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Advanced document retrieval techniques for patent research

Author

Listed:
  • Ryley, James F.
  • Saffer, Jeff
  • Gibbs, Andy

Abstract

Latent semantic indexing (LSI) can be used in patent searching to overcome drawbacks of Boolean searching and to give more accurate retrieval. LSI combines the vector space model (VSM) of document retrieval with single value decomposition (SVD), using linear algebra techniques to uncover word relationships in the text. Results can be enhanced by using text clustering and tailoring SVD parameters to the specific corpus, in this case, patents, and by employing techniques to address ambiguities in language.

Suggested Citation

  • Ryley, James F. & Saffer, Jeff & Gibbs, Andy, 2008. "Advanced document retrieval techniques for patent research," World Patent Information, Elsevier, vol. 30(3), pages 238-243, September.
    • Handle: RePEc:eee:worpat:v:30:y:2008:i:3:p:238-243
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    Cited by:

    1. Martin G. Moehrle & Jan M. Gerken, 2012. "Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 805-826, June.

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