From text mining to knowledge extraction

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The Semantic Web promises a wealth of ready-to-use information to the world. Companies like Google recently engaged in such an endeavour to provide more than just textual information to its users, but some semantic content as well. Since it is a colossal work to manually encode that information into some formal knowledge representation format, automatic information extraction from text becomes therefore relevant for that purpose. Extracting information is too context sensitive to be applicable on the Web per se. Therefore, concise domains, like corporate Intranets, may prove to offer more realistic application grounds. Also, since queries to such corporate libraries are usually goal (or task) oriented, as opposed to general browsing queries, it may be useful for the query-answering system to benefit from a classification of the texts that compose these libraries. When manually built, these classifications are also too costly to be achieved. Consequently, in that context where documents are (partly) classified with regard to precise subject matters, this research program proposes: 1) to study the integration of various feature selection algorithms into the knowledge extraction process, based on some of our previous work in feature selection, text classification and knowledge extraction, 2) to study the impact of automatic ontology construction tools on the knowledge extraction process itself, 3) to propose some probabilistic assessment algorithm for the relevance of the extracted knowledge structures pertaining to recall and precision, and 4) to propose some knowledge indexing schema that would speed up the look up time needed to filter out the irrelevant documents from the relevant ones.