Open Access Issue
MHGCN: Multiview Highway Graph Convolutional Network for Cross-Lingual Entity Alignment
Tsinghua Science and Technology 2022, 27 (4): 719-728
Published: 09 December 2021

Knowledge graphs (KGs) provide a wealth of prior knowledge for the research on social networks. Cross-lingual entity alignment aims at integrating complementary KGs from different languages and thus benefits various knowledge-driven social network studies. Recent entity alignment methods often take an embedding-based approach to model the entity and relation embedding of KGs. However, these studies mostly focus on the information of the entity itself and its structural features but ignore the influence of multiple types of data in KGs. In this paper, we propose a new embedding-based framework named multiview highway graph convolutional network (MHGCN), which considers the entity alignment from the views of entity semantic, relation semantic, and entity attribute. To learn the structural features of an entity, the MHGCN employs a highway graph convolutional network (GCN) for entity embedding in each view. In addition, the MHGCN weights and fuses the multiple views according to the importance of the embedding from each view to obtain a better entity embedding. The alignment entities are identified based on the similarity of entity embeddings. The experimental results show that the MHGCN consistently outperforms the state-of-the-art alignment methods. The research also will benefit knowledge fusion through cross-lingual KG entity alignment.

Open Access Issue
Graph Neural Architecture Search: A Survey
Tsinghua Science and Technology 2022, 27 (4): 692-708
Published: 09 December 2021

In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations. The complexity of GNN model components has brought significant challenges to the existing efficiencies of GNNs. Hence, many studies have focused on building automated machine learning frameworks to search for the best GNN models for targeted tasks. In this work, we provide a comprehensive review of automatic GNN model building frameworks to summarize the status of the field to facilitate future progress. We categorize the components of automatic GNN model building frameworks into three dimensions according to the challenges of building them. After reviewing the representative works for each dimension, we discuss promising future research directions in this rapidly growing field.

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