Automated Knowledge Acquisition Meets Metareasoning: Incremental Quality Assessment of Concept Hypotheses during Text Understanding

We introduce a methodology for automated knowledge acquisition and 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 speciic \quality labels" (indicating their signiicance, reliability, strength). Quality assessment of these hypotheses accounts for conceptual criteria referring to their current 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. We also provide a preliminary empirical evaluation , with focus on the learning rates and the learning accuracy that were achieved using this approach. Abstract We introduce a methodology for automated knowledge acquisition and 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 speciic \quality labels" (indicating their signiicance, reliability, strength). Quality assessment of these hypotheses accounts for conceptual criteria referring to their current 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. We also provide a preliminary empirical evaluation, with focus on the learning rates and the learning accuracy that were achieved using this approach.