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Discovering Personalized Novel Knowledge from Text
Abstract
This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules derived and many already known to the user. Our proposed algorithm derives user’s background knowledge from a set of documents provided by the user, and exploits such knowledge in the process of knowledge discovery from text. Keywords are extracted from background documents and clustered into a concept hierarchy that captures the semantic usage of keywords and their relationships in the background documents. Target documents are retrieved by selecting documents that are relevant to the user’s background. Association rules are discovered among noun phrases extracted from target documents. Novelty of an association rule is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment shows that our novelty measure performs better than support and confidence in identifying novel knowledge.
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