# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 1 Introduction  
Knowledge Graphs (KGs) are structured representations of knowledge pertaining to real-world entities, which can be utilized to support various downstream applications, including recommendation systems [1], reasoning [2], question answering [3], intelligent security [4], and more. However, the knowledge coverage of a single KG is limited, and often insufficient to meet the needs of these downstream applications. Since different KGs are typically constructed from diverse data sources, they encompass information from distinct domains and contain overlapping entities. This presents an opportunity to integrate disparate KGs into a unified KG with enhanced knowledge coverage. Entity Alignment (EA) serves as a typical method for integrating multiple KGs by identifying entities that correspond to the same real-world object across different graphs,thereby facilitating data merging among them. As show in Figure 1, \"\(\Re\mathbb{K}\)\" in the Chinese KG and the \"_[PERSON]_\" in the English KG both refer to the real world actor and director, \"\(\Re\mathbb{K}\)\". Current EA methods can be categorized into supervised and unsupervised approaches [5; 6]. Supervised EA methods [7; 8; 9] train models to identify aligned entities based on alignment seeds. However, labeling these alignment seeds is both time-consuming and labor-intensive, rendering such methods challenging to implement in practical scenarios. Consequently, this study emphasizes unsupervised EA methods. Recent advancements in graph representation learning have accelerated the development of unsupervised EA methods. Existing unsupervised EA methods can be categorized into two main types: a pseudo-label-based method, and an unsupervised learning grounded in contrastive learning-based method. The former utilizes extensive pre-training models to generate labeled data, which is subsequently refined through supervised learning techniques. For instance, MultiKE [10] devises cross-KG inference methods to enhance labeled data generation, EVA [11] employs images as pivots for generating pseudo-labeled data, and UPLR [12] computes graph interaction divergence among entity pairs across KGs to adaptively mine confidence samples from unlabeled data. Conversely, the latter constructs loss functions from a data distribution perspective utilizing contrastive learning mechanisms, thereby eliminating the need for labeled data generation. For example, ICLEA [13] and SelfKG [14] leverage contrastive learning to extract features of entities within two KGs. The distinction lies in that ICLEA integrates semantic information, entity descriptions, relations, and attribute structures for alignment reasoning. While these unsupervised EA methods have demonstrated considerable success, they still exhibit inherent limitations. (1) The pseudo-labels generated by unsupervised techniques often lack essential information, which hampers the model's ability to learn effectively in an unsupervised manner. This limitation can be attributed to the models' heavy reliance on entity similarity to bolster false label confidence, resulting in overly  
Figure 1: Illustration of the EA task. simplistic labels that offer minimal guidance. (2) To improve the representation learning of entities, many EA methods are increasingly complex in their modeling approaches, leading to inefficient training processes and challenges when applied to large KGs in practical scenarios. Some methodologies enhance alignment learning by incorporating additional graph auxiliary information such as attributes and images; however, this increases both input data volume and modeling complexity. Furthermore, it may restrict the model's applicability, since KGs within specific contexts might not contain the requisite auxiliary information. To address these limitations, we focus on how to improve the neighborhood similarity feature extraction of aligned entities without increasing the complexity of the model.