Web Information Extraction and User Modeling: Towards Closing the Gap

Web search engines have become the primary method of accessing information on the web. Billions of queries are submitted to major web search engines, reflecting a wide range of information needs. While significant progress has been made on improving the relevance of the results, web search process often remains a frustrating experience. At the same time, web information extraction has seen tremendous progress, such that knowledge bases of millions of facts extracted from the web are now a reality. Yet it is not clear how effectively these knowledge bases support common user information needs. We posit that a key for web information extraction to significantly impact the web search experience is to connect the extraction process with user modeling, particularly with automatic methods for inferring user information needs and anticipated interaction patterns. In this paper we overview some recent efforts for user modeling and inferring user preferences in the context of closing the gap between web information extraction and user modeling.