论文标题
Neurolkg：一个开源库，用于多样化的知识图表
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
论文作者
论文摘要
Neurolkg是一个基于Python的开源图书馆，用于多样化的知识图表。它实现了三种不同的知识图嵌入（KGE）方法，包括常规kges，基于GNN的KGE和基于规则的KGE。通过一个统一的框架，Neurolkg成功地将这些方法的链接预测结果复制在基准测试中，从而使用户摆脱了重新实现它们的辛苦任务，尤其是对于某些最初用非Python编程语言编写的方法。此外，Neurolkg是高度可配置和可扩展的。它提供了可以混合和适应的各种解耦模块。因此，使用Neurolkg，开发人员和研究人员可以快速实施自己的设计模型，并获得最佳的培训方法，以有效地实现最佳性能。我们在http://neuralkg.zjukg.cn建立了一个网站，以组织一个开放和共享的KG代表学习社区。源代码均在https://github.com/zjukg/neuralkg上公开发布。
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.
