A Probabilistic Framework for Knowledge Graph Data Augmentation

Jatin Chauhan, Priyanshu Gupta, Pasquale Minervini

Abstract:

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.