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JGURD: joint gradient update relational direction-enhanced method for knowledge graph completion.

Lianhong Ding1, Mengxiao Li1, Shengchang Gao2

  • 1School of Information, Beijing Wuzi University, Beijing, China.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary

This study introduces JGURD, a new framework for knowledge graph completion (KGC) that effectively uses relational direction. JGURD improves accuracy by jointly updating entities and relationships, outperforming existing methods.

Keywords:
Encoder-decoderGraph neural networksJoint gradient updateKnowledge graph completionLink predictionMulti-relational graphRelational direction

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Area of Science:

  • Artificial Intelligence
  • Data Science

Background:

  • Multi-relational knowledge graphs (KGs) are crucial for representing complex data.
  • Existing knowledge graph completion (KGC) methods often fail to fully leverage relational direction and correlation information.

Purpose of the Study:

  • To propose a novel KGC framework, JGURD, that addresses the limitations of current methods by incorporating relational direction.
  • To enhance the utilization of relation correlation information in KGC tasks.

Main Methods:

  • JGURD employs an encoder-decoder structure for Joint Gradient Update with Relational Direction.
  • It integrates graph convolutional networks (GCNs) with KG embedding methods for joint entity and relationship updates.
  • A relation correlation graph (RCG) is constructed and processed by a GCN-based multi-relation encoder with attention to capture graph structures.

Main Results:

  • JGURD demonstrated superior performance compared to the HHAN-KGC baseline.
  • Significant improvements were observed on the FB15k dataset, with Hits@3 increasing by 6.8% and MRR by 8.9%.

Conclusions:

  • The proposed JGURD framework effectively utilizes relational direction and improves knowledge graph completion.
  • The method offers enhanced interpretability and adaptability through its flexible decoder design.