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 .
@article{arxiv.2304.14678,
title = {NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning},
author = {Wen Zhang and Zhen Yao and Mingyang Chen and Zhiwei Huang and Huajun Chen},
journal= {arXiv preprint arXiv:2304.14678},
year = {2023}
}