Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model
EMNLP/IJCNLP (1), pp. 2723-2732, 2019.
EI
Keywords:
entity alignmentGraph Convolutional Networkembedding modelKnowledge GraphsGraph Neural NetworksMore(7+)
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Abstract:
Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications. It is challenging due to the heterogeneity of KGs and limited seed alignments. In this paper, we propose a semi-supervised entity alignment method by joint Knowledge Embedding...More
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Introduction
- Many Knowledge Graphs (KGs) (e.g., DBpedia (Lehmann et al, 2015), YAGO (Rebele et al, 2016) and BabelNet (Navigli and Ponzetto, 2012)) have emerged to provide structural knowledge for different applications.
- KG-based models (Hao et al, 2016; Chen et al, 2017; Sun et al, 2017; Zhu et al, 2017; Sun et al, 2018; Chen et al, 2018) utilize existing KG representation learning methods to learn embeddings of entities and relations in different KGs, and align them into a unified vector space.
- The authors use bold-face letters to denote the vector representations of the corresponding terms throughout the paper
Highlights
- Many Knowledge Graphs (KGs) (e.g., DBpedia (Lehmann et al, 2015), YAGO (Rebele et al, 2016) and BabelNet (Navigli and Ponzetto, 2012)) have emerged to provide structural knowledge for different applications
- We propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG), which combines the above two types of methods
- Knowledge Embedding model and Cross-Graph model is significantly more effective than MTransE, JAPE, and Graph Convolutional Network-Align on all datasets, and slightly outperform AlignEA on small-scale datasets but greatly on large-scale datasets
- AlignEA is the strongest one, which results from it using two margins to control the scores of triplets, making the Knowledge Graphs structure modeling based on triplets more accurate than others
- We propose a semi-supervised entity alignment method Knowledge Embedding model and Cross-Graph model that combines the knowledge embedding model and graph-based model
- Iteratively discovering new entity alignments based on the framework of Knowledge Embedding model and Cross-Graph model is another interesting direction
Methods
- DBP15KZH−EN DBP15KJA−EN DBP15KFR−EN DWY100KWD DWY100KYG.
- Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR MTransE JAPE.
- KECG “*” denotes the unreported metrics in their papers.
- The authors reproduced the results using their source code
Results
- KECG propagates these features to unaligned entities, making the positions of the unaligned entities in the vector space very close to the corresponding entities
- It reflects the importance of the global structural information of KGs. Compared with the graph-based model, KECG significantly outperforms GCN-Align regarding all metrics.
- Compared with the graph-based model, KECG significantly outperforms GCN-Align regarding all metrics
- It shows that involving the local relationships between entities can make entities more distinguishable, making the alignment more accurate
Conclusion
- The authors propose a semi-supervised entity alignment method KECG that combines the knowledge embedding model and graph-based model.
- The authors will extend the knowledge embedding model of KECG to other KG representation learning methods, such as TransD (Ji et al, 2015), to gain a stronger ability of modeling relationships.
- Iteratively discovering new entity alignments based on the framework of KECG is another interesting direction
Summary
Introduction:
Many Knowledge Graphs (KGs) (e.g., DBpedia (Lehmann et al, 2015), YAGO (Rebele et al, 2016) and BabelNet (Navigli and Ponzetto, 2012)) have emerged to provide structural knowledge for different applications.- KG-based models (Hao et al, 2016; Chen et al, 2017; Sun et al, 2017; Zhu et al, 2017; Sun et al, 2018; Chen et al, 2018) utilize existing KG representation learning methods to learn embeddings of entities and relations in different KGs, and align them into a unified vector space.
- The authors use bold-face letters to denote the vector representations of the corresponding terms throughout the paper
Methods:
DBP15KZH−EN DBP15KJA−EN DBP15KFR−EN DWY100KWD DWY100KYG.- Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR Hits@1 Hits@10 MRR MTransE JAPE.
- KECG “*” denotes the unreported metrics in their papers.
- The authors reproduced the results using their source code
Results:
KECG propagates these features to unaligned entities, making the positions of the unaligned entities in the vector space very close to the corresponding entities- It reflects the importance of the global structural information of KGs. Compared with the graph-based model, KECG significantly outperforms GCN-Align regarding all metrics.
- Compared with the graph-based model, KECG significantly outperforms GCN-Align regarding all metrics
- It shows that involving the local relationships between entities can make entities more distinguishable, making the alignment more accurate
Conclusion:
The authors propose a semi-supervised entity alignment method KECG that combines the knowledge embedding model and graph-based model.- The authors will extend the knowledge embedding model of KECG to other KG representation learning methods, such as TransD (Ji et al, 2015), to gain a stronger ability of modeling relationships.
- Iteratively discovering new entity alignments based on the framework of KECG is another interesting direction
Tables
- Table1: Statistics of DBP15K and DWY100K
- Table2: Results comparison on entity alignment(Results of Hits@1 and Hits@10 are percentage values)
Related work
- 5.1 KG Embedding
Various KG embedding methods have shown effectiveness in modeling the semantic information of KGs. TransE (Bordes et al, 2013) is a milestone in learning embeddings for KGs, which interprets a relation as the translation from its head entity to its tail entity. Then it motivated several enhanced methods like TransR (Lin et al, 2015). In addition to them, non-translational methods also achieve satisfactory performances, such as RESCAL (Nickel et al, 2011), ConvE (Dettmers et al, 2018) and RotatE (Sun et al, 2019). Meanwhile, external information in KGs is fused to improve embedding (Wang and Li, 2016). More detailed KG embedding methods are summarized in (Wang et al, 2017).
Funding
- This work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key projects (U1736204, 6153301), a research fund by Alibaba, and THUNUS NExT Co-Lab
- NExT++ research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its IRC@SG Funding Initiative
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