mlsanityData to 7 Oct 2026

A Probabilistic Framework for Knowledge Graph Data Augmentation

arXiv 2110.13205 ↗First posted 25 Oct 2021cs.LGcited by 1

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Abstract (arXiv)

We present NNMFAug, a probabilistic framework to perform data augmentation for the task of knowledge graph completion to counter the problem of data scarcity, which can enhance the learning process of neural link predictors. Our method can generate potentially diverse triples with the advantage of being efficient and scalable as well as agnostic to the choice of the link prediction model and dataset used. Experiments and analysis done on popular models and benchmarks show that NNMFAug can bring notable improvements over the baselines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 25 Oct 2021.

Builds on

16 of 29 references.

References · 16

  1. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  2. 2016Complex Embeddings for Simple Link Predictioncited by 897
  3. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  4. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  5. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  6. 2018Generalizing to Unseen Domains via Adversarial Data Augmentationcited by 451
  7. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  8. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  9. 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
  10. 2020Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Rewardcited by 66
  11. 2018RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Informationcited by 47
  12. 2014Open Question Answering with Weakly Supervised Embedding Modelscited by 26
4 more
  1. 2019Further advantages of data augmentation on convolutional neural networkscited by 19
  2. 2020Higher-Order Spectral Clustering of Directed Graphscited by 17
  3. 2017Adversarial Sets for Regularising Neural Link Predictorscited by 15
  4. 2019A Preliminary Study on Data Augmentation of Deep Learning for Image Classificationcited by 8

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023How to Turn Your Knowledge Graph Embeddings into Generative Modelscited by 20