OTIEA:Ontology-Enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment

Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular message. Aiming to discover equivalent objects from different knowledge graphs (KGs), embedding-based entity alignment (EA) has been attracting great interest from industry and academic research recently. Most of related methods usually explore the correlation between entities and relations through neighbor nodes, structural information and external resources. However, the complex intrinsic interactions among triple elements and role information are rarely modeled, which leads to the inadequate illustration. In addition, external resources are unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the weak scalability. To tackle the above insufficiency, a novel universal EA framework (OTIEA) based on ontology pair and role enhancement mechanism via triple-aware attention is proposed in this paper without introducing external resources. Specifically, an ontology-enhanced triple encoder is designed via mining intrinsic correlations and ontology pair information instead of independent elements. In addition, the EA-oriented representations can be obtained in triple-aware entity decoder by fusing role diversity. Finally, a bidirectional iterative alignment strategy is deployed to expand seed entity pairs. The experimental results on three real-world datasets show that our framework achieves a competitive performance compared with baselines.