Disentangled Multi-view Graph Neural Network for multilingual knowledge graph completion

Abstract

Multilingual knowledge graph completion (MKGC) uses limited seed pairs from diverse knowledge graphs (KGs) to enrich and complete a target KG. Unlike traditional knowledge graph completion (KGC) tasks that focus on a single KG, MKGC deals with multiple KGs described by diverse languages, imposing a higher level of heterogeneity due to the varying semantic meanings, syntactic structures, and regular expressions across different languages. Existing MKGC methods mainly rely on an end-to-end embedding function that maps multiple KGs into a shared latent space, using relation-aware graph neural networks (GNNs) to unify the contents of entities and relations with respect to their topological structures. However, such methods might not fully exploit the heterogeneity of multilingual KGs, as they overlook inherent details related to neighborhood entities and relations. To address these limitations, we propose a novel Disentangled Multi-view Graph Neural Network (DMGNN) for MKGC. Specifically, our approach consists of two multi-view GNN modules: MKGC and multilingual KG alignment (MKGA) to facilitate knowledge transfer. Notably, DMGNN effectively captures the heterogeneity of multilingual KGs by learning graph features from three distinct views: entities, relations, and triples. Moreover, we introduce a disentangling mechanism wherein separate GNNs are employed to learn features from different views, mitigating feature interference. In addition, we incorporate an attention mechanism on each view GNN to distinguish the importance of neighborhood features. Extensive experiments on public multilingual datasets demonstrate the superiority of our proposed model over existing competitive baselines.