论文标题
Eventea：以事件为中心知识图的基准测试实体对齐
EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
论文作者
论文摘要
实体对齐是在不同的知识图（kgs）中找到相同的现实对象中的相同实体。基于嵌入的实体对准技术最近引起了很多关注，因为它们可以帮助解决不同kg中符号异质性的问题。但是，在本文中，我们表明过去取得的进展是由于偏见和无挑战的评估所致。我们重点介绍了现有数据集中的两个主要缺陷，这些缺陷有利于基于嵌入的实体对准技术，即关系三元组中的同构图结构和属性三元组中的弱异质性。为了对基于嵌入的实体对准方法进行批判性评估，我们根据以事件为中心的KG构建了一个具有异质关系和属性的新数据集。我们进行了广泛的实验来评估现有的流行方法，并发现它们无法实现有希望的性能。作为解决这个困难问题的一种新方法，我们提出了一个时间感知的实体一致性编码器。数据集和源代码可公开用于培养未来的研究。我们的工作要求更有效，更实用的基于实体对齐的解决方案。
Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to achieve promising performance. As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment. The dataset and source code are publicly available to foster future research. Our work calls for more effective and practical embedding-based solutions to entity alignment.
