基于实体和关系文本的邻居三元组匹配的无监督鲁棒跨语言实体对齐
Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts
AI总结：
针对跨语言实体对齐中获取标签困难及现有无监督方法的局限，本文提出ERAlign，通过邻居三元组匹配联合实体和关系文本语义，实现无监督鲁棒对齐，提升精度并增强对噪声的抵抗能力。
摘要：
跨语言实体对齐（EA）能够整合不同语言的多个知识图谱（KGs），为用户提供对多样化且全面知识的无缝访问。现有方法大多为有监督，面临获取带标签实体对的挑战。为此，近期研究转向自监督和无监督框架。尽管有效，这些方法存在局限：（1）关系传递：主要关注实体而忽略关系的语义信息；（2）同构假设：假设源图和目标图同构，导致噪声并降低对齐精度；（3）噪声脆弱性：易受文本特征中的噪声影响，尤其在遇到不一致翻译或词汇外（OOV）问题时。本文提出ERAlign，一种无监督鲁棒跨语言EA流水线，通过使用实体和关系的语义文本特征的邻居三元组匹配，联合执行实体级和关系级对齐。其细化步骤基于邻居三元组匹配融合实体级和关系级对齐，迭代增强结果；额外的验证步骤将实体的邻居三元组作为线性化文本进行检查。这种“对齐后验证”流水线严格评估对齐结果，即使在实体文本特征存在噪声时也能实现近乎完美的对齐。大量实验表明，ERAlign的鲁棒性和普遍适用性提升了EA任务的精度和有效性，对知识导向应用有重要贡献。
英文摘要：
Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages, providing users with seamless access to diverse and comprehensive knowledge. Existing methods, mostly supervised, face challenges in obtaining labeled entity pairs. To address this, recent studies have shifted towards self-supervised and unsupervised frameworks. Despite their effectiveness, these approaches have limitations: (1) Relation passing: mainly focusing on the entity while neglecting the semantic information of relations, (2) Isomorphic assumption: assuming isomorphism between source and target graphs, which leads to noise and reduced alignment accuracy, and (3) Noise vulnerability: susceptible to noise in the textual features, especially when encountering inconsistent translations or Out-of-Vocabulary (OOV) problems. In this paper, we propose ERAlign, an unsupervised and robust cross-lingual EA pipeline that jointly performs Entity-level and Relation-level Alignment by neighbor triple matching strategy using semantic textual features of relations and entities. Its refinement step iteratively enhances results by fusing entity-level and relation-level alignments based on neighbor triple matching. The additional verification step examines the entities' neighbor triples as the linearized text. This Align-then-Verify pipeline rigorously assesses alignment results, achieving near-perfect alignment even in the presence of noisy textual features of entities. Our extensive experiments demonstrate that the robustness and general applicability of ERAlign improved the accuracy and effectiveness of EA tasks, contributing significantly to knowledge-oriented applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
