The official implementation of the paper: Knowledge Graph Reasoning over Entities and Numerical Values
We have added the raw data for constructing the KGs in to ./data/.
The raw data we use is from the following github repository, if you use the data, please also cite their paper: https://github.com/mniepert/mmkb
The input files for the models can be downloaded here. After decompressing, they should be put in the root directory of this repository. The are two directories in the zip file, and the small one serves for debuging and unit testing. They can be downloaded from here.
To run the baseline code please use the following scripts:
scripts_gqe\train_gqe_DB15k_baseline.sh
scripts_q2b\train_q2b_DB15k_baseline.sh
scripts_q2p\train_q2p_DB15k_baseline.sh
To run the NRN model with the sinusodal numerical encoder:
scripts_gqe\train_gqe_DB15k_value_typed_position.sh
scripts_q2b\train_q2b_DB15k_value_typed_position.sh
scripts_q2p\train_q2p_DB15k_value_typed_position.sh
To run the NRN model with the DICE numerical encoder:
scripts_gqe\train_gqe_DB15k_value_typed_dice.sh
scripts_q2b\train_q2b_DB15k_value_typed_dice.sh
scripts_q2p\train_q2p_DB15k_value_typed_dice.sh
During the running process, you can monitor the training process via tensorboard. The default log storage will be logs/gradient_tape .
If you have any questions, please contact me vis [email protected].
If you find the paper and code useful, please cite our paper.
@inproceedings{DBLP:conf/kdd/BaiLLYYS23,
author = {Jiaxin Bai and
Chen Luo and
Zheng Li and
Qingyu Yin and
Bing Yin and
Yangqiu Song},
editor = {Ambuj K. Singh and
Yizhou Sun and
Leman Akoglu and
Dimitrios Gunopulos and
Xifeng Yan and
Ravi Kumar and
Fatma Ozcan and
Jieping Ye},
title = {Knowledge Graph Reasoning over Entities and Numerical Values},
booktitle = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery
and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023},
pages = {57--68},
publisher = {{ACM}},
year = {2023},
url = {https://doi.org/10.1145/3580305.3599399},
doi = {10.1145/3580305.3599399},
timestamp = {Mon, 25 Sep 2023 08:29:22 +0200},
biburl = {https://dblp.org/rec/conf/kdd/BaiLLYYS23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}