Personalized Knowledge Discovery: Mining Novel Association Rules from Text
Xin Chen, Yi-fang Brook Wu · 2006
This paper presents a methodology for personalized knowledge discovery from text. It derives a user's background knowledge from his/her background documents, and exploits such knowledge to evaluate the novelty of discovered knowledge in the form of association rules by measuring the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment results show that the proposed user-oriented novelty measure is highly correlated with the human subjective rule novelty and usefulness ratings. It outperforms seven major objective interestingness measures and the WordNet novelty measure for identifying novel and useful rules.