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This code reproduces results in Canonical Tensor Decomposition for Knowledge Base Completion (ICML 2018).
Create a conda environment with pytorch cython and scikit-learn :
conda create --name kbc_env python=3.7
source activate kbc_env
conda install --file requirements.txt -c pytorch
Then install the kbc package to this environment
python setup.py install
To download the datasets, go to the kbc/scripts folder and run:
chmod +x download_data.sh
./download_data.sh
Once the datasets are download, add them to the package data folder by running :
python kbc/process_datasets.py
This will create the files required to compute the filtered metrics.
In addition to the results in the paper, here are the performances of ComplEx regularized with the weighted N3 on several datasets, for several dimensions.
Learning rate : 0.1 (0.01 for rank 2000)
Batch size : 1000 (100 for rank 2000)
Max Epochs : 100 (200 for rank 2000)
| rank | 5 | 25 | 50 | 100 | 500 | 2000 |
|---|---|---|---|---|---|---|
| MRR | 0.36 | 0.61 | 0.78 | 0.83 | 0.84 | 0.86 |
| H@1 | 0.27 | 0.52 | 0.73 | 0.79 | 0.80 | 0.83 |
| H@3 | 0.41 | 0.67 | 0.81 | 0.85 | 0.87 | 0.87 |
| H@10 | 0.55 | 0.77 | 0.86 | 0.89 | 0.91 | 0.91 |
| reg | 1e-5 | 1e-5 | 1e-5 | 7.5e-4 | 1e-2 | 2.5e-3 |
| #Params | 163k | 815k | 1.630M | 3.259M | 1.630M | 65.184M |
Learning rate : 0.1
Batch_size : 1000
Max Epochs : 20
| rank | 5 | 8 | 16 | 25 | 50 | 100 | 500 | 2000 |
|---|---|---|---|---|---|---|---|---|
| MRR | 0.19 | 0.45 | 0.92 | 0.94 | 0.95 | 0.95 | 0.95 | 0.95 |
| H@1 | 0.14 | 0.37 | 0.91 | 0.94 | 0.94 | 0.94 | 0.94 | 0.94 |
| H@3 | 0.20 | 0.50 | 0.93 | 0.94 | 0.95 | 0.95 | 0.95 | 0.95 |
| H@10 | 0.29 | 0.60 | 0.94 | 0.95 | 0.95 | 0.95 | 0.96 | 0.96 |
| reg | 1e-3 | 5e-4 | 5e-4 | 1e-3 | 5e-3 | 5e-2 | 5e-2 | 5e-2 |
| #Params | 410k | 656k | 1.311M | 2.049M | 4.098M | 8.196M | 40.979M | 163.916M |
Learning rate : 0.1
Batch Size : 100 (1000 for rank 1000)
Max Epochs : 100
| rank | 5 | 25 | 50 | 100 | 500 | 1000 | 2000 |
|---|---|---|---|---|---|---|---|
| MRR | 0.28 | 0.33 | 0.34 | 0.35 | 0.36 | 0.37 | 0.37 |
| H@1 | 0.20 | 0.24 | 0.25 | 0.26 | 0.27 | 0.27 | 0.27 |
| H@3 | 0.31 | 0.36 | 0.37 | 0.39 | 0.40 | 0.40 | 0.40 |
| H@10 | 0.44 | 0.51 | 0.52 | 0.54 | 0.56 | 0.56 | 0.56 |
| reg | 5e-4 | 5e-2 | 5e-2 | 5e-2 | 5e-2 | 5e-2 | 5e-2 |
| #Params | 150k | 751k | 1.502M | 3.003M | 15.015M | 30.030M | 60.060M |
Learning rate : 0.1
Batch Size : 100 (1000 for rank 8)
Max Epochs : 100
| rank | 5 | 8 | 16 | 25 | 50 | 100 | 500 | 2000 |
|---|---|---|---|---|---|---|---|---|
| MRR | 0.26 | 0.36 | 0.42 | 0.44 | 0.46 | 0.47 | 0.49 | 0.49 |
| H@1 | 0.20 | 0.38 | 0.39 | 0.41 | 0.43 | 0.43 | 0.44 | 0.44 |
| H@3 | 0.29 | 0.38 | 0.42 | 0.45 | 0.47 | 0.49 | 0.50 | 0.50 |
| H@10 | 0.36 | 0.41 | 0.46 | 0.49 | 0.52 | 0.56 | 0.58 | 0.58 |
| reg | 5e-4 | 5e-4 | 5e-2 | 1e-1 | 1e-1 | 1e-1 | 1e-1 | 1e-1 |
| #Params | 410k | 655k | 1.311M | 2.048M | 4.097M | 8.193M | 40.975M | 163.860M |
Learning rate : 0.1
Batch Size : 1000
Max Epochs : 100
| rank | 5 | 16 | 25 | 50 | 100 | 500 | 1000 |
|---|---|---|---|---|---|---|---|
| MRR | 0.15 | 0.34 | 0.46 | 0.54 | 0.56 | 0.57 | 0.58 |
| H@1 | 0.10 | 0.26 | 0.38 | 0.47 | 0.49 | 0.50 | 0.50 |
| H@3 | 0.16 | 0.37 | 0.50 | 0.58 | 0.60 | 0.62 | 0.62 |
| H@10 | 0.25 | 0.50 | 0.60 | 0.67 | 0.69 | 0.71 | 0.71 |
| reg | 1e-3 | 1e-4 | 5e-3 | 5e-3 | 5e-3 | 5e-3 | 5e-3 |
| #Params | 1.233M | 3.944M | 6.163M | 12.326M | 24.652M | 123.262M | 246.524M |
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