Bootstrapping Entity Alignment with Knowledge Graph Embedding

By Zequn Sun, Wei Hu, Qingheng Zhang, Yuzhong Qu. International Joint Conference on Artificial Intelligence, 2018

597 citations recorded by the source corpus.

Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d899e434a7f2eecf33a90053df84cf32842fbca9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip S. Yu
2. Shirui Pan
3. Erik Cambria
4. Wei Hu
5. Zequn Sun
6. Tat-seng Chua
7. Shaoxiong Ji
8. Pekka Marttinen

In Management Science and Operations Research

1. Wei Hu
2. Zequn Sun
3. Lingbing Guo
4. Erik Cambria
5. Shirui Pan
6. Philip S. Yu
7. Pekka Marttinen
8. Shaoxiong Ji

Citations in the corpus

• A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, unknown): “, self-training [81, 102] and co-training [9].”
• 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).”
• 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).”
• 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.”
• 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): “For entity alignment, we picked up several state-of-the-art embedding-based methods for comparison: MTransE (Chen et al., 2017), IPTransE (Zhu et al., 2017), JAPE (Sun et al., 2017), BootEA (Sun et al., 2018) and GCN-Align (Wang et al., 2018).”