面向知识图谱链接预测的层级感知语义损失函数

中文摘要 AI 辅助

知识图谱通常附带编码有宝贵语义信息的本体类层级，但许多链接预测方法要么忽略此类层级，要么通过额外的图边间接将其纳入。近期研究引入了层级感知图神经网络（GNN），该方法利用来自盒嵌入（box embeddings）的语义损失，以在基于GNN的表示学习过程中促使子类关系得到满足。尽管该方法在生物回归任务中展现出潜力，但尚未针对知识图谱链接预测验证其有效性。本文在AIFB、CoDEx和BioKG三个基准数据集上评估了层级感知语义损失在链接预测中的效果。我们将图神经网络编码器与基于盒嵌入的语义损失相结合，以促使学习到的表示更好地满足源自本体的类层级，并将该方法与标准链接预测模型以及将子类关系作为图边纳入的模型进行比较。在所有数据集上，层级感知语义损失均显著提升了平均倒数排名（MRR），且始终优于通过额外子类边纳入层级信息的模型。相对于基线GNN模型，在AIFB、CoDEx和BioKG上的MRR分别提升了7.6%、2.4%和15.5%。此外，语义损失始终优于用子类边扩充图的替代方案。这些结果与源自本体的类层级为图结构提供互补信息的结论一致，表明通过语义损失促使层级一致性是一种有效且参数效率较高的机制，可用于改进知识图谱链接预测。

英文摘要

Knowledge graphs are often accompanied by ontological class hierarchies that encode valuable semantic information, yet many link prediction methods either ignore such hierarchies or incorporate them indirectly through additional graph edges. Recent work introduced hierarchy-aware graph neural networks (GNNs), which use semantic losses derived from box embeddings to encourage satisfaction of subclass relationships during GNN-based representation learning. While this approach has shown promise for biological regression tasks, its effectiveness for knowledge graph link prediction has not been investigated. In this paper we evaluate hierarchy-aware semantic losses on link prediction across three benchmark datasets: AIFB, CoDEx, and BioKG. We combine graph neural network encoders with box-embedding-based semantic losses that encourage learned representations to better satisfy ontology-derived class hierarchies, and compare this approach to both standard link prediction models and models incorporating subclass relations as graph edges. Across all datasets, hierarchy-aware semantic losses significantly improve mean reciprocal rank (MRR) and consistently outperform models that incorporate hierarchy information through additional subclass edges. Relative to the baseline GNN models, MRR improved by 7.6%, 2.4%, and 15.5% on AIFB, CoDEx, and BioKG, respectively. Furthermore, semantic losses consistently outperform the alternative of augmenting the graph with subclass edges. These results are consistent with ontology-derived class hierarchies providing complementary information to graph structure, and suggest that encouraging hierarchical consistency through semantic losses is an effective and comparatively parameter-efficient mechanism for improving knowledge graph link prediction.

发表机构

• Chalmers University of Technology（查尔姆斯理工大学）
• University of Gothenburg（哥德堡大学）
• University of Cambridge（剑桥大学）

机构由 AI 辅助整理，请以论文原文为准。