A knowledge grid model and platform for global knowledge sharing

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

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Cultural Impact on Global Knowledge Sharing

Information Resources Management ◽

10.4018/978-1-59904-102-5.ch012 ◽

2011 ◽

pp. 262-281

Author(s):

Timothy Shea ◽

David Lewis

Keyword(s):

Knowledge Management ◽

Knowledge Sharing ◽

Cultural Differences ◽

National Culture ◽

Decision Makers ◽

Global Environment ◽

Global Knowledge ◽

Cultural Impact ◽

Four Dimensions ◽

Types Of Knowledge

This chapter introduces how culture impacts global knowledge sharing. Effective knowledge sharing (KS), one of the four interdependent dimensions of knowledge management (KM), is particularly important in today’s global environment in which national cultural differences are negotiated all the time. Knowledge sharing is described along six dimensions and national culture along four dimensions. A model is presented, which provides guidelines for effectively sharing different types of knowledge within different cultural environments. Several examples are presented to illustrate the model’s effectiveness. Using the model as a guide, the authors believe that decision makers will increase the chances that information and knowledge will be shared successfully.

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Five steps to creating a global knowledge-sharing system: Siemens' ShareNet

Academy of Management Perspectives ◽

10.5465/ame.2005.16962590 ◽

2005 ◽

Vol 19 (2) ◽

pp. 9-23 ◽

Cited By ~ 51

Author(s):

Sven C. Voelpel ◽

Malte Dous ◽

Thomas H. Davenport

Keyword(s):

Knowledge Sharing ◽

Global Knowledge

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Assessing Knowledge Sharing in Cancer Screening Among High-, Middle-, and Low-Income Countries: Insights From the International Cancer Screening Network

Journal of Global Oncology ◽

10.1200/jgo.19.00202 ◽

2019 ◽

pp. 1-8

Author(s):

Douglas M. Puricelli Perin ◽

Amanda L. Vogel ◽

Stephen H. Taplin

Keyword(s):

Cancer Screening ◽

Knowledge Sharing ◽

Low Income ◽

National Cancer Institute ◽

Online Survey ◽

Research Evaluation ◽

Cancer Control ◽

Low Income Countries ◽

Years Of Experience ◽

Global Knowledge

PURPOSE As the global burden of cancer rises, global knowledge sharing of effective cancer control practices will be critical. The International Cancer Screening Network (ICSN) of the US National Cancer Institute facilitates knowledge sharing to advance cancer screening research and practice. Our analysis assessed perceptions of ICSN’s value and knowledge sharing in cancer screening among participants working in high-income countries (HICs) and low- and middle-income countries (LMICs). METHODS In 2018, the National Cancer Institute fielded a self-administered, online survey to 665 ICSN participants from both HICs and LMICs. RESULTS Two hundred forty-three individuals (36.5%) completed the full survey. LMIC participants engaged in more diverse screening activities and had fewer years of experience (13.5% with more than 20 years of experience v 31%; P = .048) in screening and were more interested in cervical cancer (76.9% v 52.6%; P = .002) than HIC participants. However, both groups spent most of their time on research (30.8% LMIC v 36.6% HIC; P = .518) and agreed that the ICSN biennial meeting enabled them to learn from the experiences of both higher-resource (88.2% v 75.7%; P = .122) and lower-resource (61.8% v 68.0%; P = .507) settings. ICSN helped them form new collaborations for research and implementation (55.1% v 58.2%; P = .063); informed advances in research/evaluation (71.4% v 68.0%; P = .695), implementation (59.2% v 47.9%; P = .259), and policies in their settings (55.1% v 48.0%; P = .425); and provided the opportunity to contribute their knowledge and expertise to assist others (67.3% v 71.1%; P = .695). CONCLUSION Findings suggest that HIC and LMIC participants benefit from knowledge sharing at ICSN meetings although their interests, backgrounds, and needs differ. This points to the importance of international research networks that are inclusive of HIC and LMIC participants in cancer control to advance knowledge and effective practices globally.

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Adopting the Grid Computing & Semantic Web Hybrid for Global Knowledge Sharing

Knowledge Management, Organizational Memory and Transfer Behavior ◽

10.4018/978-1-60566-140-7.ch020 ◽

2011 ◽

pp. 365-381

Author(s):

Mirghani Mohamed ◽

Michael Stankosky ◽

Vincent Ribière

Keyword(s):

Knowledge Management ◽

Semantic Web ◽

Grid Computing ◽

Knowledge Sharing ◽

Information Overload ◽

Global Knowledge ◽

Shared Resources ◽

Semantic Grid ◽

Early Results ◽

Wide Range

The purpose of this chapter is to examine the requirements of Knowledge Management (KM) services deployment in a Semantic Grid environment. A wide range of literature on Grid Computing, Semantic Web, and KM have been reviewed, related, and interpreted. The benefits of the Semantic Web and the Grid Computing convergence have been investigated, enumerated and related to KM principles in a complete service model. Although the Grid Computing model significantly contributed to the shared resources, most of KM tools obstacles within the grid are to be resolved at the semantic and cultural levels more than at the physical or logical grid levels. The early results from academia, where grid computing still in testing phase, show a synergy and the potentiality of leveraging knowledge, especially from voluminous data, at a wider scale. However, the plethora of information produced in this environment will result in a serious information overload, unless proper standardization, automated relations, syndication, and validation techniques are developed.

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