Items by Xin, Dong

Web search engines often federate many user queries to relevant structured databases. For example, a product related query might be federated to a product database containing their descriptions and specifications. The relevant structured data items are then returned to the user along with web search results. However, each structured database is searched in isolation. Hence, the search often produces empty or incomplete results as the database may not contain the required information to answer the query. In this paper, we propose a novel integrated search architecture. We establish and exploit the relationships between web search results and the items in structured databases to identify the relevant structured data items for a much wider range of queries. Our architecture leverages existing search engine components to implement this functionality at very low overhead. We demonstrate the quality and efficiency of our techniques through an extensive experimental study.

Tasks recognizing named entities such as products, people names, or locations from documents have recently received significant attention in the literature. Many solutions to these tasks assume the existence of reference entity tables. An important challenge that needs to be addressed in the entity extraction task is that of ascertaining whether or not a candidate string approximately matches with a named entity in a given reference table. Prior approaches have relied on string-based similarity which only compare a candidate string and an entity it matches with. In this paper, we exploit web search engines in order to define new similarity functions. We then develop efficient techniques to facilitate approximate matching in the context of our proposed similarity functions. In an extensive experimental evaluation, we demonstrate the accuracy and efficiency of our techniques.

Our goal is to enable users to efficiently and effectively search the web for informational queries and browse the content relevant to their queries. We achieve a unique “portal” like functionality for each query by effectively exploiting structured and unstructured content. We exploit existing structured data to identify and return per query a set of highly relevant entities such as people, products, movies, locations. Further, we are able to return additional information about the retrieved entities, such as categories, refined queries, and web sites which provide detailed information for each entity. The combination of search results and structured data creates a rich set of results, for the user to focus on and refine their search.