This repository includes the code of MTransE var4 (see paper), links to the data sets, and pretrained models.
A more recent tensorflow implementation is available at this repository: https://github.com/muhaochen/MTransE-tf (recommended), which takes in entity-level seed alignment.
Make sure your local environment has the following installed:
Python >= 2.7.6
pip
Install the dependents using:
./install.sh
Please first download the data sets:
https://drive.google.com/open?id=1AsPPU4ka1Rc9u-XYMGWtvV65hF3egi0z
and pretrained models
https://drive.google.com/open?id=17JOLNlkkBqC5q14TwBBFLpflWusqJMak
Unpack these two folders to the local clone of the repository.
To run the experiments on WK3l (wikipedia graphs), use:
./run_wk3l.sh
To run the experiments on CN3l (conceptNet), use:
./run_cn3l.sh
You may also train your own models on these two data sets using:
./train_models.sh
Please refer to our paper. Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo. Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017
@inproceedings{chen2017multigraph,
title={Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment},
author={Chen, Muhao and Tian, Yingtao and Yang, Mohan and Zaniolo, Carlo},
booktitle={Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI)},
year={2017}
}
The following links point to some recent follow-ups of this work. Here is a paper list on this topic, maintained by Chengjiang and Zequn: https://github.com/THU-KEG/Entity_Alignment_Papers
Sun, Zequn, et al. Cross-lingual entity alignment via joint attribute-preserving embedding. ISWC, 2017.
Zhu, Hao, et al. Iterative entity alignment via joint knowledge embeddings., IJCAI, 2017.
Yeo, Jinyoung, et al. Machine-Translated Knowledge Transfer for Commonsense Causal Reasoning. AAAI. 2018.
Chen, Muhao, et al. Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment., IJCAI, 2018.
Sun, Zequn, et al. Bootstrapping Entity Alignment with Knowledge Graph Embedding. IJCAI. 2018.
Otani, Naoki, et al. Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion. COLING, 2018.
Wang, Zhichun, et al. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. EMNLP, 2018.
Trsedya, Bayu D, et al. Entity Alignment between Knowledge Graphs Using Attribute Embeddings. AAAI, 2019.
Hao, J., et al. Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts. KDD, 2019.
Guo, L., et al. Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. ICML, 2019.
Zhang, Q., et al. Multi-view Knowledge Graph Embedding for Entity Alignment. IJCAI, 2019.
Zhu, Q., et al. Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs IJCAI, 2019.
Pei, S., et al. Improving Cross-lingual Entity Alignment via Optimal Transport IJCAI, 2019.
Wu, Y., et al. Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs IJCAI, 2019.
Pei, C., et al. Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference. WWW, 2019.
Xu, Kun, et al. Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network. ACL, 2019.
Cao, Yi., et al. Multi-Channel Graph Neural Network for Entity Alignment. ACL, 2019.
Sun, Z., et al. TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs. ISWC, 2019.
Yang, H., et al. Aligning Cross-Lingual Entities with Multi-Aspect Information. EMNLP, 2019.
Wu, Y., et al. Jointly Learning Entity and Relation Representations for Entity Alignment EMNLP, 2019.
Qu, M., Tang, J., Bengio, Y. Weakly-supervised Knowledge Graph Alignment with Adversarial Learning.