NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
This library addresses the need for researchers and engineers to easily work with inductive knowledge graph representation learning, but it is incremental as it builds upon the existing NeuralKG library.
The authors introduced NeuralKG-ind, the first Python library for inductive knowledge graph representation learning, which provides standardized processes, existing methods, decoupled modules, and evaluation metrics to facilitate reproduction, redevelopment, and comparison of methods.
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 .