In this paper, we present a new method of estimating the novelty of rules discovered by data-mining methods using WordNet, a lexical knowledge-base of English words. We assess the novelty of a rule by the average semantic distance in a knowledge hierarchy between the words in the antecedent and the consequent of the rule -- the more the average distance, more is the novelty of the rule. The novelty of rules extracted by the DiscoTEX text-mining system on Amazon.com book descriptions were evaluated by both human subjects and by our algorithm. By computing correlation coefficients between pairs of human ratings and between human and automatic ratings, we found that the automatic scoring of rules based on our novelty measure correlates with human judgments about as well as human judgments correlate with one another.

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In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), 233-239, San Francisco, CA, 2001.

@InProceedings{basu:kdd01, title={Evaluating the Novelty of Text-Mined Rules using Lexical Knowledge}, author={Sugato Basu and Raymond J. Mooney and Krupakar V. Pasupuleti and Joydeep Ghosh}, booktitle={Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)}, address={San Francisco, CA}, key={DiscoTEX, KDD, IE}, pages={233-239}, url="http://www.cs.utexas.edu/users/ai-lab/?basu:kdd01", year={2001} }