实体对齐(Entity Alignment)也被称作实体匹配(Entity Matching),是指对于异构数据源知识库中的各个实体,找出属于现实世界中的同一实体。 实体对齐常用的方法是利用实体的属性信息判定不同源实体是否可进行对齐。

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在多源知识图谱(KGs)中寻找等价实体是KGs集成的关键步骤,也称为实体对齐(EA)。然而,现有的EA方法大多效率低下,伸缩性差。最近的总结指出,其中一些甚至需要几天的时间来处理包含20万个节点(DWY100K)的数据集。我们认为过于复杂的图编码器和低效的负采样策略是造成这种现象的两个主要原因。本文提出了一种新的KG编码器-双注意匹配网络(Dual- AMN),该网络不仅能对图内和图间信息进行智能建模,而且大大降低了计算复杂度。此外,我们提出了归一化的硬样本挖掘损失来平滑选择硬负样本,减少了损失偏移。在广泛应用的公共数据集上的实验结果表明,该方法具有较高的精度和效率。在DWY100K上,我们的方法的整个运行过程可以在1100秒内完成,比之前的工作至少快10倍。我们的方法在所有数据集上的性能也优于之前的工作,其中𝐻𝑖𝑡𝑠@1和𝑀𝑅𝑅从6%提高到13%。

https://www.zhuanzhi.ai/paper/3d0a0bf7905b28afbdffaa48e0d640c3

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Knowledge Graph Completion (KGC) predicts missing facts in an incomplete Knowledge Graph. Almost all of existing KGC research is applicable to only one KG at a time, and in one language only. However, different language speakers may maintain separate KGs in their language and no individual KG is expected to be complete. Moreover, common entities or relations in these KGs have different surface forms and IDs, leading to ID proliferation. Entity alignment (EA) and relation alignment (RA) tasks resolve this by recognizing pairs of entity (relation) IDs in different KGs that represent the same entity (relation). This can further help prediction of missing facts, since knowledge from one KG is likely to benefit completion of another. High confidence predictions may also add valuable information for the alignment tasks. In response, we study the novel task of jointly training multilingual KGC, relation alignment and entity alignment models. We present ALIGNKGC, which uses some seed alignments to jointly optimize all three of KGC, EA and RA losses. A key component of ALIGNKGC is an embedding based soft notion of asymmetric overlap defined on the (subject, object) set signatures of relations this aids in better predicting relations that are equivalent to or implied by other relations. Extensive experiments with DBPedia in five languages establish the benefits of joint training for all tasks, achieving 10-32 MRR improvements of ALIGNKGC over a strong state-of-the-art single-KGC system completion model over each monolingual KG . Further, ALIGNKGC achieves reasonable gains in EA and RA tasks over a vanilla completion model over a KG that combines all facts without alignment, underscoring the value of joint training for these tasks.

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