Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment (2022)

Abstract: Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs), which is a fundamental task for integrating KGs. Throughout its development, Graph Convolutional Network (GCN) has become one of the mainstream methods for EA. These GCN-based methods learn the representations of entities from two KGs by message passing mechanism and then make alignments via measuring the similarity between entity embeddings. The key idea that GCN works in EA is that entities with similar neighbor structures are highly likely to be aligned. However, the noisy neighbors of entities transfer invalid information, drown out equivalent information, lead to inaccurate entity embeddings, and finally reduce the performance of EA. Based on the Sinkhorn algorithm, we design a reliability measure for potential equivalent entities and propose Adaptive Graph Convolutional Network to deal with neighbor noises in GCN. During the training, the network dynamically updates the adaptive weights of relation triples to weaken the propagation of noises. While calculating entity similarity, it comprehensively considers the self-similarity and neighborhood similarity of the entity pair to alleviate the influence of noises. Furthermore, we design a straightforward but efficient strategy to construct pseudo alignments for unsupervised EA. Extensive experiments on benchmark datasets demonstrate that our framework outperforms the state-of-the-art methods in both supervised and unsupervised settings.

## introduction in recent years, knowledge graph (kg), an effective structure for organizing and storing data, has received growing attention. it has been widely applied to many knowledge-driven applications such as search engine @xcite , question answering @xcite , and recommendation @xcite . due to the limitations of data sources and construction methods, it is difficult for a kg graph to achieve perfect knowledge coverage, which affects the performance of knowledge-driven applications. to solve this problem, knowledge fusion came into being. by capturing the differences and complementarities of multiple kgs, knowledge fusion makes up for the lack of knowledge and ensures knowledge redundancy, improving the completeness of kgs. entity alignment (ea), which aims to find equivalent entities from different kgs, is a fundamental task in knowledge fusion. figure 1 shows an example of the ea task. there is a partial french kg and a partial english kg. in the supervised ea setting, the equivalent entity pair (ville_de_séquoia, redwood_city) is already known as a seed alignment. the task is to find other equivalent entity pairs from two kgs, e.g., (comté_de_san_mateo, san_mateo_county). in the unsupervised ea setting, there are no available seed alignments. most recently, neural methods have become the dominant approach for ea. they encode entities from two kgs into a unified vector space and then make alignments via measuring the similarity between entity embeddings. existing neural methods can be divided into two main categories: (1) transe-based. with the assumption that the relation is the translation from the head entity to the tail entity in a relation triple, transe @xcite embeds all relations and entities into a unified vector space for a kg. transe-based ea methods learn entity embeddings through semantic information. they use seed alignments to construct cross-kg relation triples that connect two graphs and then adopt translation-based kg embedding models on both single-kg and cross-kg relation triples. transe and gcn work in ea due to a priori knowledge that there may be multiple pairs of equivalent entities between the respective neighbors of a pair of equivalent entities. equivalent entities have equivalence transfer to their neighboring entities, respectively. furthermore, for entities from different kgs, the more pairs of equivalent entities contained between their neighbors, the higher the probability that the two entities are equivalent. however, there still is a critical challenge for ea ignored by most studies. since equivalent entities may also have non-contributing neighbors, gcn brings additional noises during the message passing process. these noises drown out equivalent information, lead to inaccurate entity embeddings, and finally reduce the performance of ea. we call this phenomenon the neighbor noise problem and these non-contributing connections noisy edges. as an example in figure 1 , while entity ville_de_séquoia, le_parc_menlo, and comté_de_santa_clara contribute to alignment (comté_de_san_mateo, san_mateo_county), entity californie and amérique bring noises to makes the judgment of equivalence more difficult. to further confirm the effect of noisy edges on ea performance, we conduct an exploratory experiment with the ground truth alignments. figure 2 reports the performance of several classic ea methods under different edge settings on dataset dbp15k zh-en @xcite , where mtranse @xcite belongs to transebased methods and gcn-align @xcite , mraea @xcite , rrea @xcite , and raga @xcite belong to gcn-based methods. after removing all the noisy edges, the performance of both transe-based and gcn-based methods significantly improves. however, in real scenarios, we cannot gain the ground truth alignments in advance. in this paper, the core idea for dealing with the neighbor noise problem is to reduce the effect of potential noisy edges in kgs. we adopt the sinkhorn algorithm to design a reliability measure for pseudo equivalent entities. by integrating the reliability measure into gcn, we propose adaptive graph convolutional network for entity alignment, namely agea foot_0 . during the training, it automatically adjusts the weights of relation triples in kgs to weaken the propagation of noisy edges. we summarize the main contributions of this paper as follows: ## the proposed approach for high efficiency and alleviation of overfitting, our framework agea contains a lightweight network with no training parameters except input entity embeddings. figure 3 depicts the overall network architecture of agea. the input consists of relation triples of two kgs, seed alignments between two kgs, and initial entity embeddings. first, for entities in each kg, we adopt a basic single-kg encoder, including a weighted gcn and a relation enhancement module to obtain entity embeddings. then, a entity similarity matrix is constructed by cosine similarity. for every n a epoch, we update adaptive weights of relation triples by calculating adaptive edge weights. during the training, we adopt the cross-entropy function to calculate both the supervised and unsupervised loss, and the embeddings of input entities are updated via the backpropagation algorithm. ## conclusion in this work, we deal with the neighbor noise problem in the ea task. a lightweight and efficient framework agea has been proposed, which mainly consists of an adaptive edge weight calculation module. besides, the sinkhorn algorithm is incorporated into our gcn-based ea framework and shows satisfactory performance. experiments on five datasets indicate that agea outperforms the state-of-the-art methods in both supervised and unsupervised settings with high efficiency and interpretability.