Temporal Company Relation Mining from the Web

  • Changjian Hu
  • Liqin Xu
  • Guoyang Shen
  • Toshikazu Fukushima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)


Relationships between companies and business events of company relation changing can act as the most important factors to conduct market analysis and business management. However, it is very difficult to track company relations and to detect the related events, because the relations change rapidly and continuously. Existing technologies are limited to analysis of non-temporal relations or temporal tracking web data in a single feature. Our work targets temporal and multiple-type relation mining. The proposed method automates relation instance extraction from the Web, temporal relation graph creation, and business event detection based on the created temporal graph. In the experiment, more than 70 thousand relation instances were extracted from 1.7 million English news articles, and a temporal relation graph of 255 companies from 1995 to 2007 was generated, and acquisition events and cases of competition relation evolution were detected. These results show our method is effective.


Business Activity Analysis Knowledge Acquisition Graph Theory 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Changjian Hu
    • 1
  • Liqin Xu
    • 1
  • Guoyang Shen
    • 1
  • Toshikazu Fukushima
    • 1
  1. 1.BeijingChina

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