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<p>Knowledge graph embedding (KGE) methods map entities and relations from knowledge graphs to continuous vector spaces, simplifying their representations and enhancing performance across various tasks (e.g., link prediction, question answering). As concerns about personal privacy rise, machine unlearning (MU), an emerging artificial intelligence technology that enables models to eliminate the influence of specific data, has garnered increasing attention from the academic community. The existing KGE unlearning works mainly achieve MU through data obfuscation and adjustments to the model’s training loss. Furthermore, existing approaches lack generalization ability across different unlearning tasks. In this paper, we propose a <Emphasis Type="Underline">Meta</Emphasis>-Learning-Based Knowledge Graph <Emphasis Type="Underline">E</Emphasis>mbedding <Emphasis Type="Underline">U</Emphasis>nlearning framework (MetaEU), specifically designed for KGE unlearning. With the help of meta-learning, the model can discover the inherent relationships between different unlearning tasks, thereby avoiding the need to start from scratch for each unlearning task and achieving better generalization across various task scenarios. A thorough experimental study on benchmark datasets shows that MetaEU demonstrates promising performance in the knowledge graph embedding unlearning task.</p>

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Learn to unlearn: meta-learning-based knowledge graph embedding unlearning

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • Naixing Xu,
  • Qian Li,
  • Zhaochuan Li,
  • Xu Wang,
  • Bingchen Liu,
  • Jabulani Brown Mpofu,
  • Jingchen Li,
  • Xin Li

摘要

Knowledge graph embedding (KGE) methods map entities and relations from knowledge graphs to continuous vector spaces, simplifying their representations and enhancing performance across various tasks (e.g., link prediction, question answering). As concerns about personal privacy rise, machine unlearning (MU), an emerging artificial intelligence technology that enables models to eliminate the influence of specific data, has garnered increasing attention from the academic community. The existing KGE unlearning works mainly achieve MU through data obfuscation and adjustments to the model’s training loss. Furthermore, existing approaches lack generalization ability across different unlearning tasks. In this paper, we propose a Meta-Learning-Based Knowledge Graph Embedding Unlearning framework (MetaEU), specifically designed for KGE unlearning. With the help of meta-learning, the model can discover the inherent relationships between different unlearning tasks, thereby avoiding the need to start from scratch for each unlearning task and achieving better generalization across various task scenarios. A thorough experimental study on benchmark datasets shows that MetaEU demonstrates promising performance in the knowledge graph embedding unlearning task.