Rule-based data augmentation for knowledge graph embedding - Guangyao Li; Zequn Sun; Lei Qian; Qiang Guo; Wei Hu (AI Open, 2021)
Knowledge graph (KG) embedding models suffer from the incompleteness issue of observed facts. Different from existing solutions that incorporate additional information or employ expressive and complex embedding techniques, we propose to augment KGs by iteratively mining logical rules from the observed facts and then using the rules to generate new relational triples. We incrementally train KG embeddings with the coming of new augmented triples, and leverage the embeddings to validate these new triples. To guarantee the quality of the augmented data, we filter out the noisy triples based on a propagation mechanism during the validation. The mined rules and rule groundings are human-understandable, and can make the augmentation procedure reliable. Our KG augmentation framework is applicable to any KG embedding models with no need to modify their embedding techniques. Our experiments on two popular embedding-based tasks (i.e., entity alignment and link prediction) show that the proposed framework can bring significant improvement to existing KG embedding models on most benchmark datasets.