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  • × author_ss:"Budzik, J."
  1. Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001) 0.00 
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  2. Budzik, J.; Hammond, K.: Watson : anticipating and contextualizing information needs (1999) 0.00 
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    Abstract
    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
  3. Budzik, J.; Hammond, K.: Q&A: a system for the capture, organization and reuse of expertise (1999) 0.00 
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    Abstract
    It is a time-consuming and difficult task for an individual, a group, or an organization to systematically express and organize their expertise so it can be captured and reused. Yet the expertise of individuals within an organization is perhaps its most valuable resource. Q&A attempts to address this tension by providing an environment in which textual representations of expertise are captured as a byproduct of using the system as a semiautomatic question answering intermediary. Q&A mediates interactions between an expert and a question-asking user. It uses its experience referring questions to expert users to answer new questions by retrieving previously answered ones. If a user's question is not found within the collection of previously answered questions, Q&A suggests the set of experts who are most likely to be able to answer the question. The system then gives the user the option of passing a question along to one or more of these experts. When an expert answers a user's question, the resulting question answer pair is captured and indexed under a topic of the expert's choice for later use, and the answer is sent to the user. Unlike previous work on question-answering systems of this sort, Q&A does not assume a fixed hierarchy of topics. Rather, experts build the hierarchy themselves, as their corpus of questions grows. One of the main contributions of this work is a set of techniques for managing the emerging organization of textual representations of expertise over time by mediating the negotiation of shared representations among multiple experts

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