A Probabilistic Framework for Knowledge Graph Data Augmentation
This addresses data scarcity for knowledge graph completion tasks, but appears incremental as it builds on existing augmentation methods.
The authors tackled data scarcity in knowledge graph completion by developing NNMFAug, a probabilistic framework for data augmentation, which improved performance over baselines in experiments on popular models and benchmarks.
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.