Personal ontologies for web navigation
Proceedings of the ninth international conference on Information and knowledge management
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
PVA: A Self-Adaptive Personal View Agent
Journal of Intelligent Information Systems
Index Structures for Information Filtering Under the Vector Space Model
Proceedings of the Tenth International Conference on Data Engineering
Improving Category Specific Web Search by Learning Query Modifications
SAINT '01 Proceedings of the 2001 Symposium on Applications and the Internet (SAINT 2001)
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The explosive growth of information on the web demands effective intelligent search and filtering methods. Consequently, techniques have been developed that extract conceptual information from the document and use the conceptual information in the user profile to form part of the user's information intent from his/her query. In a similar vein, we build the profile without user interaction, automatically monitoring the user's browsing habits. These profiles, in turn, are used to automatically learn the semantic context of user's information need. These sets of categories can serve as a context to disambiguate the words in the user's query. In this paper, we present a framework for assisting the user in one of the most difficult information retrieval tasks, i.e., that of formulating an effective search query. Our experimental results show that implicit measurements of user interests, combined with the semantic knowledge embedded in a concept hierarchy, can be used effectively to infer the user context and to improve the results of information retrieval.