面向跨语言实体对齐的基于三元组感知注意力的类型增强集成三元组表示
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment
AI总结：
针对现有实体对齐方法忽略三元组不可分割性与实体角色多样性的问题，提出TTEA框架，以关系为信息载体结合三元组注意力与实体增强，在三个跨语言数据集上性能优于SOTA方法。
摘要：
实体对齐（EA）是整合跨语言、跨领域知识图谱（KG）的关键任务，旨在从不同知识图谱中发现指向同一现实世界对象的实体。现有大多数方法通过基于嵌入的方法挖掘三元组元素的相关性来生成对齐实体表示，很少关注三元组的不可分割性和实体角色多样性。本文提出了一种名为TTEA（面向跨语言实体对齐的基于三元组感知注意力的类型增强集成三元组表示）的新型框架，结合集成三元组特异性和实体角色特征解决上述问题。具体而言，该框架将关系作为语义空间与类型空间之间的信息载体来推导集成三元组表示，从而可通过特异性感知的三元组注意力平稳控制空间变换和信息传播过程中的噪声影响。此外，该框架利用三元组感知实体增强来建模三元组元素的角色多样性。在三个真实跨语言数据集上的大量实验表明，该框架的性能优于现有最优方法。
英文摘要：
Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing methods generate aligning entity representation by mining the relevance of triple elements via embedding-based methods, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA -- Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above issues considering ensemble triple specificity and entity role features. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic space and type space, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, our framework uses triple-ware entity enhancement to model the role diversity of triple elements. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework outperforms state-of-the-art methods.
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。
