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Text knowledge engineering by qualitative terminological learning

  • Uncertainty Handling and Qualitative Reasoning
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Database and Expert Systems Applications (DEXA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1308))

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Abstract

We propose a methodology for enhancing domain knowledge bases through natural language text understanding. The acquisition of previously unknown concepts is based on the assessment of the “quality” of linguistic and conceptual evidence underlying the generation and refinement of concept hypotheses. Text understanding and concept learning are both grounded on a terminological knowledge representation and reasoning framework.

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References

  1. R. Agarwal and M. Tanniru. Knowledge extraction using content analysis. Knowledge Acquisition, 3(4):421–441, 1991.

    Google Scholar 

  2. D. Appelt, J. Hobbs, J. Bear, D. Israel, and M. Tyson. FASTUS: A finite-state processor for information extraction from real-world text. In IJCAI '93 — Proc. of the 13th Int'l. Joint Conf. on Artificial Intelligence, pages 1172–1178, 1993.

    Google Scholar 

  3. F. Gomez and C. Segami. The recognition and classification of concepts, in understanding scientific texts. Journal of Experimental and Theoretical Artificial Intelligence, 1:51–77, 1989.

    Google Scholar 

  4. U. Hahn, M. Klenner, and K. Schnattinger. Learning from texts: A terminological metareasoning perspective. In S. Wermter, E. Riloff, and G. Scheler, editors, Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, pages 453–468. Berlin: Springer, 1996.

    Google Scholar 

  5. U. Hahn, M. Klenner, and K. Schnattinger. A quality-based terminological reasoning model for text knowledge acquisition. In N. Shadbolt, K. O'Hara, and G. Schreiber, editors, EKAW '96 — Proc. of the 9th European Knowledge Acquisition Workshop, pages 131–146. Berlin: Springer, 1996.

    Google Scholar 

  6. U. Hahn, S. Schacht, and N. Bröker. Concurrent, object-oriented natural language parsing: The ParseTalk model. International Journal of Human-Computer Studies, 41(1/2):179–222, 1994.

    Google Scholar 

  7. P. Hastings. Implications of an automatic lexical acquisition system. In S. Wermter, E. Riloff, and G. Scheler, editors, Connectionist, Statistical and Symbolic Approaches to Learning in Natural Language Processing, pages 261–274. Berlin: Springer, 1996.

    Google Scholar 

  8. B. Nebel. Reasoning and Revision in Hybrid Representation Systems. Berlin: Springer, 1990.

    Google Scholar 

  9. L. Rau, P. Jacobs, and U. Zernik. Information extraction and text summarization using linguistic knowledge acquisition. Information Processing & Management, 25(4):419–428, 1989.

    Google Scholar 

  10. U. Reimer. Automatic acquisition of terminological knowledge from texts. In ECAI '90 — Proc. of the 9th European Conf. on Artificial Intelligence, pages 547–549. London: Pitman, 1990.

    Google Scholar 

  11. K. Schnattinger and U. Hahn. A terminological qualification calculus for preferential reasoning under uncertainty. In KI '96 — Proc. of the 20th Annual German Conf. on Artificial Intelligence, pages 349–362. Berlin: Springer, 1996.

    Google Scholar 

  12. K. Schnattinger and U. Hahn. Intelligent text analysis for dynamically maintaining and updating domain knowledge bases. In IDA '97 — Proc. of the 2nd Int'l. Symposium on Intelligent Data Analysis. Berlin: Springer, 1997.

    Google Scholar 

  13. K. Schnattinger, U. Hahn, and M. Klenner. Terminological meta-reasoning by reification and multiple contexts. In EPIA '95 — Proc. of the 7th Portuguese Conf. on Artificial Intelligence, pages 1–16. Berlin: Springer, 1995.

    Google Scholar 

  14. D. Skuce, S. Matwin, B. Tauzovich, F. Oppacher, and S. Szpakowicz.A logicbased knowledge source system for natural language documents. Data & Knowledge Engineering, 1(3):201–231, 1985.

    Google Scholar 

  15. S. Soderland, D. Fisher, J. Aseltine, and W. Lehnert. CRYSTAL: Inducing a conceptual dictionary. In IJCAI '95-Proc. of the 14th Intl. Joint Conf. on Artificial Intelligence, pages 1314–1319, 1995.

    Google Scholar 

  16. S. Szpakowicz. Semi-automatic acquisition of conceptual structure from technical texts. International Journal of Man-Machine Studies, 33:385–397, 1990.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Linguistics Lab - Text Knowledge Engineering Group, Freiburg University, Werthmannplatz 1, D-79085, Freiburg, Germany

    Udo Hahn & Klemens Schnattinger

Authors
  1. Udo Hahn
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  2. Klemens Schnattinger
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Editor information

Abdelkader Hameurlain A Min Tjoa

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© 1997 Springer-Verlag Berlin Heidelberg

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Hahn, U., Schnattinger, K. (1997). Text knowledge engineering by qualitative terminological learning. In: Hameurlain, A., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1997. Lecture Notes in Computer Science, vol 1308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022070

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  • DOI: https://doi.org/10.1007/BFb0022070

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63478-2

  • Online ISBN: 978-3-540-69580-6

  • eBook Packages: Springer Book Archive

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