export DATA_PATH=../data/FB15k-237-betae
export SAVE_PATH=../logs/FB15k-237/gqe_temp
export LOG_PATH=../logs/FB15k-237/gqe_temp.out
export MODEL=temp
export FAITHFUL=no_faithful
export MAX_STEPS=450000 export VALID_STEPS=10000 export SAVE_STEPS=10000 export ENT_TYPE_NEIGHBOR=32 export REL_TYPE_NEIGHBOR=64
CUDA_VISIBLE_DEVICES=0 nohup python -u ../main.py --cuda --do_train --do_valid --do_test \ --data_path $DATA_PATH --save_path $SAVE_PATH -n 128 -b 512 -d 800 -g 24 \ -lr 0.0001 --max_steps $MAX_STEPS --valid_steps $VALID_STEPS --save_checkpoint_steps $SAVE_STEPS \ --cpu_num 1 --geo vec --test_batch_size 16 --tasks "1p.2p.3p.2i.3i.ip.pi.2u.up" --print_on_screen \ --faithful $FAITHFUL --model_mode $MODEL --neighbor_ent_type_samples $ENT_TYPE_NEIGHBOR --neighbor_rel_type_samples $REL_TYPE_NEIGHBOR \
$LOG_PATH 2>&1 &
#### Deductive
export DATA_PATH=../data/FB15k-237-betae export SAVE_PATH=../logs/FB15k-237/gqe_faithful_temp export LOG_PATH=../logs/FB15k-237/gqe_faithful_temp.out export MODEL=temp export FAITHFUL=faithful
export MAX_STEPS=450000 export VALID_STEPS=10000 export SAVE_STEPS=10000 export ENT_TYPE_NEIGHBOR=32 export REL_TYPE_NEIGHBOR=64
CUDA_VISIBLE_DEVICES=0 nohup python -u ../main.py --cuda --do_train --do_valid --do_test \ --data_path $DATA_PATH --save_path $SAVE_PATH -n 128 -b 512 -d 800 -g 24 \ -lr 0.0001 --max_steps $MAX_STEPS --valid_steps $VALID_STEPS --save_checkpoint_steps $SAVE_STEPS \ --cpu_num 1 --geo vec --test_batch_size 16 --tasks "1p.2p.3p.2i.3i.ip.pi.2u.up" --print_on_screen \ --faithful $FAITHFUL --model_mode $MODEL --neighbor_ent_type_samples $ENT_TYPE_NEIGHBOR --neighbor_rel_type_samples $REL_TYPE_NEIGHBOR \
$LOG_PATH 2>&1 &
* Other running scripts can be seen in ./scripts. ## Citation If you find this code useful, please consider citing the following paper.
@article{DBLP:journals/corr/abs-2205-00782, author = {Zhiwei Hu and Víctor Gutiérrez-Basulto and Zhiliang Xiang and Xiaoli Li and Ru Li and Jeff Z. Pan}, title = {Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs}, journal = {CoRR}, volume = {abs/2205.00782}, year = {2022}, url = {https://doi.org/10.48550/arXiv.2205.00782}, doi = {10.48550/arXiv.2205.00782}, eprint = {2205.00782}, }
We refer to the code of KGReasoning. Thanks for their contributions.