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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 287-291 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.132

Context Modeling and Measuring for Context Aware Knowledge Management

Ke Ning and David O’Sullivan

Abstract—To fully understand and to better reuse knowledge, it’s necessary to correlate knowledge with the context under which the knowledge is generated and managed. However, it remains a challenge to support context awareness in knowledge management system. This paper tries to overcome the difficulties by introducing key enabling technologies for context modeling and measuring: The knowledge context ontology correlates knowledge and its contexts through a high level activity based model is defined; The algorithm for Context similarity measurement is designed by exploiting the hierarchical domain structure defined in the context model.

Index Terms—Context aware, context modeling, knowledge management, similariy measure.

Ke Ning is with High-tech Industrial Park, Nanshan, Shenzhen, China(email: ke_ning@kingdee.com).
David O’Sullivan is with National University of Ireland, Galway,Ireland(email: dos@nuigalway.ie)

[PDF]

Cite: Ke Ning and David O’Sullivan, "Context Modeling and Measuring for Context Aware Knowledge Management," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 287-291, 2012.

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