ConvKB: A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

This program provides the implementation of the CNN-based model ConvKB for the knowledge base completion task. ConvKB obtains new state-of-the-art results on two standard datasets: WN18RR and FB15k-237 as described in the paper:

    @InProceedings{Nguyen2018,
      author={Dai Quoc Nguyen and Tu Dinh Nguyen and Dat Quoc Nguyen and Dinh Phung},
      title={{A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network}},
      booktitle={Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)},
      year={2018},
      pages={327--333}
      }

Please cite the paper whenever ConvKB is used to produce published results or incorporated into other software. I would highly appreciate to have your bug reports, comments and suggestions about ConvKB. As a free open-source implementation, ConvKB is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

ConvKB is free for non-commercial use and distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA) License.

Note

Update a new initialization for WN18RR: MR:763, MRR:0.253 and Hits@10:56.7. Please check our new NAACL2019 paper.

	$ python train.py --embedding_dim 100 --num_filters 400 --learning_rate 0.00005 --name WN18RR --num_epochs 101 --saveStep 100 --model_name wn18rr_400_3

Usage

Requirements

Python 3
Tensorflow >= 1.6

Training

To run the program:

    python train.py --embedding_dim <int> --num_filters <int> --learning_rate <float> --name <dataset_name> [--useConstantInit] --model_name <name_of_saved_model>

Required parameters:

--embedding_dim: Dimensionality of entity and relation embeddings.

--num_filters: Number of filters.

--learning_rate: Initial learning rate.

--name: Dataset name (WN18RR or FB15k-237).

--useConstantInit: Initialize filters by [0.1, 0.1, -0.1]. Otherwise, initialize filters by a truncated normal distribution.

--model_name: Name of saved models.

Optional parameters:

--l2_reg_lambda: L2 regularizaion lambda (Default: 0.001).

--dropout_keep_prob: Dropout keep probability (Default: 1.0).

--num_epochs: Number of training epochs (Default: 200).

--run_folder: Specify directory path to save trained models.

--batch_size: Batch size.

Reproduce the ConvKB results

To reproduce the ConvKB results published in the paper:

    $ python train.py --embedding_dim 100 --num_filters 50 --learning_rate 0.000005 --name FB15k-237 --useConstantInit --model_name fb15k237
    
    $ python train.py --embedding_dim 50 --num_filters 500 --learning_rate 0.0001 --name WN18RR --model_name wn18rr --saveStep 50

Evaluation metrics

File eval.py provides ranking-based scores as evaluation metrics, including the mean rank, the mean reciprocal rank and Hits@10 in a setting protocol "Filtered".

Files evalFB15k-237.sh and evalWN18RR.sh contain evaluation commands. Depending on the memory resources, you should change the value of --num_splits to a suitable value to get a faster process. To get the results (supposing num_splits = 8):

    $ python eval.py --embedding_dim 100 --num_filters 50 --name FB15k-237 --useConstantInit --model_name fb15k237 --num_splits 8 --decode
    
    $ python eval.py --embedding_dim 50 --num_filters 500 --name WN18RR --model_name wn18rr --num_splits 8 --decode

Acknowledgments

I would like to thank Denny Britz for implementing a CNN for text classification in TensorFlow.

WeidongLi-KG/ConvKB