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Mining Ideas from Textual Information

Author

Listed:
  • D. THORLEUCHTER
  • D. VAN DEN POEL

    ()

  • A. PRINZIE
  • -

Abstract

This approach introduces idea mining as process of extracting new and useful ideas from unstructured text. We use an idea definition from technique philosophy and we focus on ideas that can be used to solve technological problems. The rationale for the idea mining approach is taken over from psychology and cognitive science and follows how persons create ideas. To realize the processing, we use methods from text mining and text classification (tokenization, term filtering methods, Euclidean distance measure etc.) and combine them with a new heuristic measure for mining ideas. As a result, the idea mining approach extracts automatically new and useful ideas from a user given text. We present these problem solution ideas in a comprehensible way to support users in problem solving. This approach is evaluated with patent data and it is realized as a web-based application, named 'Technological Idea Miner' that can be used for further testing and evaluation.

Suggested Citation

  • D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2009. "Mining Ideas from Textual Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/619, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:09/619
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    File URL: http://wps-feb.ugent.be/Papers/wp_09_619.pdf
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    Cited by:

    1. D. Thorleuchter & D. Van Den Poel, 2012. "Improved Multilevel Security with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/811, Ghent University, Faculty of Economics and Business Administration.
    2. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    3. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    4. D. Thorleuchter & D. Van Den Poel, 2012. "Technology Classification with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/814, Ghent University, Faculty of Economics and Business Administration.
    5. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.

    More about this item

    Keywords

    Idea Mining; Text Mining; Text Classification; Technology;

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