# NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs

arXiv: https://arxiv.org/abs/2202.12571 · HTML: https://mlsanity.com/paper/2202.12571/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [SimplE Embedding for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1802.04868/) (2018)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [BoxE: A Box Embedding Model for Knowledge Base Completion](https://mlsanity.com/paper/2007.06267/) (2020)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [DGL-KE: Training Knowledge Graph Embeddings at Scale](https://mlsanity.com/paper/2004.08532/) (2020)
- [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019)
- [Knowledge Graph Embedding with Iterative Guidance from Soft Rules](https://mlsanity.com/paper/1711.11231/) (2017)
- [Interaction Embeddings for Prediction and Explanation in Knowledge Graphs](https://mlsanity.com/paper/1903.04750/) (2019)
- [Improving Knowledge Graph Embedding Using Simple Constraints](https://mlsanity.com/paper/1805.02408/) (2018)
- [Pykg2vec: A Python Library for Knowledge Graph Embedding](https://mlsanity.com/paper/1906.04239/) (2019)
- [TorchKGE: Knowledge Graph Embedding in Python and PyTorch](https://mlsanity.com/paper/2009.02963/) (2020)

## Built on by (most cited first, 8 of 8)

- [Tele-Knowledge Pre-training for Fault Analysis](https://mlsanity.com/paper/2210.11298/) (2022)
- [Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding](https://mlsanity.com/paper/2302.01849/) (2023)
- [LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings](https://mlsanity.com/paper/2210.00305/) (2022)
- [μKG: A Library for Multi-source Knowledge Graph Embeddings and Applications](https://mlsanity.com/paper/2207.11442/) (2022)
- [Analogical Inference Enhanced Knowledge Graph Embedding](https://mlsanity.com/paper/2301.00982/) (2023)
- [Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking](https://mlsanity.com/paper/2501.00244/) (2024)
- [NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning](https://mlsanity.com/paper/2304.14678/) (2023)
- [SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network](https://mlsanity.com/paper/2407.02762/) (2024)

## Abstract (arXiv)

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, especially for some methods originally written in non-python programming languages. Besides, NeuralKG is highly configurable and extensible. 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. We built an website in http://neuralkg.zjukg.cn to organize an open and shared KG representation learning community. The source code is all publicly released at https://github.com/zjukg/NeuralKG.