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Evaluation of Knowledge Management Technologies for the Support of Technology Forecasting

Paper, was published by Stefan Smolnik, Marc Henselewski, and Gerold Riempp at 2006-01-04

This paper investigates to what extent knowledge management technologies improve the technology forecasting process.

External Links: IEEE record and ACM record


Today’s business environment is characterized by highly transparent markets and global competition. Technology life cycles are decreasing due to the fast pace at which development of new technologies is progressing. To compete in this environment, it is necessary to identify upcoming innovations and trends as early as possible to decrease uncertainty, implement technology leadership, and create competitive advantage. In a parallel development, the amount of information available is already vast and increasing daily. With a growing number of features for innovation in technology, each contributing a new need for analysis, technology forecasting has become increasingly challenging. The goal of our paper is to investigate to what extent knowledge management technologies support and improve the technology forecasting process to face the aforementioned problems successfully. Consequently, we will develop a characterization scheme which works as a framework for the subsequent evaluation of knowledge management technologies and apply this to a real world case.

 

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As a former information scientist, I am fascinated since 1999 by the capabilities for building Topic Maps-based knowledge systems having the potential to augment human mind. One can model arbitrary knowledge organization systems, deal with semantic heterogeneity, collocate all facts about one subject in one logical place, and with TMQL have semantic retrieval on federated semantic networks. Therefore I expect bright prospects for business concepts building on the exchange of such knowledge snippets via semantic knowledge services.

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