NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs

Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Yajing Xu, Ningyu Zhang, Zezhong Xu, Zonggang Yuan, Feiyu Xiong, Huajun Chen

Abstract: NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three kinds of Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs. With a unified framework, NeuralKG successfully reproduces link prediction results of these methods on benchmarks, freeing users from the laborious task of reimplementing them, especially for some methods originally written in non-python programming languages. Besides, NeuralKG is highly configurable and extensible. It provides various decoupled modules that can be mixed and adapted to each other. Thus with NeuralKG, developers and researchers can quickly implement their own designed models and obtain the optimal training methods to achieve the best performance efficiently. We built a website http://neuralkg.zjukg.org to organize an open and shared KG representation learning community. The library, experimental methodologies, and model reimplement results of NeuralKG are all publicly released at https://github.com/zjukg/NeuralKG.