论文标题
多语言知识图完成具有自我监督的自适应图对齐方式
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
论文作者
论文摘要
在知识图（kg）中预测缺失的事实至关重要，因为现代公斤远非完整。由于劳动密集型的人类标签，当处理各种语言代表的知识时，这种现象会恶化。在本文中，我们探讨了多语言kg的完成，该kg完成了限制种子对准作为桥梁，以接受多种语言的集体知识。但是，先前工作中使用的语言一致性仍未得到充分利用：（1）对齐对的对齐对被同样对待，以最大地推动并行实体，这忽略了kg容量的不一致； （2）种子对齐是稀缺的，通常以吵闹的无监督方式进行新的对准识别。为了解决这些问题，我们提出了一种新颖的自我监督自适应图形对齐方式（SS-AGA）方法。具体而言，SS-AGA通过将对齐方式作为新的边缘类型融合了所有kgs。因此，可以通过关系意识的注意力重量自适应地控制信息的信息传播和噪声影响。同时，SS-AGA具有新的对生成器，该发电机动态捕获了自我监督范式中的潜在对齐对。对公共多语言DBPEDIA KG和新创建的工业多语言电子商务KG的广泛实验在经验上证明了SS-AG的有效性
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages. However, language alignment used in prior works is still not fully exploited: (1) alignment pairs are treated equally to maximally push parallel entities to be close, which ignores KG capacity inconsistency; (2) seed alignment is scarce and new alignment identification is usually in a noisily unsupervised manner. To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method. Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type. As such, information propagation and noise influence across KGs can be adaptively controlled via relation-aware attention weights. Meanwhile, SS-AGA features a new pair generator that dynamically captures potential alignment pairs in a self-supervised paradigm. Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AG
