Implementing Inductive Concept Learning For Cooperative Query Answering

  • Maheen Bakhtyar
  • Nam Dang
  • Katsumi Inoue
  • Lena Wiese
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Generalization operators have long been studied in the area of Conceptual Inductive Learning (Michalski, A theory and methodolgy of inductive learning. In: Machine learning: An artificial intelligence approach (pp. 111–161). TIOGA Publishing, 1983; De Raedt, About knowledge and inference in logical and relational learning. In: Advances in machine learning II (pp. 143–153). Springer, Berlin, 2010). We present an implementation of these learning operators in a prototype system for cooperative query answering. The implementation can however also be used as a usual concept learning mechanism for concepts described in first-order predicate logic. We sketch an extension of the generalization process by a ranking mechanism on answers for the case that some answers are not related to what user asked.


  1. De Raedt, L. (2010). About knowledge and inference in logical and relational learning. In Advances in Machine Learning II (pp. 143–153). Berlin: Springer.CrossRefGoogle Scholar
  2. Gaasterland, T., Godfrey, P., & Minker, J. (1992). Relaxation as a platform for cooperative answering. Journal of Intelligent Information Systems, 1(3/4), 293–321.CrossRefGoogle Scholar
  3. Inoue, K., & Wiese, L. (2011). Generalizing conjunctive queries for informative answers. In 9th International Conference on Flexible Query Answering Systems. Lecture notes in artificial intelligence (Vol. 7022, pp. 1–12). New York: Springer.Google Scholar
  4. Michalski, R. S. (1983). A theory and methodolgy of inductive learning. In Machine learning: An artificial intelligence approach (pp. 111–161). TIOGA Publishing.Google Scholar
  5. Muslea, I. (2004). Machine learning for online query relaxation. In Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 246–255). New York: ACM.Google Scholar
  6. Nikitina, N., Rudolph, S., & Glimm, B. (2012). Interactive ontology revision. Journal of Web Semantics, 12, 118–130.CrossRefGoogle Scholar
  7. Shin, M. K., Huh, S.-Y., & Lee, W. (2007). Providing ranked cooperative query answers using the metricized knowledge abstraction hierarchy. Expert Systems with Applications, 32(2), 469–484.CrossRefGoogle Scholar
  8. Wu, Z., & Palmer, M. (1994). Verb semantics and Lexical selection. In 32nd Annual Meeting of the Association for Computational Linguistics (pp. 133–138). Los Altos: Morgan Kaufmann Publishers.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maheen Bakhtyar
    • 1
  • Nam Dang
    • 4
  • Katsumi Inoue
    • 3
  • Lena Wiese
    • 2
  1. 1.Asian Institute of Technology BangkokBangkokThailand
  2. 2.University of GöttingenGöttingenGermany
  3. 3.National Institute of InformaticsTokyoJapan
  4. 4.Tokyo Institute of TechnologyTokyoJapan

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