Awesome

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

pytorch 1.9.1+cu102
torch_scatter 2.0.9

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}
}