Semantic log analysis based on a user query behavior model

We propose a novel log analysis method to capture the semantic relations among words appearing in Web search logs. Our method focuses on the reciprocal relations among a user's intentions, stages of information need, and query behavior in seeking information via a search engine. The approach works because it is based on the assumption that a user's intentions in each query can be derived as a model on the basis of his stage of information need and query behavior, through multiple empirical observations of search logs. The user's intentions drive user to change the words in each successive queries and can thus be used to clarify the semantic relations among words. As a result, this method has the advantage of capturing the semantic relations among words without requiring either manual or natural language processing. Our experimental results indicate that semantic relations could successfully be derived from search logs, confirming that an ontology and thesaurus could be constructed automatically.