Abstract not found

Abstract not found

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 specific "quality labels" (indicating their significance, 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. Appeared in: KAW'96 - Proc. 10th Knowledge Acquisition Workshop, 1996, pp...

This paper presents our more recent research on the area of text reading and understanding and knowledge extraction. More specifically, we give an overview of TextStorm and Clouds: two modules for the construction of concept maps. The first one deals with the task of extracting relations between concepts from a text file, while the latter concentrates on completing these relations and extrapolating rules about the knowledge in hand. This is a hybrid framework that applies two different areas of Artificial Intelligence: Natural Language Processing (in TextStorm) and Machine Learning (in Clouds). We show an example and draw conclusions.

We propose a knowledge-intensive text analysis approach which deals with the continuous assimilation of new concepts into domain knowledge bases. Text understanding and knowledge acquisition proceed in tandem on the basis of terminological reasoning. Concept learning is considered an evidence-based choice problem the solution of which balances the "quality" of various clues from the linguistic structure of the texts and conceptual structures in the knowledge bases.

We introduce a knowledge-based approach to the analysis of real-world natural language texts, which addresses the particular needs of dealing with new knowledge items. In order to incrementally expand the underlying conceptual knowledge base, we exploit two kinds of evidence from the text understanding process, viz. qualitative knowledge about linguistic constructions in natural language texts and that about structural patterns in the emerging text knowledge base. These clues are used to generate concept hypotheses, rank them according to plausibility, and select the most credible ones for assimilation into the conceptual knowledge base. We demonstrate the feasibility of our approach and discuss the results of an empirical evaluation in terms of concept learning rates and learning accuracy. Appeared in: RIAO97 - Proc. 5th Conference on Computer-Assisted Information Searching on the Internet. Montreal, Quebec, Canada, 25-27 June 1997. Centre de Hautes Etudes Internationales d'Informatiq...