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README.md

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural NetworkTwitter

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This program provides the implementation of the CNN-based model ConvKB for knowledge graph embeddings as described in the paper. ConvKB uses a convolution layer with different filters of the same 1 × 3 shape and then concatenates output feature maps into a single vector which is multiplied by a weight vector to produce a score for the given triple.

Usage

News

  • June 13, 2020: Update Pytorch (1.5.0) implementation. The ConvKB Pytorch implementation, which is based on the OpenKE framework, is to deal with the issue #5.

  • May 30, 2020: The Tensorflow implementation was completed approximately three years ago, and now it is out-of-date. I will release the Pytorch implementation soon.

  • March 06, 2018: Note that our Tensorflow implementation can leverage different filters of different n × 3 shapes, so we can tune the hyper-parameter n. In our paper, we set n to 1 for simplification.

Requirements

  • Python 3.6
  • Pytorch 1.5.0 or Tensorflow 1.6

Training

Regarding the Pytorch implementation:

$ python train_ConvKB.py --dataset WN18RR --hidden_size 50 --num_of_filters 64 --neg_num 10 --valid_step 50 --nbatches 100 --num_epochs 300 --learning_rate 0.01 --lmbda 0.2 --model_name WN18RR_lda-0.2_nneg-10_nfilters-64_lr-0.01 --mode train

$ python train_ConvKB.py --dataset FB15K237 --hidden_size 100 --num_of_filters 128 --neg_num 10 --valid_step 50 --nbatches 100 --num_epochs 300 --learning_rate 0.01 --lmbda 0.1 --model_name FB15K237_lda-0.1_nneg-10_nfilters-128_lr-0.01 --mode train
Dataset MR MRR Hits@10
WN18RR 2741 0.220 50.8
FB15K-237 196 0.302 48.3

Cite

Please cite the paper whenever ConvKB is used to produce published results or incorporated into other software:

@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)},
  pages={327--333},
  year={2018}
}

License

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 licensed under the Apache License 2.0.

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