Managing knowledge in light of its evolution process: An empirical study on citation network-based patent classification

Xin Li, Hsinchun Chen, Zhu Zhang, Jiexun Li, Jay Nunamaker

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Knowledge management is essential to modern organizations. Due to the information overload problem, managers are facing critical challenges in utilizing the data in organizations. Although several automated tools have been applied, previous applications often deem knowledge items independent and use solely contents, which may limit their analysis abilities. This study focuses on the process of knowledge evolution and proposes to incorporate this perspective into knowledge management tasks. Using a patent classification task as an example, we represent knowledge evolution processes with patent citations and introduce a labeled citation graph kernel to classify patents under a kernel-based machine learning framework. In the experimental study, our proposed approach shows more than 30 percent improvement in classification accuracy compared to traditional content-based methods. The approach can potentially affect the existing patent management procedures. Moreover, this research lends strong support to considering knowledge evolution processes in other knowledge management tasks.

Original languageEnglish (US)
Pages (from-to)129-154
Number of pages26
JournalJournal of Management Information Systems
Volume26
Issue number1
DOIs
StatePublished - Jul 1 2009

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Keywords

  • Citation analysis
  • Classification
  • Kernel-based method
  • Knowledge management
  • Machine learning
  • Patent management

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management

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