Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250400136-10.doi: 10.11896/jsjkx.250400136

• Artificial Intelligence • Previous Articles     Next Articles

Triple Extraction Based on Pixel Difference Convolutional Network and Attention Mechanism

FENG Guang1, LIN Jianzhong1, ZHONG Ting1, ZHOU Yuanhua1, ZHENG Runting2, LIU Tianxiang2   

  1. 1 School of Automation,Guangdong University of Technology,Guangzhou 510006,China
    2 School of Computer Science,Guangdong University of Technology,Guangzhou 510006,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:FENG Guang,born in 1973,Ph.D,professor-level senior experimenter,and master's supervisor.His main research interests include classroom streaming,big data and artificial intelligence.
  • Supported by:
    Key Program of the National Natural Science Foundation of China(62237001) and Youth Project of Guangdong Provincial Philosophy and Social Sciences(GD23YJY08).

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Abstract: Extracting relational triples from unstructured text is crucial for building knowledge graphs.Traditional models often suffer from relational redundancy and overlap due to insufficient contextual information capture.To tackle this,this paper proposes a relation extraction model based on pixel difference convolutional networks and attention mechanisms.It uses BERT to encode sentence representations and generate subject,object,and relation markers.By capturing contextual semantic information from local and global perspectives,the proposed model enhances entity pair interaction,reduces error propagation via bidirectional extraction,and strengthens sentence-entity connections through conditional normalization.A double imitation mechanism is employed to predict triples.Experiments on NYT and WebNLG datasets show the proposed model outperforms baselines in extracting overlapping triples.

Key words: Pixel difference convolution, Convolutional block attention module, Biaffine attention mechanism, Overlapping triples, Conditional normalization

CLC Number: 

  • TP391.1
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