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

Relational direction plays an important role in multi-relational knowledge graphs (KGs). Current knowledge graph completion (KGC) methods suffer from insufficient utilization of relation correlation information. To address this issue, this article proposes a novel KGC framework, namely JGURD, which...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Lianhong Ding, Mengxiao Li, Shengchang Gao, Juntao Li, Ruiping Yuan, Jianye Yu
Format: Artigo
Sprache:Inglês
Veröffentlicht: PeerJ Inc. 2025-04-01
Schriftenreihe:PeerJ Computer Science
Schlagworte:
Knowledge graph completion
Graph neural networks
Joint gradient update
Link prediction
Relational direction
Encoder-decoder
Online-Zugang:https://peerj.com/articles/cs-2808.pdf
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