Table of Content

1. Introduction
2. Getting Started
   • Datasets
   • Installation
3. Experiments
   • Training & Testing

Two-view Graph Neural Networks for Knowledge Graph Completion

We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms strong baselines on seven benchmark datasets for knowledge graph completion.

Details of the model architecture and experimental results can be found in our following paper:

@inproceedings{wge,
    title     = {{Two-view Graph Neural Networks for Knowledge Graph Completion}},
    author    = {Vinh Tong and Dai Quoc Nguyen and Dinh Phung and Dat Quoc Nguyen},
    booktitle = {Proceedings of the 20th Extended Semantic Web Conference},
    year      = {2023}
}

Please CITE our paper whenever our model implementation is used to help produce published results or incorporated into other software.

Getting Started

Datasets

LitWD, CodEx and FB15k237 datasets are stored in data.zip. Please xtract the zip file before running the code.

Installation:

# clone the repo
git clone https://github.com/vinhsuhi/WGE.git
cd WGE

# install dependencies
pip install -r requirements.txt

Experiments

Training and Testing

python main.py --dataset codex-s --lr 0.0005 --beta 0.2 --emb_dim 256
python main.py --dataset codex-m --lr 0.0005 --beta 0.2 --emb_dim 256
python main.py --dataset codex-l --lr 0.0001 --beta 0.2 --emb_dim 256