Active knowledge mining for intelligent web page management

Hiroshi Ishikawa, Manabu Ohta, Shohei Yokoyama, Takuya Watanabe, Kaoru Katayama

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


In this paper, we describe active knowledge mining approaches to intelligent Web page management. Through application of them to operational Web systems, we have found them very effective as follows: First, we describe an adaptable recommendation system called the system L-R, which constructs user models as knowledge by classifying the Web access logs and by extracting access patterns based on the transition probability of page accesses and recommends the relevant pages to the users based both on the user models and the Web site structures. We have evaluated the prototype system and have successfully obtained the positive effects of the mined knowledge. Second, we describe another approach to constructing user models, which clusters Web access logs for operational systems based on access patterns. In this case, the knowledge helps to discover unexpected access paths corresponding to ill-formed Web site design. Third, we have successfully identified undiscovered research issues such as dynamic page recommendation when we have attempted to mine Web usage logs for operational systems.

Original languageEnglish
Pages (from-to)975-983
Number of pages9
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2773 PART 1
Publication statusPublished - Jan 1 2003
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: Sept 3 2003Sept 5 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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