NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning

• URL: http://arxiv.org/abs/2304.14678v1
• Date: Fri, 28 Apr 2023 08:09:08 GMT
• Title: NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
• Authors: Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang and Huajun Chen
• Abstract summary: NeuralKG-ind is the first library of inductive knowledge graph representation learning. It includes standardized processes, rich existing methods, decoupled modules, and comprehensive evaluation metrics.
• Score: 20.717388858072106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 .

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