Applying taxonomic knowledge to Bayesian belief network for personalized search
Jae‐Won Lee, Hanjoon Kim, Sang-goo Lee · 2010
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.