Awesome
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
This repo provides the source code & data of our paper: Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs (IJCAI 2022).
Dependencies
- conda create -n temp python=3.7 -y
- PyTorch 1.8.1
- tensorboardX 2.5.1
- numpy 1.21.6
Running the code
Dataset
- Download the datasets from here.
- Create the root directory ./data and put the datasets in.
- It should be noted that we only provide the data provided by the BetaE paper (the corresponding dataset in Table 7 of the paper). For the dataset corresponding to Q2B (the corresponding dataset in Table 1 of the paper), you can download it from here.
- You need to move id2type.pkl, type2id.pkl, entity_type.npy and relation_type.npy in the corresponding BetaE's dataset to the corresponding Q2B's dataset.
Models
- We added our TEMP module to the above four models.
Training Model
- Take the GQE model in the FB15k-237 dataset as an example:
Generalization
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},
}
Acknowledgement
We refer to the code of KGReasoning. Thanks for their contributions.