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LARS-research / RED-GNN

Knowledge Graph Reasoning with Relational Digraph. WebConf 2022
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RED-GNN

The code for our paper "Knowledge Graph Reasoning with Relational Digraph" which has been accepted by WebConf 2022.

Instructions

A quick instruction is given for readers to reproduce the whole process.

Requirements

For transductive reasoning

cd transductive
python -W ignore train.py --data_path=data/WN18RR

For inductive reasoning

cd inductive
python -W ignore train.py --data_path=data/WN18RR_v1

Data splition in transductive setting

We follow the rule mining methods, i.e., Neural-LP and DRUM, to randomly split triplets in the original train.txt file into two files facts.txt and train.txt with ratio 3:1. This step is to make sure that the query triplets will not be leaked in the fact triplets used in RED-GNN. Empirically, increasing the ratio of facts, e.g. from 3:1 to 4:1, will lead to better performance.

Transductive results

Metrics Family UMLS WN18RR FB15k-237 NELL-995
MRR .992 .964 .533 .374 .543
Hit@1 (%) 98.8 94.6 48.5 28.3 47.6
Hit@10 (%) 99.7 99.0 62.4 55.8 65.1

Inductive results

We use the full set of negative samples in evaluating the inductive results. This is different from the setting of 50 negative samples in GraIL.

metrics WN-V1 WN-V2 WN-V3 WN-V4 FB-V1 FB-V2 FB-V3 FB-V4 NL-V1 NL-V2 NL-V3 NL-V4
MRR .701 .690 .427 .651 .369 .469 .445 .442 .637 .419 .436 .363
Hit@1 (%) 65.3 63.3 36.8 60.6 30.2 38.1 35.1 34.0 52.5 31.9 34.5 25.9
Hit@10 (%) 79.9 78.0 52.4 72.1 48.3 62.9 60.3 62.1 86.6 60.1 59.4 55.6

Readers are welcomed to fork this repository to reproduce the experiments and follow our work. Please kindly cite our paper

@inproceedings{zhang2022redgnn,
    title={Knowledge graph reasoning with relational digraph},
    author={Zhang, Yongqi and Yao, Quanming},
    booktitle={Proceedings of the ACM Web Conference 2022},
    pages={912--924},
    year={2022}
}