This repository contains the code and the datasets of Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs. Jiajun Chen, Huarui He, Feng Wu, Jie Wang. AAAI 2021. [arXiv]
The code is based on Python 3.7. You can use the following command to create a environment and enter it.
conda create --name TACT-env python=3.7
source activate TACT-envAll the required packages can be installed by running
pip install -r requirements.txtTo test the code, run the following commands.
cd code/AUC-PR
bash run_once.sh WN18RR_v1 8 1 2 0.01 0.01 demo_test 10 4Notice that, for the first time you run the code, it would take some time to sample the subgraph.
bash {run_once.sh | run_five.sh} <dataset> <gamma: margin in the loss function> \
<negative_sample_size> <enclosing_subgraph_hop_number> <learning_rate> <weight_decay> \
<experiment_id> <max_epoch> <gpu_id>
{ | }: Mutually exclusive items. Choose one from them.< >: Placeholder for which you must supply a value.
To reproduce the results, run the following commands.
#################################### AUC-PR ####################################
cd code/AUC-PR
bash run_five.sh WN18RR_v1 8 1 2 0.01 0.01 demo 10 0
bash run_five.sh WN18RR_v2 8 1 2 0.01 0.01 demo 10 0
bash run_five.sh WN18RR_v3 8 1 2 0.01 0.01 demo 10 0
bash run_five.sh WN18RR_v4 8 1 2 0.01 0.01 demo 10 0
bash run_five.sh fb237_v1 16 1 2 0.01 0.01 demo 10 0
bash run_five.sh fb237_v2 16 1 2 0.01 0.01 demo 10 0
bash run_five.sh fb237_v3 16 1 2 0.01 0.01 demo 10 0
bash run_five.sh fb237_v4 16 1 2 0.01 0.01 demo 10 0
bash run_five.sh nell_v1 10 1 2 0.01 0.01 demo 10 0
bash run_five.sh nell_v2 10 1 2 0.01 0.01 demo 10 0
bash run_five.sh nell_v3 10 1 2 0.01 0.01 demo 10 0
bash run_five.sh nell_v4 10 1 2 0.01 0.1 demo 10 0
#################################### Ranking #############################
cd code/Ranking
bash run_five.sh WN18RR_v1 8 8 2 0.01 0.01 demo 10 0
bash run_five.sh WN18RR_v2 8 8 2 0.01 0.01 demo 10 0
bash run_five.sh WN18RR_v3 8 8 2 0.01 0.01 demo 10 0
bash run_five.sh WN18RR_v4 8 8 2 0.01 0.01 demo 10 0
bash run_five.sh fb237_v1 16 8 2 0.005 0.01 demo 10 0
bash run_five.sh fb237_v2 16 8 2 0.005 0.01 demo 10 0
bash run_five.sh fb237_v3 16 8 2 0.005 0.01 demo 10 0
bash run_five.sh fb237_v4 16 8 2 0.005 0.01 demo 10 0
bash run_five.sh nell_v1 10 8 2 0.01 0.01 demo 10 0
bash run_five.sh nell_v2 10 8 2 0.01 0.01 demo 10 0
bash run_five.sh nell_v3 10 8 2 0.01 0.01 demo 10 0
bash run_five.sh nell_v4 16 8 2 0.008 0.01 demo 5 0Remark: We run each experiment five times and report the mean results.
If you find this code useful, please consider citing the following paper.
@article{DBLP:journals/corr/abs-2103-03642,
author = {Jiajun Chen and
Huarui He and
Feng Wu and
Jie Wang},
title = {Topology-Aware Correlations Between Relations for Inductive Link Prediction
in Knowledge Graphs},
journal = {CoRR},
volume = {abs/2103.03642},
year = {2021},
url = {https://arxiv.org/abs/2103.03642},
archivePrefix = {arXiv},
eprint = {2103.03642},
timestamp = {Mon, 15 Mar 2021 17:30:55 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2103-03642.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}We refer to the code of GraIL. Thanks for their contributions.
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