A framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps

Jin Xu; Zheng Li; Shuliang Li; Yanyan Zhang

doi:10.1117/12.2196528

6 July 2015 A framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps

Jin Xu, Zheng Li, Shuliang Li, Yanyan Zhang

Author Affiliations +

Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96311P (2015) https://doi.org/10.1117/12.2196528
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

Abstract

There is still a lack of effective paradigms and tools for analysing and discovering the contents and relationships of project knowledge contexts in the field of project management. In this paper, a new framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps under big data environments is proposed and developed. The conceptual paradigm, theoretical underpinning, extended topic model, and illustration examples of the ontology model for project knowledge maps are presented, with further research work envisaged.

Citation Download Citation

Jin Xu, Zheng Li, Shuliang Li, and Yanyan Zhang "A framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96311P (6 July 2015); https://doi.org/10.1117/12.2196528

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available