NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
This provides a unified and extensible tool for developers and researchers in knowledge graph representation learning, though it is incremental as it builds on existing methods.
The authors tackled the problem of reimplementing diverse knowledge graph embedding methods by developing NeuralKG, an open-source library that reproduces link prediction results on benchmarks, freeing users from laborious reimplementation tasks.
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.