Finding relevant information of certain types from enterprise data

Search over enterprise data is essential to every aspect of an enterprise because it helps users fulfill their information needs. Similar to Web search, most queries in enterprise search are keyword queries. However, enterprise search is a unique research problem because, compared with the data in traditional IR applications (e.g., text data), enterprise data includes information stored in different formats. In particular, enterprise data include both unstructured and structured information, and all the data center around a particular enterprise. As a result, the relevant information from these two data sources could be complementary to each other. Intuitively, such integrated data could be exploited to improve the enterprise search quality. Despite its importance, this problem has received little attention so far. In this paper, we demonstrate the feasibility of leveraging the integrated information in enterprise data to improve search quality through a case study, i.e., finding relevant information of certain types from enterprise data. Enterprise search users often look for different types of relevant information other than documents, e.g., the contact information of per- sons working on a product. When formulating a keyword query, search users may specify both content requirements, i.e., what kind of information is relevant, and type requirements, i.e., what type of information is relevant. Thus, the goal is to find information relevant to both requirements specified in the query. Specifically, we formulate the problem as keyword search over structured or semistructured data, and then propose to leverage the complementary unstructured information in the enterprise data to solve the problem. Experiment results over real world enterprise data and simulated data show that the proposed methods can effectively exploit the unstructured information to find relevant information of certain types from structured and semistructured information in enterprise data.