Abstract
Entity alignment (EA) is to match entities referring to identical real-world facts among different knowledge graphs (KGs). For simplicity, most previous work ignores the existence of dangling entities in the source KG. However, entity alignment task with dangling entities has emerged as a novel demand in real scenarios. Some work explores new dangling-aware loss functions and transfers existing methods on dangling settings, whose performance is still not satisfactory. Thus, in this work, we propose Relation-aware Masked Graph Convolutional Networks (RMGCN). In the learning stage, it can not only take advantage of the masking mechanism to alleviate the negative impact from nearby dangling neighbors, but also take both graph structure and relations of KGs into consideration. In the inference stage, it performs a two-step alignment which firstly filters out dangling entities, and then align the remaining entities. We adopt a novel distribution-based dangling entity detection method in first step to decrease the error propagation from dangling detection to the following EA task. The experimental results show that our model outperforms all state-of-the-art models with at least 7.5% on F1 score.
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Liu, X., Zhou, F., Li, X.Y. (2023). RMGCN: Masked Graph Convolutional Networks for Relation-Aware Entity Alignment with Dangling Cases. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14302. Springer, Cham. https://doi.org/10.1007/978-3-031-44693-1_22
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