Lingbing Guo, Zequn Sun, Wei Hu*. Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. In: ICML 2019
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Please first install Python 3.5+, and then unpack data.7z.
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Type
pip install -r requirementsin shell to install required packages. Note that, when using Tensorflow 1.2+, the learning rate has to be re-adjusted. We suggest using tensorflow-gpu = 1.1.
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Run jupyter by typing
jupyter notebookin shell. -
In the opened browser, click RSN4EA.ipynb for entity alignment, or 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 and FB15K-237, you can easily download them 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 64GB memory.
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For entity alignment, V1 denotes the normal datasets, and V2 denotes the dense ones. Please use first 10% data of
ref_ent_idsfor validation. -
entity-alignment-full-data.7z provides a complete version of the EA datasets, including attribute files and datasets with different proportions.
If you find our work useful, please kindly cite it as follows:
@inproceedings{RSN,
author = {Lingbing Guo and Zequn Sun and Wei Hu},
title = {Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs},
booktitle = {ICML},
pages = {2505--2514},
year = {2019}
}
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