NeuralKG Library

Easy Representation For Knowledge Graphs

Knowledge Graph Embeddings

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

Rule Enhanced Representations

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

Graph Neural Network Models

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

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.

Demos

Show what you can do with NeuralKG.

Terminal Demonstration, KG-enhanced Applications, Bioinformatics Prediction...

+ Details

Blogs

Blogs of NeuralKG.

Blogs from NeuralKG core team.

+ Blogs

Project

Download or install NeuralKG.

Welcome to use NeuralKG.

+ Github

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标题图层

Brief Introduction to NeuralKG

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. With a unified framework, NeuralKG successfully reproduces link prediction results of these methods on benchmarks, freeing users from the laborious task of reimplementing them… [more]

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NeuralKG for Recommendation

It is worth mentioning that NeuralKG’s use is not limited to the standard benchmarks such as FB15K237, WN18RR. We could construct self-defined Knowledge Graphs datasets and get strong baseline for more comprehensive experiments with NeuralKG. We could use an example to illustrate how to apply NeuralKG on self-defined datasets. [more]

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NeuralKG in Biomolecules

Drug Repurposing Knowledge Graph
Drug Repurposing Knowledge Graph (DRKG) is a comprehensive biological knowledge graph relating genes, compounds, diseases, biological processes, side effects and symptoms. DRKG includes information from six existing databases including DrugBank, Hetionet, GNBR, String, IntAct and DGIdb, and data collected from recent publications particularly related to Covid19. It includes 97,238 entities belonging to 13 entity-types; and 5,874,261 triplets belonging to 107 edge-types.[more]

Gene Ontology
The Gene Ontology(GO) resource provides a computational representation of our current scientific knowledge about the functions of genes (or, more properly, the protein and non-coding RNA molecules produced by genes) from many different organisms, from humans to bacteria. It is widely used to support scientific research, and has been cited in tens of thousands of publications..[more]

DrugBank
In DrugBank, conditions are often specific medical states, including diseases, symptoms, and other health-related characteristics or problems. Conditions may also be used to describe other clinical human phenomena, such as procedures, therapies, and the presence or absence of certain genes.[more]

Contributors

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Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu

Yufeng Huang, Zezhong Xu, Yajing Xu, Ningyu Zhang, huajun chen

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Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu

Yufeng Huang, Zezhong Xu, Yajing Xu, Ningyu Zhang, huajun chen