Lingbing Guo, Zequn Sun, Wei Hu. Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. ICML2019
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Please install Python 3.5+ first, and then unpack data.7z.
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Type
pip install -r requirements
in shell to install required packages. Note that, when using Tensorflow 1.2+, the learning rate has to be readjusted. We suggest using tensorflow-gpu=1.1.
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Run jupyter by typing
jupyter notebook
in shell. -
In the opened browser, click RSN4EA.ipynb for EA, RSN4KGC.ipynb for KG completion.
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The files RSN4EA.ipynb and RSN4KGC.ipynb record the latest results on DBP-WD (normal) and FB15K, respectively.
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You can also click 'Toolbar -> Kernel -> Restart&Run All' to run these two experiments.
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Limited by the space, we only uploaded FB15K for KG completion. For WN18, FB15K-237, you can easily download from the Internet.
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Change options.data_path or other options.* to run RSN on different datasets with different settings.
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For RSN4KGC.ipynb, we adopt a matrix filter method for evaluation, which may use more than 64 GB memories.
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For EA datasets, V1 denotes the normal ones, V2 denotes the dense ones.
If you found our work useful, please cite us as follows:
@inproceedings{RSN,
Author = {Lingbing Guo, Zequn Sun, Wei Hu},
Booktitle = {ICML 2019},
Title = {Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs},
Year = {2019},
}