RePS: Relation, Position and Structure aware Entity Alignment

Anil Surisetty, Deepak Chaurasiya, Nitish Kumar, Alok Singh, Gaurav Dhama, Aakarsh Malhotra, Ankur Arora, Vikrant Dey

Abstract: Entity Alignment (EA) is the task of recognizing the same entity present in different knowledge bases. Recently, embedding-based EA techniques have established dominance where alignment is done based on closeness in latent space. Graph Neural Networks (GNN) gained popularity as the embedding module due to its ability to learn entities’ representation based on their local sub-graph structures. Although GNN shows promising results, limited works have aimed to capture relations while considering their global importance and entities’ relative position during EA. This paper presents Relation, Position and Structure aware Entity Alignment (RePS), a multi-faceted representation learning-based EA method that encodes local, global, and relation information for aligning entities. To capture relations and neighborhood structure, we propose a relation-based aggregation technique – Graph Relation Network (GRN) that incorporates relation importance during aggregation. To capture the position of an entity, we propose Relation-aware Position Aggregator (RPA) to capitalize entities’ position in a non-Euclidean space using training labels as anchors, which provides a global view of entities. Finally, we introduce a Knowledge Aware Negative Sampling (KANS) that generates harder to distinguish negative samples for the model to learn optimal representations. We perform exhaustive experimentation on four cross-lingual datasets and report an ablation study to demonstrate the effectiveness of GRN, KANS, and position encodings.