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Knowledge graph representation learning with relation-guided aggregation and interaction
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DOI：10.1016/j.ipm.2024.103752.png)
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Abstract
En 中文
Knowledge graph representation learning (KGRL) aims to study the feature representations to solve the incompleteness of knowledge graphs (KGs). Recently, graph neural network (GNN) has achieved satisfactory performance in KGRL tasks. However, many GNN based KGRL approaches fail to capture the various semantic of entities, which may reduce the representation quality of embeddings. Furthermore, most of them treat relations equally when learning embeddings, which will lead to weakening of the specific semantic features. To alleviate the above problems, we propose a novel KGRL method (RGAI) with relation-guided aggregation (RGA) and relation- guided interaction (RGI). To capture rich attributes of entities, the RGA module generates multiple sub-structures according to relation types, and aggregates the semantic information among them. To mine specific semantic features and dig out the different contribution of relations, the RGI module calculates the importance of each relation to the central entity, and generates final entity features through aggregating representations of neighbors. Extensive experiments on standard KG datasets validate the effectiveness of the proposed innovations, and RGAI achieves state-of-the-art performance compared to existing approaches (e.g., compared to state-of-the-art approaches, RGAI improves Hits@10 from 0.548 to 0.562 on FB15K-237, Hits@3 from 0.561 to 0.582 on YAGO3-10, and Hits@1 from 0.449 to 0.455 on WN18RR).
Keywords:
Link prediction
Representation learning
Knowledge graphs
Graph neural networks
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