  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 .
