KI - Künstliche Intelligenz

, Volume 24, Issue 1, pp 51–55 | Cite as

Logic-Based Question Answering

  • Ulrich Furbach
  • Ingo Glöckner
  • Hermann Helbig
  • Björn Pelzer


Question answering systems aim to provide concise and correct responses to arbitrary questions, communicating with the user in a natural language. This way they help making the knowledge of large textual sources accessible in an intuitive manner which goes beyond the capabilities of conventional search engines. In the LogAnswer project the universities of Hagen and Koblenz cooperate to build a German language question answering system which combines computational linguistics and automated reasoning to deduce answers from a knowledge base derived from Wikipedia.


Theorem Prover Automate Reasoning Textual Source Question Answering Automate Theorem Prover 
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.


  1. 1.
    Furbach U, Glöckner I, Helbig H, Pelzer B (2008) LogAnswer—a deduction-based question answering system. In: Automated reasoning (IJCAR 2008). Lecture notes in computer science. Springer, Berlin, pp 139–146 CrossRefGoogle Scholar
  2. 2.
    Furbach U, Glöckner I, Pelzer B (2009) An application of automated reasoning in natural language question answering. AI communications, Special issue on practical aspects of automated reasoning (to appear) Google Scholar
  3. 3.
    Bos J (2001) DORIS 2001: Underspecification, resolution and inference for discourse representation structures. In: ICoS-3—inference in computational semantics, workshop proceedings Google Scholar
  4. 4.
    Harabagiu A, Moldovan D, Mihalcea R, Surdeanu M, Rus V (2000) FALCON: boosting knowledge for answer engines. In: Proc of the 9th text retrival conference (TREC-9), pp 479–488 Google Scholar
  5. 5.
    Fellbaum C (ed) (1998) WordNet: an electronic lexical database, 1st edn. MIT Press, Cambridge (ISBN: 0-262-06197-X) zbMATHGoogle Scholar
  6. 6.
    Moldovan D, Clark C, Harabagiu S, Maiorano S (2003) COGEX: a logic prover for question answering. In: Proc. of NAACL-HLT, Morristown, NJ, vol 1, pp 87–93 Google Scholar
  7. 7.
    Bos J, Markert K (2006) When logical inference helps determining textual entailment (and when it doesn’t). In: Proc. of 2nd PASCAL RTE challenge workshop Google Scholar
  8. 8.
    Bobrow D, Condoravdi C, Crouch R, de Paiva V, Kaplan R, Karttunen L, King T, Zaenen A (2005) A basic logic for textual inference. In: Proceedings of the AAAI workshop on inference for textual question answering, Pittsburgh, PA Google Scholar
  9. 9.
    Helbig H (2006) Knowledge representation and the semantics of natural language. Springer, Berlin zbMATHGoogle Scholar
  10. 10.
    Pelzer B, Wernhard C (2007) System description: E-KRHyper. In: Automated deduction—CADE-21, proceedings, pp 508–513 Google Scholar
  11. 11.
    Baumgartner P, Furbach U, Niemelä I (1996) Hyper tableaux. In: JELIA’96, proceedings, pp 1–17 Google Scholar
  12. 12.
    Glöckner I Pelzer B (2008) Exploring robustness enhancements for logic-based passage filtering. In: Knowledge based intelligent information and engineering systems (proc of KES2008, part I). LNAI, vol 5117. Springer, Berlin, pp 606–614 CrossRefGoogle Scholar
  13. 13.
    Peters C, Deselaers T, Ferro N, Gonzalo J, Jones GJF, Kurimo M, Mandl T, Peñas A, Petras V (eds) (2009) Evaluating systems for multilingual and multimodal information access: 9th workshop of the cross-language evaluation forum, CLEF 2008, Aarhus, Denmark, September 17–19. Revised selected papers. LNCS, vol 5706. Springer, Berlin/Heidelberg Google Scholar
  14. 14.
    Glöckner I, Pelzer B (2009) Combining logic and machine learning for answering questions. In: Evaluating systems for multilingual and multimodal information access: 9th workshop of the cross-language evaluation forum, CLEF 2008, Aarhus, Denmark, September 17–19. Revised selected papers. LNCS, vol 5706. Springer, Berlin/Heidelberg, pp 401–408 CrossRefGoogle Scholar
  15. 15.
    Lenat DB (1995) CYC: a large-scale investment in knowledge infrastructure. Commun ACM 38(11):33–38 CrossRefGoogle Scholar
  16. 16.
    Niles I, Pease A (2001) Towards a standard upper ontology. In: Proceedings of the 2nd international conference on formal ontology in information systems (FOIS-2001) Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Ulrich Furbach
    • 1
  • Ingo Glöckner
    • 2
  • Hermann Helbig
    • 2
  • Björn Pelzer
    • 1
  1. 1.AI Research Group, Computer Science FacultyUniversität Koblenz-LandauKoblenzDeutschland
  2. 2.Intelligent Information and Communication Systems Group (IICS)FernUniversität in HagenHagenDeutschland

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