In this paper, we introduce a class of systems called Information Management Assistants (IMAs). IMAs automatically discover related material on behalf of the user by serving as an intermediary between the user and information retrieval systems. IMAs observe users interact with everyday applications and then anticipate their information needs using a model of the task at hand. IMAs then automatically fulfill these needs using the text of the document the user is manipulating and a knowledge of how to form queries to traditional information retrieval systems (e.g., Internet search engines, abstract databases, etc.). IMAs automatically query information systems on behalf of users as well as provide an interface by which the user can pose queries explicitly. Because IMAs are aware of the user's task, they can augment their explicit query with terms representative of the context of this task. In this way, IMAs provide a framework for bringing implicit task context to bear on servicing explicit information requests, significantly reducing ambiguity. IMAs embody a just-in-time information infrastructure in which information is brought to users as they need it, without requiring explicit requests. In this paper, we present our work on an architecture for this class of system, and our progress implementing Watson, a prototype of such a system. Watson observes users in word processing and Web browsing applications and uses a simple model of the user's tasks, knowledge of term importance, and an understanding of query generation to find relevant documents and service explicit queries. We close by discussing our experimental evaluations of the system.
|Original language||English (US)|
|Number of pages||14|
|Journal||Proceedings of the ASIS Annual Meeting|
|State||Published - Dec 1 1999|
ASJC Scopus subject areas
- Information Systems
- Library and Information Sciences