Information retrieval and artificial intelligence
Artificial Intelligence - Special issue on applications of artificial intelligence
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Investigative Profiling with Computer Forensic Log Data and Association Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Ontology-Based Web Mining Model: Representations of User Profiles
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Mining Ontology for Automatically Acquiring Web User Information Needs
IEEE Transactions on Knowledge and Data Engineering
How are we searching the world wide web?: a comparison of nine search engine transaction logs
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
A knowledge retrieval model using ontology mining and user profiling
Integrated Computer-Aided Engineering
Concept-Based, Personalized Web Information Gathering: A Survey
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Ontology mining for semantic interpretation of information needs
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Ontology based web mining for information gathering
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
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The traditional techniques rely on human effort to acquire training sets, which is expensive and inefficient. In this paper we present an alternative method to automatically acquire training sets without heavy investment of user efforts. The proposed method tends to fill a gap for effectiveness of using Web data in Web mining, and contributes to Web information gathering. The evaluation shows that the method is adequate to yield an promising achievement.