A quality-based terminological reasoning model for text knowledge acquisition

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.