Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
By Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. Conference on Empirical Methods in Natural Language Processing, 2018
580 citations recorded by the source corpus.
Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the cross-lingual links in multilingual KGs. In this paper, we propose a novel approach for cross-lingual KG alignment via graph convolutional networks (GCNs). Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space. Entity alignments are discovered based on the distances between entities in the embedding space. Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments. In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/89c650ccb646bdcce886e96e837db3767855c6d2
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Citations in the corpus
- A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, unknown): “Attribute embedding is used by several approaches [9, 29, 80, 84, 89, 92, 94, 96, 100] to enhance the similarity measure of entities.”
- Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation (2019, methodology): “We also observe that the GCN baseline outperforms many other embedding-based entity alignment models including another GCN variant GCN-Align.”
- Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs (2019, methodology): “GCN-Align (Wang et al., 2018) employs graph convolutional networks (GCNs) to embed entities based on adjacent neighborhoods, but it does not consider relation semantics among entities.”
- Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information (2020, background): “Graph neural networks (GNNs), which generalize neural networks for graph structured data, have shown great success for various applications [2, 18, 26, 47, 50, 51].”
- Aligning Cross-Lingual Entities with Multi-Aspect Information (2019, methodology): “Recently, embedding-based solutions (Chen et al., 2017b; Sun et al., 2017; Zhu et al., 2017; Wang et al., 2018; Chen et al., 2018) have been proposed to unify multilingual KGs into the same low-dimensional vector space where equivalent entities are close to each other.”
- A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “GCN-Align [315] was the first to propose a GCN-based framework for entity alignment tasks.”
- Multi-Channel Graph Neural Network for Entity Alignment (2019, methodology): “Inspired by Wang et al. (2018b), which utilize Graph Convolutional Network (GCN) (Kipf and Welling, 2017) to encode the entire KGs, we aim at reconciling the heterogeneity between KGs through completion and pruning, and learn alignment-oriented KG embeddings by modeling structural features from…”
- Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs (2019, methodology): “…6 more recent alignment methods that we have mentioned in Section 1: JE [Hao et al. , 2016], MTransE [Chen et al. , 2017], JAPE [ Sun et al. , 2017 ] , IPTransE [ Zhu et al. , 2017 ] , BootEA [Sun et al. , 2018] and GCN [Wang et al. , 2018], where the BootEA achieves the best performance on DBP15K.”