Artificial Intelligence Lab, Department of MIS, University of Arizona / Observations of Information Retrieval (IR) system user experiences reveal a strong desire for collaborative search while at the same time suggesting that collaborative capabilities are rarely, and then only in a limited fashion, supported by current searching and visualization tools. Equally interesting is the fact that observations of user experiences with Group Support Systems (GSS) reveal that although access to external information and the ability to search for relevant material is often vital to the progress of GSS sessions, integrated support for collaborative searching and visualization of results is lacking in GSS systems. After reviewing both user experiences described in IR and GSS literature and observing and interviewing users of existing IR and GSS commercial and prototype systems, the authors conclude that there is an obvious demand for systems supporting multi-user IR.. It is surprising to the authors that very little attention has been given to the common ground shared by these two important research domains. With this in mind, our paper describes how user experiences with IR and GSS systems has shed light on a promising new area of collaborative research and led to the development of a prototype that merges the two paradigms into a Collaborative Information Retrieval Environment (CIRE). Finally the paper presents theory developed from initial user experiences with our prototype and describes plans to test the efficacy of this new paradigm empirically through controlled experimentation.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Valuable criminal-justice data in free texts such as police narrative reports are currently difficult to be accessed and used by intelligence investigators in crime analyses. It would be desirable to automatically identify from text reports meaningful entities, such as person names, addresses, narcotic drugs, or vehicle names to facilitate crime investigation. In this paper, we report our work on a neural network-based entity extractor, which applies named-entity extraction techniques to identify useful entities from police narrative reports. Preliminary evaluation results demonstrated that our approach is feasible and has some potential values for real-life applications. Our system achieved encouraging precision and recall rates for person names and narcotic drugs, but did not perform well for addresses and personal properties. Our future work includes conducting larger-scale evaluation studies and enhancing the system to capture human knowledge interactively.

This paper reports the core findings of an international study that examined the perceptions of LIS community towards knowledge management inclusion in the LIS education. Taking the perspectives of members of international LIS communities, we try to identify the rationale for a paradigm shift in library education towards knowledge management. We also explore the perceptions of LIS community towards the nature and content of knowledge management program in the LIS education which best meets the challenges of the knowledge management work environment.

Artificial Intelligence Lab, Department of MIS, University of Arizona / In this era of the Internet and distributed multimedia computing, new and emerging classes of information technologies have swept into the lives of office workers and everyday people. As technologies and applications become more overwhelming, pressing, and diverse, several well-known information technology problems have become even more urgent. Information overload, a result of the ease of information creation and rendering via Internet and WWW, has become more evident in people’s lives. Significant variations of database formats and structures, the richness of information media text, audio, and video , and an abundance of multilingual information content also have created various information interoperability problems - structural interoperability, media interoperability, and multilingual interoperability.

Artificial Intelligence Lab, Department of MIS, University of Arizona / The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. Research in which properties of SOM were validated, called the Proximity and Size Hypotheses,is presented through a user evaluation study. Building upon the previous research in automatic concept generation and classification, it is demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. A positive relationship between the size of an SOM region and the number of documents contained in the region is also demonstrated.

This article first introduces the needs for a true interoperability environment that allows information and its context can be transfer across domains and applications. Then it describes an approach to build a RDF-based digital library system at the University of Arizona Libraries. The system architecture consists of a storage layer, a metadata management and semantic layer, a common service layer and an application layer. The system is an artifact of the RDF model and also uses an RDF database to facilitate internal management of information resources. The article presents background for a journal delivery project and reports the implementation of the journal application using Java Servlet technology. Issues about metadata management such as various metadata formats for specific communities, and MARC to DC mapping are discussed.

This paper reports the results of an exploratory qualitative study of how senior engineers in a research laboratory environment do personal information management (PIM). Responsive, semi-structured interviews were conducted with four senior engineers. Thirteen themes in four groupings emerged. The four groupings are: organization and retrieval, un-organized aspects, information keeping and preservation, and use of specialized tools. Themes not seen in other studies are: writing to remember and reporting to retrieve, and personal handbooks. The themes are described in detail. Implications for the design of information systems and future work are discussed.

In today's knowledge-based and competitive economy, research and development (R&D) efforts are increasingly geographically distributed across multiple institutions. This chapter explores the management of cognitive and affective trust and distrust within a new type of geographically distributed and multi-institutional R&D organization, called the conceptual organization. Both cognitive and affective trust are important to the conceptual organization because it relies on collaboration among individual members to achieve its goals, and collaboration is not possible without cognitive or affective trust. Data from a 2-year case study of a conceptual organization illuminates how the organization's structure, use of power and information and communications technology (I&CT) shape and are shaped by cognitive and affective trust. Tightly coupled collaboration appears to only emerge in situations where high cognitive and affective trust simultaneously exist, and no collaboration will emerge in situations with high cognitive and affective distrust exist. In comparison, limited collaboration emerges when affective trust and cognitive distrust exist concurrently, and competitive collaboration appears to emerge when cognitive trust and affective distrust exist concurrently. Different mechanisms to manage the collaboration emerged in these situations. These results help inform our understanding of cognitive and affective trust and distrust, and their management in R&D.

This paper is an abridged version of my thesis for an M.A. in Communications Studies granted by the University of Calgary. I presented it at the National Communication Association 2004 Convention in Chicago, IL (Session 1536). I decided not to have it published in the conference proceedings. / Based on the ethnographic data collected from the workplace of an academic library, I argue that workplace learning (WL) is a situated socio-cognitive process. It is expedited by knowledge management (KM), which is a collective effort to generate, share, and institutionalize work-related knowledge. KM is inherent in the face-to-face conversational interactions embedded in planned formal training, planned informal sharing, and spontaneous informal learning. When face-to-face interaction is not possible, KM is accomplished through textualization. It helps the members of the workplace acquire new work-related knowledge and integrate it to their common, contextualized knowledge base. The contents of the knowledge base are manifested in the membersâ professional practices and explicated by their professional/communal discourse. By virtue of their distinctive practices and discourse, the members form a community of practice (CoP) and gain their professional/communal identity. Whenever they engage in KM, perform their practices, and/or use their discourse, they authenticate their professional/communal identity and enact their CoP.

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