Selecting Task-Relevant Sources for Just-in-Time Retrieval

\Just-in-time" information systems monitor their users' tasks, anticipate task-based information needs, and proactively provide their users with relevant information. The e ectiveness of such systems depends both on their capability to track user tasks and on their ability to retrieve information that satis es taskbased needs. The Watson system (Budzik et al. 1998; Budzik & Hammond 1999) provides a framework for monitoring user tasks and identifying relevant content areas, and uses this information to generate focused queries for general-purpose search engines and for specialized search engines integrated into the system. The proliferation of specialized search engines and information repositories on the Web provides a rich source of additional information pre-focused for a wide range information needs, potentially enabling just-in-time systems to exploit that focus by querying the most relevant sources. However, putting this into practice depends on having general scalable methods for selecting the best sources to satisfy the user's needs. This paper describes early research on augmenting Watson with a general-purpose capability for automatic information source selection. It presents a source selection method that has been integrated into Watson and discusses general issues and research directions for task-relevant source selection.