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https://github.com/zhiweihu1103/QE-TEMP
[IJCAI2022] Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
https://github.com/zhiweihu1103/QE-TEMP
Last synced: about 2 months ago
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[IJCAI2022] Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
- Host: GitHub
- URL: https://github.com/zhiweihu1103/QE-TEMP
- Owner: zhiweihu1103
- Created: 2022-04-20T20:27:53.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-08-09T09:51:59.000Z (about 2 years ago)
- Last Synced: 2024-07-27T15:43:51.566Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 39.1 KB
- Stars: 26
- Watchers: 2
- Forks: 3
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-logical-query - QE-TeMP
README
# 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)](https://arxiv.org/pdf/2205.00782.pdf).
## 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](https://drive.google.com/drive/folders/15ZJo6zuoj0S3Sx_8nz7TKr3Tq7Ku8JMR?usp=sharing).
* 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](http://snap.stanford.edu/betae/KG_data.zip).
* 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
- [x] [GQE](https://arxiv.org/abs/1806.01445)
- [x] [Query2Box](https://arxiv.org/abs/1806.01445)
- [x] [BetaE](https://arxiv.org/abs/2010.11465)
- [x] [LogicE](https://arxiv.org/pdf/2103.00418.pdf)
* 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](https://hub.fastgit.xyz/snap-stanford/KGReasoning). Thanks for their contributions.