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AcrE
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<h1 align="center">AcrE</h1> <h4 align="center">Source codes for our COLING 2020 paper: Knowledge Graph Embedding with Atrous Convolution and Residual Learning. </h4> <h2 align="center"> Overview of AcrE <img align="center" src="./overview.png" alt="..."> </h2> We denote our model as AcrE (the abbreviation of Atrous Convolution and Residual Embedding). We design two structures to integrate the standard convolution and atrous convolutions together. One is a serial structure as shown in Figure 1 (a), and the other is a parallel structure as shown in Figure 1 (b). Please refer to the paper for details. ### Requirements * Compatible with PyTorch 1.0 and Python 3.x. * Dependencies can be installed using requirements.txt. ### Datasets * We use FB15k-237,FB15k,DB100K,Kinship,WN18RR,WN18 datasets for evaluation, and they are included in the repo. ### Training and evaluate on datasets * Install all the requirements from `requirements.txt.` * Execute `sh preprocess.sh` for extracting the datasets and setting up the environment. * The command for training arce and its prompt are below: - `data` indicates the dataset used for training the model. - `gpu` is the GPU used for training the model. - `name` is the provided name of the run which can be later used for restoring the model. - Execute `python acre.py --help` for listing all the available options. ``` #Serial fb15k237 python -u acre.py --data FB15k-237 --batch 128 \ --hid_drop 0.5 --feat_drop 0.2 --lr 0.001 --inp_drop 0.3 --gpu 0 --name fb15k_237_s --way s --train_strategy one_to_x #Serial fb15k python -u acre.py --data FB15k --batch 256 \ --hid_drop 0.2 --feat_drop 0.2 --lr 0.001 --inp_drop 0.2 --gpu 0 --name fb15k_s --way s --train_strategy one_to_n #Serial kinship python -u acre.py --data kinship --batch 128 \ --hid_drop 0.5 --feat_drop 0.5 --lr 0.001 --inp_drop 0.2 --gpu 0 --name kinship_s --way s --train_strategy one_to_n #Serial WN18RR python -u acre.py --data WN18RR --batch 256 \ --hid_drop 0.5 --feat_drop 0.1 --lr 0.00125 --inp_drop 0.2 --gpu 0 --name wn18rr_s --way s --train_strategy one_to_n #Serial WN18 python -u acre.py --data WN18 --batch 256 \ --hid_drop 0.3 --feat_drop 0.3 --lr 0.0012 --inp_drop 0.2 --gpu 0 --name wn18_s --way s --train_strategy one_to_n #Serial DB100K python -u acre.py --data DB100K --batch 256 \ --hid_drop 0.3 --feat_drop 0.2 --lr 0.0012 --inp_drop 0.2 --gpu 0 --name db100k_s --way s --train_strategy one_to_x #Parallel fb15k237 python -u acre.py --data FB15k-237 --batch 128 \ --hid_drop 0.5 --feat_drop 0.2 --lr 0.001 --inp_drop 0.3 --gpu 0 --name --fb15k_237_p --way p --train_strategy one_to_x #Parallel fb15k python -u acre.py --data FB15k --batch 256 \ --hid_drop 0.2 --feat_drop 0.2 --lr 0.001 --inp_drop 0.2 --gpu 0 --name --fb15k_p --way p --train_strategy one_to_n #Parallel kinship python -u acre.py --data kinship --batch 128 \ --hid_drop 0.5 --feat_drop 0.2 --lr 0.0001 --inp_drop 0.3 --gpu 0 --name --kinship_p --way p --train_strategy one_to_n #Parallel WN18RR python -u acre.py --data WN18RR --batch 256 \ --hid_drop 0.5 --feat_drop 0.1 --lr 0.00125 --inp_drop 0.3 --gpu 0 --name --wn18rr_p --way p --train_strategy one_to_x #Parallel WN18 python -u acre.py --data WN18 --batch 256 \ --hid_drop 0.3 --feat_drop 0.3 --lr 0.0012 --inp_drop 0.2 --gpu 0 --name --wn18_p --way p --train_strategy one_to_x #Parallel DB100K python -u acre.py --data DB100K --batch 256 \ --hid_drop 0.3 --feat_drop 0.2 --lr 0.0012 --inp_drop 0.2 --gpu 0 --name --db100k_p --way p --train_strategy one_to_x ``` ### Citation ### Acknowledgement Parts of our codes come from [InteractE](https://github.com/malllabiisc/InteractE). Thanks for their contributions.
