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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

25 Scopus Citations
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  • Xin Li
  • Hsinchun Chen
  • Zhu Zhang
  • Jiexun Li
  • Jay Nunamaker

Related Research Unit(s)


Original languageEnglish
Pages (from-to)129-153
Journal / PublicationJournal of Management Information Systems
Issue number1
Publication statusPublished - 2009



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.

Research Area(s)

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

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