Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference

Abstract

Entity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection. Most of the recent solutions for entity alignment are based on knowledge graph embedding, which maps knowledge entities in a low-dimension space where entities are connected with the guidance of prior aligned entity pairs. The study in this paper focuses on two important issues that limit the accuracy of current entity alignment solutions: 1) labeled data of priorly aligned entity pairs are difficult and expensive to acquire, whereas abundant of unlabeled data are not used; and 2) knowledge graph embedding is affected by entity's degree difference, which brings challenges to align high frequent and low frequent entities. We propose a semi-supervised entity alignment method (SEA) to leverage both labeled entities and the abundant unlabeled entity information for the alignment. Furthermore, we improve the knowledge graph embedding with awareness of the degree difference by performing the adversarial training. To evaluate our proposed model, we conduct extensive experiments on real-world datasets. The experimental results show that our model consistently outperforms the state-of-the-art methods with significant improvement on alignment accuracy.

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

“…In addition to the alignment loss, embeddings of aligned entities are swapped regularly to calibrate embedding spaces against each other. SEA [16] learns mapping between embedding spaces in both directions and additionaly adds cycle-consistency loss. Therefore the distance between original embedding of an entity and its representation, which was first translated to another space and then back from it, is penalized.…”

Jointly Learning Entity and Relation Representations for Entity Alignment

“…BootEA (Sun et al, 2018) exploits a bootstrapping process to learn KG embeddings. SEA (Pei et al, 2019) proposes a degree-aware KG embedding model to embed KGs. KDCoE (Chen et al, 2018) is a semi-supervised learning approach for co-training embeddings for multilingual KGs and entity descriptions.…”

REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs

“…Then several methods, such as ITransE [50], IPTransE [50] and BootEA [32] leverage the bootstrapping strategy to address the lack of labeled data. Further, semi-supervised approaches [18,26,27] were developed to utilize the unlabeled data for enhancing the performance of entity alignment. Moreover, several works consider to jointly model the structure and attribute information of KGs [6,31,34], or the structure and relation information [29,51] or the structure, relation and attribute information together [49]; 2) Graph Neural Network-based methods.…”

“…Existing methods are often designed to address the alignment problem in a supervised way with human-designed features [23] or with entity representations learned from KG embedding approaches [6,7,31,38]. Also, a few semi-supervised methods [18,26,27] were proposed to make use of unlabeled data to enhance the performance of supervised entity alignment. Supervised or semi-supervised alignment methods have made remarkable discovery of semantically related entities.…”