An Experimental Study of State-of-the-Art Entity Alignment Approaches
The repos of the methods discussed in this paper can be found in the following.
- MTransE (IJCAI 2017): Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
- JAPE-Stru/JAPE (ISWC 2017): Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding
- GCN/GCN-Align (EMNLP 2018): Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
- RSNs (ICML 2019): Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
- MuGNN (ACL 2019): Multi-Channel Graph Neural Network for Entity Alignment
- KECG (EMNLP 2019): Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model
- ItransE (IJCAI 2017): Iterative Entity Alignment via Joint Knowledge Embeddings
- BootEA (IJCAI 2018): Bootstrapping Entity Alignment with Knowledge Graph Embedding
- NAEA (IJCAI 2019): Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs. (The codes are not publicly available yet, but one could ask the authors for a preliminary version)
- TransEdge (ISWC 2019) : TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs
- HMAN (EMNLP 2019): Aligning Cross-lingual Entities with Multi-Aspect Information
- GM-Align (ACL 2019): Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
- RDGCN (IJCAI 2019): Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
- HGCN (EMNLP 2019): Jointly Learning Entity and Relation Representations for Entity Alignment
- CEA (ICDE 2020): Collective Entity Alignment via Adaptive Features
The log files of our implementatin can be found in the logs directory.
The new mono-lingual dataset can be found in the dataset directory
We also offer a solution that combines the modules in exsiting methods, which can achieve competitive performance.
We thank the authors of aforementioned papers for their great works and for making the source codes publicly available.