.
    Schubert, L.: Can we derive general world knowledge from texts? In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 94–97 (2002)Google Scholar
  11. 11.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8 (2011)Google Scholar
  12. 12.
    Krawczyk, M., Rzepka, R., Araki, K.: Extracting ConceptNet knowledge triplets from Japanese Wikipedia. In: Proceedings of the 21st Annual Meeting of the Association for Natural Language Processing, pp. 1052–1055 (2015)Google Scholar
  13. 13.
    Sumida, A., Torisawa, K.: Hacking Wikipedia for hyponymy relation acquisition. In: IJCNLP, vol. 8, pp. 883–888 (2008)Google Scholar
  14. 14.
    Sumida, A., Yoshinaga, N., Torisawa, K.: Boosting precision and recall of hyponymy relation acquisition from hierarchical layouts in Wikipedia. In: LREC (2008)Google Scholar
  15. 15.
    Yamada, I., Hashimoto, C., Oh, J., Torisawa, K., Kuroda, K., De Saeger, S., Tsuchida, M., Kazama, J.: Generating information-rich taxonomy from Wikipedia. In: 4th International Universal Communication Symposium (IUCS), pp. 97–104 (2010)Google Scholar
  16. 16.
    Randolph, J.J.: Free-Marginal Multirater Kappa (multirater K [free]): An Alternative to Fleiss’ Fixed-Marginal Multirater Kappa (2005). Online SubmissionGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Future ProcessingGliwicePoland
  2. 2.Hokkaido UniversitySapporoJapan

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