NeuralKG Library

Easy Representation for Knowledge Graphs!

Knowledge Graph Representation Learning

Easy & Diverse

NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs. It provides various decoupled modules that can be mixed and adapted to each other. Thus with NeuralKG, developers and researchers can quickly implement their own designed models and obtain the optimal training methods to achieve the best performance efficiently.

Knowledge Graph Embeddings

C-KGEs

NeuralKG contains various Knowledge Graph Embedding methods and will be continuous updated with new KGEs.[Learn more]

Rule Enhanced Representations

Rule-based KGEs

NeuralKG contains a series of rule enhanced representation learning methods, making neural-symbolic reasoning easy. [Learn more]

Graph Neural Network Models

GNN-based KGEs

NeuralKG contains the most recently proposed graph neural network based representation learning methods for knowledge graph.[Learn more]

FEATURES

Flexible Usage

NeuralKG provides various functional modules and organizes all components by consistent frameworks.

Decoupled Modules. NeuralKG provides various decoupled modules that can be mixed and adapted to each other.

Diverse types of Models. NeuralKG includes conventional KGEs, GNN-based KGEs and Rule-injected KGEs. 

APPLICATIONS

What you can do with NeuralKG?

Knowledge Graphs are widely used in tasks related to entities, such as link prediction and recommendation. It also widely used in many domain applications such as e-commerce and bio-informatics. With NeuralKG, you can easy conduct reasoning over entities.

Common Tasks

NeuralKG provide flexible methods for representing entities and relations in vector space and could be easily utilized in task models.[More]

Domain Applications

With domain KGs, NeuralKG provides easy implementation to discover semantic relationships and relatedness between entities.[More]

WHAT'S NEW

Latest Blogs

We constantly write blogs related to NeuralKG, including updates of the toolkits, hands-on domain applications, project experience sharing, etc.

NeuralKG for KGQA

Knowledge Graphs (KG) consists of a large number of entities and relations among them as typed edges…

C-KGEs

NeuralKG reproduces the conventional knowledge graph embedding models, namely C-KGEs, which are desi…

GNN-based KGEs

GNN-based KGE methods usually add a graph neural network (GNN) structure after the embeddings of ent…

Learn More

Contact Us

NeuralKG core team provides long-term technical support. You can follow or contact us via Github, Gitee, Twitter, Email, etc. We welcome all contributions that will make NeuralKG better. Please feel free to contact us if there are any good suggestions.

NeuralKG Core Team

Zhejiang University

Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Zezhong Xu, Yajing Xu, Ningyu Zhang, Huajun Chen