Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11): 170-175.doi: 10.11896/jsjkx.201100004

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Study on Multi-source Data Fusion Framework Based on Graph

KUANG Guang-sheng1,2, GUO Yan2, YU Xiao-ming2, LIU Yue2, CHENG Xue-qi2   

  1. 1 University of Chinese Academy of Sciences,Beijing 100049,China
    2 Key Laboratory of Network Data Science & Technology,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2020-11-02 Revised:2021-03-18 Online:2021-11-15 Published:2021-11-10
  • About author:KUANG Guang-sheng,born in 1995,postgraduate.His main research in-terests include natural language proces-sing and data fusion.
    GUO Yan,born in 1974,Ph.D,associate researcher.Her main research interests include network information acquisition and so on.
  • Supported by:
    National Key Research and Development Program of China(2017YFB0803302).

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Abstract: When analyzing various data in a given task,most of current researches only analyze single-source data and lack me-thods applied to multi-source data.But now data are becoming more abundant,therefore,this paper proposes a multi-source data fusion framework for fusing data from multiple network platforms.The data of the same platform contains text and various attri-butes,and there are also great differences in content and form among data of different platforms.Most existing network information mining methods only use part of the data in the same platform for analysis,and even ignore the interaction between the data of different platforms.Therefore,this paper proposes a data fusion framework,which can not only use more features of the same platform to improve the performance of a single platform,but also fuse the data features of different platforms to complement each other,thereby improving the performance of multiple platforms.This paper uses the task of event classification,and the abundant features effectively improve the F1 value,which verifies the effectiveness of the proposed multi-source data framework.

Key words: Fusion representation, Graph fusion, Multi-source

CLC Number: 

  • TP391
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