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A quality-based terminological reasoning model for text knowledge acquisition

  • Udo Hahn
  • Manfred Klenner
  • Klemens Schnattinger
Eliciting Knowledge from Textual and Other Sources
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1076)

Abstract

We introduce a methodology for knowledge acquisition and concept learning from texts that relies upon a quality-based model of terminological reasoning. Concept hypotheses which have been derived in the course of the text understanding process are assigned specific “quality labels” (indicating their significance, reliability, strength). Quality assessment of these hypotheses accounts for conceptual criteria referring to their given knowledge base context as well as linguistic indicators (grammatical constructions, discourse patterns), which led to their generation. We advocate a metareasoning approach which allows for the quality-based evaluation and a bootstrapping-style selection of alternative concept hypotheses as text understanding incrementally proceeds.

Keywords

Knowledge Acquisition Target Concept Computational Linguistics Hypothesis Space Translation Rule 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Udo Hahn
    • 1
  • Manfred Klenner
    • 1
  • Klemens Schnattinger
    • 1
  1. 1.Computational Linguistics GroupFreiburg UniversityFreiburgGermany

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