Ontology-Based hazard information extraction from chinese food complaint documents

  • Authors:

  • Xiquan Yang;Rui Gao;Zhengfu Han;Xin Sui

  • Affiliations:

  • School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin, China;School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin, China;School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin, China;School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin, China

  • Venue:
  • ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
  • Year:
  • 2012

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Abstract

Ensuring food safety has become a global research subject these years. In this paper, a knowledge model of domain ontology with the aim of hazard information extraction from Chinese food complaint documents has been designed based on ontology theory. Two components are essential to this model the learning model and the extraction model. In the learning model, we propose the algorithms of seed words selection and related words generation. In the extraction model we propose the algorithms of hazard information extraction and modifying related words. We compare the results of our method with the method of traditional ontology based information extraction and traditional information extraction. The results show that the method we proposed has better indexes.