While knowledge is viewed by many as an asset, it is often difficult to locate particular items within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together with the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required.

Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. People who make referrals fill key organizational roles that have been identified by CSCW and affiliated research. Expertise locating systems are not designed to replace people who fill these key organizational roles. Instead, expertise locating systems attempt to decrease workload and support people who have no other options. Recommendation systems are collaborative software that can be applied to expertise locating. This work describes a general recommendation architecture that is grounded in a field study of expertise locating. Our expertise recommendation system details the work necessary to fit expertise recommendation to a work setting. The architecture and implementation begin to tease apart the technical aspects of providing good recommendations from social and collaborative concerns.

This paper represents a step toward a theory of knowledge reusability, with emphasis on knowledge ma nagement systems and repositories, often called organizational memory systems. Synthesis of evidence from a wide variety of sources suggests four distinct types of knowledge reuse situations according to the knowledge reuser and the purpose of knowledge reuse. The types involve shared work producers, who produce knowledge they later reuse, shared work practitioners, who reuse each other’s knowledge contributions, expertise-seeking novices, and secondary knowledge miners. Each type of knowledge reuser has different requirements for knowledge repositories. Owing to how repositories are created, reusers ’ requirements often remain unmet. Repositories often require considerable rework to be useful for new reusers, but knowledge producers rarely have the resources and incentives to do a good job of repurposing knowledge. Solutions include careful use of incentives and human and technical intermediaries.

The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other's experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations. In HCI In The New Millennium, Jack Carroll, ed., Addison-Wesley, 2001 p. 2 of 21 Introduction The new millennium is an age of i...

A common method for finding information in an organization is to use social networks—ask people, following referrals until someone with the right information is found. Another way is to automatically mine documents to determine who knows what. Email documents seem particularly well suited to this task of “expertise location”, as people routinely communicate what they know. Moreover, because people explicitly direct email to one another, social networks are likely to be contained in the patterns of communication. Can these patterns be used to discover experts on particular topics? Is this approach better than mining message content alone? To find answers to these questions, two algorithms for determining expertise from email were compared: a contentbased approach that takes account only of email text, and a graph-based ranking algorithm (HITS) that takes account both of text and communication patterns. An evaluation was done using email and explicit expertise ratings from two different organizations. The rankings given by each algorithm were compared to the explicit rankings with the precision and recall measures commonly used in information retrieval, as well as the d ′ measure commonly used in signal-detection theory. Results show that the graph-based algorithm performs better than the content-based algorithm at identifying experts in both cases, demonstrating that the graph-based algorithm effectively extracts more information than is found in content alone.

Most cooperative work takes place in information-rich environments. However, studies of "information work" tend to focus on the decontextualized access and retrieval problems faced by individual information seekers. Our work is directed towards understanding how information management is seamlessly integrated into the course of everyday activities. Drawing on an ethnographic study of medical work, we explore the relationship between information and temporal coordination and discuss the role of temporal patterns or "rhythms" in providing individuals with the means to coordinate information and work.

carl.gutwin, reagan.penner, kevin.schneider @ usask.ca Open-source software development projects are almost always collaborative and distributed. Despite the difficulties imposed by distance, these projects have managed to produce large, complex, and successful systems. However, there is still little known about how open-source teams manage their collaboration. In this paper we look at one aspect of this issue: how distributed developers maintain group awareness. We interviewed developers, read project communication, and looked at project artifacts from three successful open source projects. We found that distributed developers do need to maintain awareness of one another, and that they maintain both a general awareness of the entire team and more detailed knowledge of people that they plan to work with. Although there are several sources of information, this awareness is maintained primarily through text-based communication (mailing lists and chat systems). These textual channels have several characteristics that help to support the maintenance of awareness, as long as developers are committed to reading the lists and to making their project communication public.

Previous research has documented the fragmented nature of software development work, with frequent interruptions and coordination. To explain this in more detail, we analyzed software developers ’ day-to-day information needs. We observed seventeen developers at a large software development company and transcribed their activities minute by minute in 90 minute sessions. We analyzed these logs for the information that developers sought, the sources that they used, and the situations that prevented information from being acquired. We identify twentyone information types and catalog the outcome and source when each type of information was sought. The most frequently sought information included awareness about artifacts and coworkers. The most often deferred searches included knowledge about design and program behavior, such as why code was written a particular way, what a program was supposed to do, and the cause of a program state. Developers often had to defer tasks because the only sources of knowledge were unavailable coworkers. 1.

This paper reports fieldwork from the electrical utilities industry, examining the suitability of current knowledge management perspectives to the day-to-day work of mobile staff. Reporting the results of the empirical study, we make a distinction between four aspects of local and mobile "knowledge management" as it took place in the mobile work setting: sharing, i.e., several parties exchange knowledge; indexing, i.e., one party explains to another what knowledge to retrieve; diagnosing, i.e., two parties make sense of how to interpret a situation, and; foreseeing, i.e., one party (or more) uses knowledge to project the future. We compare and contrast the empirical findings with current knowledge management perspectives, and outline an initial sketch of a framework for "practical knowledge management."

We consider a social network of software agents who assist each other in helping their users find information. Unlike in most previous approaches, our architecture is fully distributed and includes agents who preserve the privacy and autonomy of their users. These agents learn models of each other in terms of expertise (ability to produce correct domain answers) and sociability (ability to produce accurate referrals). We study our framework experimentally to study how the social network evolves. Specifically, we find that under our multiagent learning heuristic, the quality of the network improves with interactions; the quality is maximized when both expertise and sociability are considered; pivot agents further improve the quality of the network and have a catalytic effect on its quality even if they are ultimately removed. Moreover, the quality of the network improves when clustering decreases, reflecting the intuition that you need to talk to people outside your close circle to get the best information.