SAGE: Structure-Aware Generative Enhancement for Long-Tail Knowledge Graph Completion
TL;DR
A pre-extractor model based on a hybrid architecture of rules and neural networks is introduced, which is used to identify long tail entities in the dataset and generate several candidate tail entities through relationships to improve the inference performance of the model.
Abstract
Knowledge graph completion is one of the core tasks in the field of knowledge graphs, which predicts missing links through inference of existing facts. With the advancement of deep learning technology, utilizing end-to-end deep learning models for knowledge graph completion has become a cutting-edge research direction. However, the performance of current knowledge graph completion models is still limited by text quality and incomplete structure. To address this issue, this paper proposes a method of using large models for data augmentation to improve the inference performance of the model. Specifically, we first introduced a pre-extractor model based on a hybrid architecture of rules and neural networks, which is used to identify long tail entities in the dataset and generate several candidate tail entities through relationships. Then, we use this data to have the Large Language Model infer the most factual triplet. Finally, we use the enhanced dataset for predictive inference. SAGE achieves better results on three standard KGC datasets. For instance, on the FB15K-237 dataset, compared to the SimKGC baseline model, SAGE improves Hits@1 by 1%, Hits@3 by 0.9%, and Hits@10 by 1.6%.