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.

Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one’s curiosity about a countless number of things. In this paper, we seek to understand YA’s knowledge sharing activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users ’ interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics

Data is more than knowledge: implications of the reversed knowledge hierarchy for knowledge management and organizational memory In knowledge management literature it is often pointed out that it is important to distinguish between data, information and knowledge. The generally accepted view sees data as simple facts that become information as data is combined into meaningful structures, which subsequently become knowledge as meaningful information is put into a context and when it can be used to make predictions. This view sees data as a prerequisite for information, and information as a prerequisite for knowledge. In this paper, I will explore the conceptual hierarchy of data, information and knowledge, showing that data emerges only after we have information, and that information emerges only after we already have knowledge. The reversed hierarchy of knowledge is shown to lead to a different approach in developing information systems that support knowledge management and organizational memory. It is also argued that this difference may have major implications for organizational flexibility and renewal. 2

As the basis of value creation increasingly depends on the leverage of the intangible assets of firms, Knowledge Management Systems (KMS) are emerging as powerful sources of competitive advantage. However, the general recognition of the importance of such systems seems to be accompanied by a technology-induced drive to implement systems with inadequate consideration of the fundamental knowledge problems that the KMS are likely to solve. This paper contributes to the stream of research on Knowledge Management Systems by proposing an inductively developed framework for this important class of information systems, classifying KMS based on the locus of the knowledge and the a-priori structuring of contents. This framework provides a means to explore issues related to KMS and unifying dimensions underlying different types of KMS. The contingencies that we discuss: the size and diversity of networks, the maintenance of knowledge flows and the long term effects of the use of KMS provide a window into work in a number of reference disciplines that would enrich the utility of KMS and also open up fruitful areas for future research.

Organizations are attempting to leverage their knowledge resources by employing knowledge management (KM) systems, a key form of which are electronic knowledge repositories (EKRs). A large number of KM initiatives fail due to reluctance of employees to share knowledge through these systems. Motivated by such concerns, this study formulates and tests a theoretical model to explain EKR usage by knowledge contributors. The model employs social exchange theory to identify cost and benefit factors affecting EKR usage, and social capital theory to account for the moderating influence of contextual factors. The model is validated through a large-scale survey of public sector organizations. The results reveal that knowledge self-efficacy and enjoyment in helping others significantly impact EKR usage by knowledge contributors. Contextual factors (generalized trust, pro-sharing norms, and identification) moderate the impact of codification effort, reciprocity, and organizational reward on EKR usage, respectively. It can be seen that extrinsic benefits (reciprocity and organizational reward) impact EKR usage contingent on particular contextual factors whereas the effects of intrinsic benefits (knowledge self-efficacy and enjoyment in helping others) on EKR usage are not moderated by contextual factors. The loss of knowledge power and image do not appear to impact EKR usage by knowledge contributors. Besides contributing to theory building in KM, the results of this study inform KM practice.

Bringing communities of buyers and sellers together in the arena of electronic commerce stimulates three major potentials: the building of trust, the collection and effective use of community knowledge and the economic impacts of accumulated buying power. In this context, we introduce the concept of Virtual Communities of Transaction and review important personalization approaches which we may utilize in their design: collaborative filtering, data mining, and techniques to optimize the user interface and the underlying product offerings. The key contributions of this paper are the elaboration of Virtual Communities, the presentation of a categorization scheme for different types of communities, the identification of classes of member profiles, and the innovative concept of community products. We conclude with the case of the Amazon.com Recommendation Center to illustrate key design ideas and discuss an evolutionary application, the Participatory Product Catalog. 1.

Modern commercial organisations are facing pressures which have caused them to lose personnel. When they lose people, they also lose their knowledge. Organisations also have to cope with the internationalisation of business forcing collaboration and knowledge sharing across time and distance. Knowledge Management (KM) claims to tackle these issues. This paper looks at an area where KM does not offer sufficient support, that is, the sharing of knowledge that is not easy to articulate.

For many, Tim Berners-Lee’s Semantic Web symbolizes the next generation in the evolution of the WWW. Its capability to represent rich information and enable machines to interpret that information may mean that next generation intelligent searching and information filtering services will make our lives better. However, what if not enough

This paper investigates such systems in two parts. We first explore the expert finding problem in depth, review and analyze existing systems in this domain, and suggest a domain model that can serve as a framework for design and development decisions. Based on our analyses of the problem and solution spaces, we then bring to light the gaps that remain to be addressed. Finally, we present our approach called DEMOIR, which is a modular architecture for expert finding systems that is based on a centralized expertise modeling server while also incorporating decentralized components for expertise information gathering and exploitation

Based on a study of knowledge transfer within more than 15 industries, across three forms of governance, and between both domestic and international R&D partners, knowledge transfer success was found to be associated with several key variables, and to hinge upon (a) both R&D units’ understanding where the desired knowledge resides within the source, (b) the extent to which the parties share similar knowledge bases, and the extent of interactions between the source and the recipient to (c) transfer the knowledge and (d) participate in an articulation process through which the source’s knowledge is made accessible to the recipient.