Abstract
As an important research work in knowledge fusion, entity alignment can promote the sharing and integration of multi-source knowledge graphs. Recently, entity alignment based on graph neural networks has received a lot of attention for its ability to capture the topology of entities, but it ignores the noise in neighbor subgraphs and the impact of distant neighbors on central entities. In addition, the knowledge graph is a sparse structure, with the vast majority of entities obeying the long-tail effect.But existing works pay little attention to the alignment of long-tail entities. To address the above problems, this paper proposes an entity alignment approach, which aggregates bi-directional multi-hop neighbors to enrich the context of the central entity, and uses entity names to supply entities with less structural information. The feature fusion module can dynamically adjust weights for the significance of different features. Experimental results show that the overall performance of our model is superior than that of GNN-based methods.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (62072086, 62172082, 62072084), the Fundamental Research Funds for the central Universities (N2116008).
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Bai, J., Nie, T., Shen, D., Kou, Y., Yu, G. (2022). Bi-Directional Neighborhood-Aware Network for Entity Alignment in Knowledge Graphs. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_6
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