Towards Better Ontological Support for Recognizing Textual Entailment

  • Andreas Wotzlaw
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6317)


Many applications in modern information technology utilize ontological knowledge to increase their performance, precision, and success rate. However, the integration of ontological sources is in general a difficult task since the semantics of all concepts, individuals, and relations must be preserved across the various sources. In this paper we discuss the importance of combined background knowledge for recognizing textual entailment (RTE). We present and analyze formally a new graph-based procedure for integration of concepts and individuals from ontologies based on the hierarchy of WordNet. We embed it in our experimental RTE framework where a deep-shallow semantic text analysis combined with logical inference is used to identify the logical relations between two English texts. Our results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results. The RTE application performs significantly better when a substantial amount of problem-relevant knowledge has been integrated into its inference process.


Inference Process Logical Inference Knowledge Graph Query Processor Knowledge Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cafarella, M.J., Re, C., Suciu, D., Etzioni, O.: Structured querying of web text data: A technical challenge. In: Proceedings of the Third Biennial Conference on Innovative Data Systems Research (CIDR), pp. 225–234 (2007)Google Scholar
  2. 2.
    Milne, D.N., Witten, I.H., Nichols, D.M.: A knowledge-based search engine powered by wikipedia. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM), pp. 445–454 (2007)Google Scholar
  3. 3.
    Chatterjee, N., Goyal, S., Naithani, A.: Resolving pattern ambiguity for English to Hindi machine translation using WordNet. In: Workshop on Modern Approaches in Translation Technologies (2005)Google Scholar
  4. 4.
    Ifrim, G., Weikum, G.: Transductive learning for text classification using explicit knowledge models. In: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD (2006)Google Scholar
  5. 5.
    Hunt, W., Lita, L., Nyberg, E.: Gazetteers, wordnet, encyclopedias, and the web: analyzing question answering resources. Technical Report CMU-LTI-04-188, Language Technologies Institute, Carnegie Mellon (2004)Google Scholar
  6. 6.
    Bentivogli, L., Dagan, I., Dang, H.T., Giampiccolo, D., Magnini, B.: The fifth PASCAL recognizing textual entailment challenge. In: TAC 2009 Workshop, Gaithersburg, Maryland (2009)Google Scholar
  7. 7.
    Bos, J., Markert, K.: When logical inference helps determining textual entailment (and when it doesn’t). In: Proceedings of the Second PASCAL Challenges Workshop on Recognizing Textual Entailment, Venice, Italy (2006)Google Scholar
  8. 8.
    Noy, N.F., Doan, A., Halevy, A.Y.: Semantic integration. AI Mag. 26(1), 7–9 (2005)Google Scholar
  9. 9.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. The MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  10. 10.
    Dagan, I., Dolan, B., Magnini, B., Roth, D.: Recognizing textual entailment: Rational, evaluation and approaches. Natural Language Engineering. Special Issue on Textual Entailment 15(4), i–xvii (2009)Google Scholar
  11. 11.
    Herrera, J., Peñas, A., Verdejo, F.: Techniques for recognizing textual entailment and semantic equivalence. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 419–428. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Blackburn, P., Bos, J.: Representation and Inference for Natural Language. A First Course in Computational Semantics. CSLI (2005)Google Scholar
  13. 13.
    Van der Sandt, R.A.: Presupposition projection as anaphora resolution. Journal of Semantics 9(4), 333–377 (1992)CrossRefGoogle Scholar
  14. 14.
    Suchanek, F., Kasneci, G., Weikum, G.: YAGO - a large ontology from Wikipedia and WordNet. Elsevier Journal of Web Semantics 6(3), 203–217 (2008)CrossRefGoogle Scholar
  15. 15.
    Curran, J.R., Clark, S., Bos, J.: Linguistically motivated large-scale NLP with C&C and boxer. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, Prague, Czech Republic, pp. 33–36 (2007)Google Scholar
  16. 16.
    Schäfer, U.: Integrating Deep and Shallow Natural Language Processing Components – Representations and Hybrid Architectures. PhD thesis, Saarland University, Saarbrücken, Germany (2007)Google Scholar
  17. 17.
    Bos, J., Markert, K.: Combining shallow and deep NLP methods for recognizing textual entailment. In: Proceedings of the First PASCAL Challenges Workshop on Recognising Textual Entailment, Southampton, UK, pp. 65–68 (2005)Google Scholar
  18. 18.
    Kamp, H., Reyle, U.: From Discourse to Logic. Introduction to Modeltheoretic Semantics of Natural Language. In: Formal Logic and Discourse Representation Theory. Kluwer Academic Publishers, Dordrecht (1993)Google Scholar
  19. 19.
    Blackburn, P., Bos, J., Kohlhase, M., Nivelle, H.D.: Automated theorem proving for natural language understanding. In: Problemsolving Methodologies with Automated Deduction (Workshop at CADE-15) (1998)Google Scholar
  20. 20.
    Bos, J., Markert, K.: Recognising textual entailment with logical inference. In: Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (EMNLP), Vancouver, Canada, pp. 628–635 (2005)Google Scholar
  21. 21.
    Bos, J.: Towards wide-coverage semantic interpretation. In: Proceedings of the 6th International Workshop on Computational Semantics IWCS-6, pp. 42–53 (2005)Google Scholar
  22. 22.
    Tatu, M., Moldovan, D.: A logic-based semantic approach to recognizing textual entailment. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, Morristown, NJ, pp. 819–826 (2006)Google Scholar
  23. 23.
    MacCartney, B., Manning, C.D.: An extended model of natural logic. In: Proceedings of the 8th International Conference on Computational Semantics (IWCS-8), pp. 140–156 (2009)Google Scholar
  24. 24.
    de Melo, G., Suchanek, F., Pease, A.: Integrating YAGO into the Suggested Upper Merged Ontology. In: Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI (2008)Google Scholar
  25. 25.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A nucleus for a web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  26. 26.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data: Principles and state of the art. In: Proceedings of the 17th World Wide Web Conference, WWW (2008)Google Scholar
  27. 27.
    Copestake, A., Flickinger, D., Pollard, C., Sag, I.A.: Minimal recursion semantics: An introduction. Research on Language and Computation 3, 281–332 (2005)CrossRefGoogle Scholar
  28. 28.
    Flickinger, D.: On building a more efficient grammar by exploiting types. Natural Language Engineering 6(1), 15–28 (2000)CrossRefGoogle Scholar
  29. 29.
    Brants, T.: TnT – a statistical part-of-speech tagger. In: Proceedings of the Sixth Applied Natural Language Processing Conference ANLP 2000, Seattle, WA, pp. 224–231 (2000)Google Scholar
  30. 30.
    Drożdżyński, W., Krieger, H.U., Piskorski, J., Schäfer, U., Xu, F.: Shallow processing with unification and typed feature structures – foundations and applications. Künstliche Intelligenz 18(1), 17–23 (2004)Google Scholar
  31. 31.
    Callmeier, U.: PET – a platform for experimentation with efficient HPSG processing techniques. Natural Language Engineering 6(1), 99–108 (2000)CrossRefGoogle Scholar
  32. 32.
    McCune, W.: Mace4 Reference Manual and Guide. Argonne National Laboratory, IL (2003)Google Scholar
  33. 33.
    McCune, W.: Prover9 manual (2009),
  34. 34.
    Dowty, D.: On semantic content of the notion of “thematic role”. In: Barbara Partee, G.C., Turner, R. (eds.) Properties, Types and Meaning, vol. 2, pp. 69–129. Kluwer, Dordrecht (1989)CrossRefGoogle Scholar
  35. 35.
    Boolos, G.S., Burgess, J.P., Jeffrey, R.C.: Computability and Logic. Cambridge University Press, Cambridge (2002)CrossRefzbMATHGoogle Scholar
  36. 36.
    Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: Proceedings of the 3rd International Conference on Computational Linguistics and Intelligent Text Processing, London, UK, pp. 136–145 (2002)Google Scholar
  37. 37.
    Giampiccolo, D., Magnini, B., Dagan, I., Dolan, B.: The third PASCAL recognizing textual entailment challenge. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague, Czech Republic, pp. 1–9 (2007)Google Scholar
  38. 38.
    Matuszek, C., Cabral, J., Witbrock, M., DeOliveira, J.: An introduction to the syntax and content of Cyc. In: Proceedings of the 2006 AAAI Spring Symposium on Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering, Stanford, CA (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andreas Wotzlaw
    • 1
  1. 1.Information Processing and Ergonomics FKIEFraunhofer Institute for CommunicationWachtbergGermany

Personalised recommendations