On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Communications of the ACM
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Using ODP metadata to personalize search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized Search Based on User Search Histories
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Classifying search engine queries using the web as background knowledge
ACM SIGKDD Explorations Newsletter
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Supporting intelligent Web search
ACM Transactions on Internet Technology (TOIT)
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
Keyword-based search returns its results without concern for the information needs of users at a particular time. In general, search queries are too short to represent what users want, and thus it is necessary to more exactly represent the users' intended semantics. Hence, our goal is to enrich the semantics of user-specific information (e.g., users' queries and preferences) with a set of concepts for personalized search. To achieve this goal, we adopt a Bayesian belief network (BBN) as a strategy for personalized search since the Bayesian belief network provides a clear formalism for mapping user-specific information to its corresponding concepts. Nevertheless, as the concept layer of the Bayesian belief network consists of only index terms extracted from documents, it does not use domain knowledge which is required for computers to understand the intended semantics of queries. Thus, we extend the Bayesian belief network to represent the semantics of user-specific information as concepts (not index terms). The concepts are extracted from a taxonomic knowledge base such as the Open Directory Project Web directory. In our experiments, we have shown that the extended Bayesian belief network using taxonomic knowledge significantly outperforms the conventional methods for personalized search.