wzh-nlp / ookg

VNC

This repo is a PyTorch implementation of "Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations" (CIKM 2023).

Introduction

Current KGE methods for out-of-knowledge-graph (OOKG) entities still face two key challenges:

1. Identifying inter-rule correlations to further facilitate the inference process;
2. Capturing interactions among rule mining, rule inference, and embedding to enhance both rule and embedding learning.

In this paper, we propose a virtual neighbor network with inter-rule correlations (VNC) to address the above challenges. VNC consists of three main components: (i) rule mining, (ii) rule inference, and (iii) embedding. To identify useful complex patterns in knowledge graphs, both logic rules and inter-rule correlations are extracted from knowledge graphs based on operations over relation embeddings. To reduce data sparsity, virtual networks for OOKG entities are predicted and assigned soft labels by optimizing a rule-constrained problem. We also devise an iterative framework to capture the underlying interactions between rule and embedding learning.

Main results

We assess the performance of VNC for link prediction on publicly available datasets, namely YAGO37 and FB15K.

Get started

Please note that we utilize Deep Graph Library (DGL) version 0.5.3. We list commands with different hyperparameters in run.sh. For example, you can train and evaluate VNC with the following command:

python -u main_run.py --gpu 0 --penalty 0.5 --epochs 4000 --model "distmult" --embedding-dim 100 --evaluate-every 50  --data fb15k  --sub-data subject-10 --isSigmoid True  --n-bases 100  --batch-size 30000  --n_epochs_aux 200

Reference

@inproceedings{wang2023Iterative,
  author    = {Zihan Wang, Kai Zhao, Yongquan He, Zhumin Chen, Pengjie Ren, Maarten de Rijke, and Zhaochun Ren},
  title     = {Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations},
  booktitle = {{CIKM} '23: the 32nd ACM International Conference on Information and Knowledge Management, October 21–25, 2023, Birmingham, UK.},
  year      = {2023}
}