  NeuralKG is an open-source Python-based library for diverse representation
learning of knowledge graphs. It implements three different series 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 an website in
http://neuralkg.zjukg.cn to organize an open and shared KG representation
learning community. The source code is all publicly released at
https://github.com/zjukg/NeuralKG.
