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Weak signal identification with semantic web mining

 
: Thorleuchter, Dirk; Poel, Dirk van den

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Preprint urn:nbn:de:0011-n-2581451 (250 KByte PDF)
MD5 Fingerprint: b99c14dc5e7ba4971d7f5d77116b0af8
Created on: 21.9.2013


Expert Systems with Applications 40 (2013), No.12, pp.4978-4985
ISSN: 0957-4174
English
Journal Article, Electronic Publication
Fraunhofer INT ()
weak signal; Ansoff; Latent Semantic Indexing; SVD; web mining

Abstract
We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.

: http://publica.fraunhofer.de/documents/N-258145.html