Abstract
A natural language text understanding system with advanced learning capabilities is presented. New concepts are acquired on the fly by incorporating two kinds of evidence — knowledge about linguistic constructions in which unknown lexical items occur and knowledge about structural patterns in ontologies such that new concept descriptions can be compared with prior knowledge. On the basis of the quality of evidence gathered this way concept hypotheses are generated, ranked according to plausibility, and the most credible ones are selected for assimilation into the domain knowledge base.
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© 1998 Springer-Verlag Berlin Heidelberg
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Hahn, U., Schnattinger, K. (1998). Quality control in the concept learning process. In: Mercer, R.E., Neufeld, E. (eds) Advances in Artificial Intelligence. Canadian AI 1998. Lecture Notes in Computer Science, vol 1418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64575-6_48
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DOI: https://doi.org/10.1007/3-540-64575-6_48
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