From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment (2021)

Abstract: Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.

## introduction the knowledge graph (kg) represents a collection of interlinked descriptions of real-world objects and events, or abstract concepts (e.g., documents), which has facilitated many downstream applications, such as recommendation systems @xcite and question-answering @xcite . over recent years, a large number of kgs are constructed from different domains and languages by different organizations. these cross-lingual kgs usually hold unique information individually but also share some overlappings. integrating these cross-lingual kgs could provide a broader view for users, especially for the minority language users who usually suffer from lacking language resources. therefore, how to fuse the knowledge from cross-lingual kgs has attracted increasing attentions. as shown in figure 1 , cross-lingual entity alignment (ea) aims to find the equivalent entities marvel usa robert downey jr. iron man spider man [own by] [born in] [friend] 漫威 美国 钢铁侠 美国队长 [own by] [born in] [friend] [act] across multi-lingual kgs, which is a crucial step for integrating kgs. conventional methods @xcite usually solely rely on lexical matching and probability reasoning, which requires machine translation systems to solve cross-lingual tasks. however, existing machine translation systems are not able to achieve high accuracy with limited contextual information, especially for language pairs that are not alike, such as chinese-english and japanese-english. recently, graph convolutional network (gcn) @xcite and subsequent graph neural network (gnn) variants have achieved state-of-the-art results in various graph application. intuitively, gnn is better in capturing structural information of kgs to compensate for the shortcoming of conventional methods. specifically, several gnn-based ea methods @xcite @xcite indeed demonstrate decent performance improvements on public datasets. all these gnn-based ea methods are built upon a core premise, i.e., entities and their counterparts have similar neighborhood structures. however, better performance is not the only outcome of using gnn. existing gnn-based methods inevitably inherit the following inborn defects from neural networks: in this paper, we notice that existing gnn-based ea methods inherit considerable complexity from their neural network lineage. naturally, we consider eliminating the redundant designs from existing ea methods to enhance interpretability and efficiency without losing accuracy. leveraging the core premise of gnn-based ea methods, we restate the assumption that both structures and textual features of source and target kgs are isomorphic. with this assumption, we are able to successfully transform the cross-lingual ea problem into an assignment problem, which is a fundamental and well-studied combinatorial optimization problem. afterward, the assignment problem could be easily solved by the hungarian algorithm @xcite or sinkhorn operation @xcite . based on the above findings, we propose a frustratingly simple but effective unsupervised ea method (seu) without neural networks. compared to existing gnn-based ea methods, seu only retains the basic graph convolution operation for feature propagation while abandoning the complex neural networks, significantly improving efficiency and interpretability. experimental results on the public datasets show that seu could be completed in several seconds with the gpu or tens of seconds with the cpu. more startlingly, our unsupervised method even outperforms the state-of-the-art supervised approaches across all public datasets. furthermore, we discuss the possible reasons behind the unsatisfactory performance of existing complex ea methods and the necessity of neural networks in cross-lingual ea. the main contributions are summarized as follows: kg stores the real-world knowledge in the form of triples (h, r, t). a kg could be defined as @xmath0, r, t ), where e, r, and t represent the entity set, relation set, and triple set, respectively. given a source graph g @xmath1, r s , t s ) and a target graph g @xmath2, r t , t t ), ea aims to find the entity correspondences p between kgs. ## experiments our experiments are conducted on a workstation with a geforce gtx titan x gpu and a ryzen threadripper 3970x cpu. the code and datasets are available in github.com/maoxinn/seu. ## conclusion in this paper, we successfully transform the crosslingual ea problem into the assignment problem. based on this finding, we propose a frustratingly simple but effective unsupervised ea method (seu) without neural networks. experiments on widely used public datasets indicate that seu outperforms all advanced competitors and has high efficiency, interpretability, and stability.