A framework to represent and mine knowledge evolution from Wikipedia revisions

  • Authors:
  • Xian Wu;Wei Fan;Meilun Sheng;Li Zhang;Xiaoxiao Shi;Zhong Su;Yong Yu

  • Affiliations:
  • Shanghai Jiao Tong University & IBM Research China, Shanghai, China;IBM T.J. Watson Research Center, New York, USA;Shanghai Jiao Tong University, Shanghai, China;IBM Research China, Beijing, China;University of Illinois at Chicago, Chicago, USA;IBM Research China, Beijing, China;Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • Proceedings of the 21st international conference companion on World Wide Web
  • Year:
  • 2012

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Abstract

State-of-the-art knowledge representation in semantic web employs a triple format (subject-relation-object). The limitation is that it can only represent static information, but cannot easily encode revisions of semantic web and knowledge evolution. In reality, knowledge does not stay still but evolves over time. In this paper, we first introduce the concept of "quintuple representation" by adding two new fields, state and time, where state has two values, either in or out, to denote that the referred knowledge takes effective or becomes expired at the given time. We then discuss a two-step statistical framework to mine knowledge evolution into the proposed quintuple representation. Utilizing extracted quintuple properly, it not only can reveal knowledge changing history but also detect expired information. We evaluate the proposed framework on Wikipedia revisions, as well as, common web pages currently not in semantic web format.