Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-ind is the first library of inductive KGRL as an important update of NeuralKG library. It includes standardized processes, rich existing methods, decoupled modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy for researchers and engineers to reproduce, redevelop, and compare inductive KGRL methods. The library, experimental methodologies, and model re-implementing results of NeuralKG-ind are all publicly released at https://github.com/zjukg/NeuralKG/tree/ind .

翻译：鉴于知识图谱的动态特性，近年来涌现出许多面向新实体预测的归纳式知识图谱表示学习（KGRL）研究。作为NeuralKG库的重要更新，NeuralKG-ind是首个归纳式KGRL库，包含标准化流程、丰富的现有方法、解耦模块和全面的评估指标。借助NeuralKG-ind，研究人员和工程师可以轻松复现、扩展和比较归纳式KGRL方法。该库、实验方法论及模型复现结果均已公开发布于https://github.com/zjukg/NeuralKG/tree/ind。