Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding

By Zequn Sun, Wei Hu, Chengkai Li. International Workshop on the Semantic Web, 2017

520 citations recorded by the source corpus.

Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c186baf0ece4167bcb2a5fd0a7272e4a51583dd8

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Citations in the corpus

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, methodology): “There are also more datasets for specific tasks such as cross-lingual DBP15K [101] and DWY100K [100] for entity alignment, multi-view knowledge graphs of YAGO26K-906 and DB111K174 [201] with instances and ontologies.”
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs (2019, methodology): “JAPE (Sun et al., 2017) is also based on TransE, but it learns embeddings of different KGs in a uniﬁed space.”
Aligning Cross-Lingual Entities with Multi-Aspect Information (2019, methodology): “We ﬁrst compare M AN and H MAN against previous systems (Hao et al., 2016; Chen et al., 2017a; Sun et al., 2017; Wang et al., 2018).”
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs (2019, methodology): “Recently, embedding-based methods [ Hao et al. , 2016; Chen et al. , 2017; Sun et al. , 2017; Zhu et al. , 2017; Sun et al. , 2018; Wang et al. , 2018 ] have been proposed to address this issue.”
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (2018, methodology): “Because we use the same datasets as in (Sun et al., 2017), the results of JE, MTransE, and JAPE are obtained from (Sun et al., 2017).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “Unlike previous methods, JAPE [291] consolidates attribute embeddings with overlay relationship graph structures to capture cross-lingual disparities.”
A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, unknown): “Figure 2: Degree distributions and average degrees of two popular datasets DBP15K [72] and WK3L [10] used in previous approaches, along with our contributed dataset EN-FR15K (V1).”
Multi-Channel Graph Neural Network for Entity Alignment (2019, methodology): “Instead, recent work utilizes general KG embedding methods (e.g., TransE (Bor-des et al., 2013)) and align equivalent entities into a uniﬁed vector space based on a few seed alignments (Chen et al., 2017; Sun et al., 2017; Zhu et al., 2017; Chen et al., 2018; Sun et al., 2018; Wang et al., 2018b).”
Entity Alignment between Knowledge Graphs Using Attribute Embeddings (2019, methodology): “Moreover, our method achieves better performance than the existing embedding-based entity alignment models MTransE and JAPE on these tasks.”
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation (2019, methodology): “We compare with recent embedding-based entity alignment models: MTransE (Chen et al. 2017), IPTransE (Zhu et al. 2017), JAPE (Sun, Hu, and Li 2017), AlignE (Sun et al. 2018), GCN-Align (Wang et al. 2018), SEA (Pei et al. 2019), RSN (Guo, Sun, and Hu 2019) and MuGNN (Cao et al. 2019).”