Mining Conference Proceedings for Corporate Technology Knowledge Management

International Journal of Innovation and Technology Management, Vol. 4, (No. 2), 103-119, 2007.

Robert J. Watts (US Army Tank and Automotive Research, Development & Engineering Center)
Alan L. Porter (Search Technology, Inc. and Technology Policy & Assessment Center, Georgia Tech)

Published: 2007


Content

The PDF linked above is a preprint of this article as submitted for consideration in International Journal of Innovation and Technology Management © 2007 World Scientific Publishing Company, www.worldscinet.com/ijitm.  The published version is available at this link:
International Journal of Innovation and Technology Management, Vol. 4, No. 2 (2007) 103-119

Abstract: An organization’s knowledge gained through technical conference attendance is generally isolated to the individual(s) attending the event. The aggregate corporate knowledge is extremely limited, unless the organization institutes a process to document and transfer that knowledge to the organization. Even if such a process exists, the knowledge gains are limited to the experiences and communication skills of the individuals attending the conference. Many conference proceedings are now published and provided to attendees in electronic format, such as on CD-ROM and/or published on the internet, such as IEEE conference proceedings. These proceedings provide a rich repository that can be mined. Paper abstract compilations reflect "hot topics," as defined by the researchers in the field, and delineate the technical approaches being applied. R&D profiling can more fully exploit recorded conference proceedings’ research to enhance corporate knowledge. This paper illustrates the potential in profiling conference proceedings through use of WebQL information retrieval and TechOasis (VantagePoint) text mining software. It shows how tracking research patterns and changes over a sequence of conferences can illuminate R&D trends, map dominant issues, and spotlight key research organizations.

Keywords: Technology intelligence; data mining

 

 

 

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