Sequential pattern mining and nonmonotonic reasoning for intelligent information agents

Raymond Y. K. Lau, Yuefeng Li, Sheng-Tang Wu, Xujuan Zhou

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


With the explosive growth of information available on the Internet, more effective data mining and data reasoning mechanism is required to process the sheer volume of information. Belief revision logic offers the expressive power to represent information retrieval contexts, and it also provides a sound inference mechanism to model the nonmonotonicity arising in changing retrieval contexts. Contextual knowledge for information retrieval can be extracted via efficient sequential pattern mining. We present a pattern taxonomy extraction model which efficiently performs the task of discovering descriptive frequent sequential patterns by pruning the noisy associations. This paper illustrates a novel approach of integrating the sequential data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. Initial experiments show that our belief revision logic and sequential pattern mining based intelligent information agents outperform the vector space model based information agents. Our work opens the door to the development of next generation of intelligent information agents to alleviate the information overload problem.
Original languageEnglish
Pages (from-to)773-789
Number of pages17
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number4
Publication statusPublished - 2007
Externally publishedYes


  • sequential pattern mining
  • nonmonotonic reasoning
  • belief revision
  • information agents


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