query taxonomy generation for web search
We propose an approach that organizes the search-result clusters into a hierarchical structure, called a query taxonomy, from the user's perspective. The proposed approach is based on an unsupervised classification method, which uses the dynamic Web as the training corpus. With query taxonomy, users can browse relevant Web documents more conveniently and comprehensibly. Our experimental results verify the feasibility and the effectiveness of the proposed approach to query taxonomy generation in Web search.
query taxonomy query taxonomies generation web web search search cluster hierarchic structure users users classification method dynamic training browse relevance documentation experimentation effect user