Liu Guiyang, Jin Canghong, Shi Longxiang, Yang Cheng, Shuai Jiangbing, Ying Jing
School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2023 Aug 10;23(16):7096. doi: 10.3390/s23167096.
Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.
知识图谱中的跨语言实体对齐是知识融合中的一项关键任务。该任务涉及为不同知识图谱中的节点学习低维嵌入，并通过测量其表示向量之间的距离来识别跨图谱的等效实体。现有的对齐模型使用神经网络模块和最近邻算法来找到合适的实体对。然而，这些模型在对齐阶段往往忽略了实体局部结构特征的重要性，这可能导致匹配精度降低。具体而言，表示不佳的节点可能无法从其周围上下文获益。在本文中，我们提出了一种名为SSR的新型对齐模型，该模型利用图中的节点嵌入算法来选择候选实体，然后根据源知识图谱和目标知识图谱中的局部结构相似性对它们进行重新排列。我们的方法提高了现有方法的性能，并且与它们兼容。我们在DBP15k数据集上证明了我们方法的有效性，表明它在需要更少时间的同时优于现有方法。
