KGAT

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Tensorflow implementation of Graph Attention Neural Network for Knowledge Graph

KGAT 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/learning-attention-based-embeddings-for-1/knowledge-graph-completion-on-fb15k-237)](https://paperswithcode.com/sota/knowledge-graph-completion-on-fb15k-237?p=learning-attention-based-embeddings-for-1)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/learning-attention-based-embeddings-for-1/knowledge-graph-completion-on-wn18rr)](https://paperswithcode.com/sota/knowledge-graph-completion-on-wn18rr?p=learning-attention-based-embeddings-for-1)

Source code for our [ACL 2019](http://www.acl2019.org/EN/index.xhtml) paper: [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://arxiv.org/abs/1906.01195)
Blog [link](https://deepakn97.github.io/blog/2019/Knowledge-Base-Relation-Prediction/) for this publication.

### Requirements
- [conda](https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh)

Please download miniconda from above link and create an environment using the following command:

conda env create -f pytorch35.yml

Activate the environment before executing the program as follows:

source activate pytorch35
### Dataset
We used five different datasets for evaluating our model. All the datasets and their folder names are given below.
- Freebase: FB15k-237
- Wordnet: WN18RR
- Nell: NELL-995
- Kinship: kinship
- UMLS: umls

### Training

**Parameters:**

`--data`: Specify the folder name of the dataset.

`--epochs_gat`: Number of epochs for gat training.

`--epochs_conv`: Number of epochs for convolution training.

`--lr`: Initial learning rate.

`--weight_decay_gat`: L2 reglarization for gat.

`--weight_decay_conv`: L2 reglarization for conv.

`--get_2hop`: Get a pickle object of 2 hop neighbors.

`--use_2hop`: Use 2 hop neighbors for training.

`--partial_2hop`: Use only 1 2-hop neighbor per node for training.

`--output_folder`: Path of output folder for saving models.

`--batch_size_gat`: Batch size for gat model.

`--valid_invalid_ratio_gat`: Ratio of valid to invalid triples for GAT training.

`--drop_gat`: Dropout probability for attention layer.

`--alpha`: LeakyRelu alphas for attention layer.

`--nhead_GAT`: Number of heads for multihead attention.

`--margin`: Margin used in hinge loss.

`--batch_size_conv`: Batch size for convolution model.

`--alpha_conv`: LeakyRelu alphas for conv layer.

`--valid_invalid_ratio_conv`: Ratio of valid to invalid triples for conv training.

`--out_channels`: Number of output channels in conv layer.

`--drop_conv`: Dropout probability for conv layer.

### Reproducing results

To reproduce the results published in the paper:      
When running for first time, run preparation script with:

$ sh prepare.sh

* **Wordnet**

$ python3 main.py --get_2hop True

* **Freebase**

$ python3 main.py --data ./data/FB15k-237/ --epochs_gat 3000 --epochs_conv 150 --weight_decay_gat 0.00001 --get_2hop True --partial_2hop True --batch_size_gat 272115 --margin 1 --out_channels 50 --drop_conv 0.3 --output_folder ./checkpoints/fb/out/

### Citation
Please cite the following paper if you use this code in your work.

@InProceedings{KBGAT2019,
        author = "Nathani, Deepak and Chauhan, Jatin and Sharma, Charu and Kaul, Manohar",
        title = "Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs",
        booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
        year = "2019",
        publisher = "Association for Computational Linguistics",
        location = "Florence, Italy",
        }

For any clarification, comments, or suggestions please create an issue or contact deepakn1019@gmail.com

KGAT

KGAT 최신버젼 다운로드

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