A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020)

Abstract: Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.

## introduction knowledge graphs (kgs) have recently demonstrated their potential in many natural language processing (nlp) tasks, such as language modelling @xcite and question answering @xcite . with the rapid growth of multilingual kgs, such as dbpedia @xcite and yago @xcite , cross-lingual entity alignment has attracted considerable focus due to the lack of cross-lingual links. the task of cross-lingual entity alignment aims to automatically detect equivalent entities from different monolingual kgs to bridge the language gap. most recently, many graph neural networks (gnns) based methods are proposed for entity alignment @xcite @xcite @xcite . since gnns are powerful to model graph-structured data by aggregating neighborhood information, the gnn-based methods have shown promising performance. however, existing gnn-based methods model different kgs separately, which ignore the useful pre-aligned links between two kgs. these gnn-based methods only use the pre-aligned seed alignments to optimize the objective function during training and fail to take full use of seed alignments, which provide useful contextual alignment information for entity alignment task. intuitively, if two entities in different kgs share many pre-aligned neighbors, they are most likely to be equivalent entities. taking the example shown in figure 1 (a), although the entity "哥威迅语(gothic language)" in chinese from g 1 has the same translated surface form with the entity "gothic language" in english from g 2 , the correct alignment for "哥威迅语(gothic language)" is the entity "gwich'in language" in english. by considering the contextual alignments in different kgs, such as "美国(united states)" and "united states", "加拿大(canada)" and "canada", "纳-德内语系(na de nene)" and "na-dene languages", "德内语支(german language branch)" and "athabaskan languages", we can get more evidences that the embeddings of entity "哥威迅语(gothic language)" and entity "gwich'in language" are equivalent with each other. thus, the mismatch issue can be easily corrected. the contextual alignments information is not explicitly considered in conventional gnn-based methods, which leads to sub-optimal results. the main contributions of this study are summarised as follows: ## experiments in this section, we conduct extensive experiments to evaluate our proposed method caecgat on crosslingual entity alignment task. the detailed experiments on benchmark datasets and the results are described in the following subsections. ## conclusions and future work in this paper, we propose a novel caecgat model for cross-lingual kg entity alignment task, which take full use of seed alignments to alleviate the semantic gap between different kgs. we first use a cross-kg aggregation layer to transfer entity information across different kgs, which enables the embeddings of different kgs to share cross-kg information. then, an attention-based cross-kg propagation layer is applied to gather neighbors with important cross-kg information. by staking multiple cgat layers, the cross-kg information can be propagated in the two kgs. experimental results on several benchmark datasets demonstrate the effectiveness of the proposed caecgat model. in the future, we will consider more entity information, such as entity properties and descriptions. besides, we may investigate the proposed model to other datasets (e.g., dbp100k @xcite and wk31 @xcite ).