RSN

Lingbing Guo, Zequn Sun, Wei Hu. Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. ICML2019

INSTALLATION

1. Please install Python 3.5+ first, and then unpack data.7z.

2. 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.

RUNNING

1. Run jupyter by typing jupyter notebook in shell.

2. In the opened browser, click RSN4EA.ipynb for EA, RSN4KGC.ipynb for KG completion.

3. The files RSN4EA.ipynb and RSN4KGC.ipynb record the latest results on DBP-WD (normal) and FB15K, respectively.

4. You can also click 'Toolbar -> Kernel -> Restart&Run All' to run these two experiments.

DATA

1. Limited by the space, we only uploaded FB15K for KG completion. For WN18, FB15K-237, you can easily download from the Internet.

2. Change options.data_path or other options.* to run RSN on different datasets with different settings.

3. For RSN4KGC.ipynb, we adopt a matrix filter method for evaluation, which may use more than 64 GB memories.

4. For EA datasets, V1 denotes the normal ones, V2 denotes the dense ones.

CITATION

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},
}