mlsanityData to 7 Oct 2026

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

arXiv 2202.12571 ↗First posted 25 Feb 2022cs.LG cs.AI cs.CLcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 18 of its 35 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

18 of 35 references.

References · 18

  1. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  2. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  3. 2016Complex Embeddings for Simple Link Predictioncited by 897
  4. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  5. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  6. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
  7. 2019Knowledge Enhanced Contextual Word Representationscited by 294
  8. 2018SimplE Embedding for Link Prediction in Knowledge Graphscited by 195
  9. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  10. 2020BoxE: A Box Embedding Model for Knowledge Base Completioncited by 105
  11. 2020PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddingscited by 77
  12. 2020DGL-KE: Training Knowledge Graph Embeddings at Scalecited by 67
6 more
  1. 2019Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoningcited by 63
  2. 2017Knowledge Graph Embedding with Iterative Guidance from Soft Rulescited by 51
  3. 2019Interaction Embeddings for Prediction and Explanation in Knowledge Graphscited by 40
  4. 2018Improving Knowledge Graph Embedding Using Simple Constraintscited by 36
  5. 2019Pykg2vec: A Python Library for Knowledge Graph Embeddingcited by 9
  6. 2020TorchKGE: Knowledge Graph Embedding in Python and PyTorchcited by 9

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 2.

8 papers.

  1. 2022Tele-Knowledge Pre-training for Fault Analysiscited by 18
  2. 2023Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embeddingcited by 8
  3. 2022LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddingscited by 5
  4. 2022μKG: A Library for Multi-source Knowledge Graph Embeddings and Applicationscited by 4
  5. 2023Analogical Inference Enhanced Knowledge Graph Embeddingcited by 3
  6. 2024Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinkingcited by 2
  7. 2023NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learningcited by 1
  8. 2024SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Networkcited by 0