RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
This addresses the problem of integrating multi-source knowledge graphs for applications like data fusion, though it appears incremental by building on existing embedding methods.
The paper tackles entity alignment across knowledge graphs by proposing a relation-aware graph attention network to better leverage structural information, achieving state-of-the-art performance on cross-lingual datasets.
Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. The majority of the existing embeddings-based entity alignment methods embed entities and relations into a vector space based on relation triples of KGs for local alignment. As these methods insufficiently consider the multiple relations between entities, the structure information of KGs has not been fully leveraged. In this paper, we propose a novel framework based on Relation-aware Graph Attention Networks to capture the interactions between entities and relations. Our framework adopts the self-attention mechanism to spread entity information to the relations and then aggregate relation information back to entities. Furthermore, we propose a global alignment algorithm to make one-to-one entity alignments with a fine-grained similarity matrix. Experiments on three real-world cross-lingual datasets show that our framework outperforms the state-of-the-art methods.