Abstract
As the web grows larger, knowledge acquisition from the web has gained increasing attention. Web search logs are getting a lot more attention lately as a source of information for applications such as targeted advertisement and query suggestion. However, it may not be appropriate to use queries themselves because query strings are often too heterogeneous or inspecifiec to characterize the interests of the search user population. the web. Thus, we propose to use web clickthrough logs to learn semantic categories. We also explore a weakly-supervised label propagation method using graph Laplacian to alleviate the problem of semantic drift. Experimental results show that the proposed method greatly outperforms previous work using only web search query logs.
