Relational multi-scale metric learning for few-shot knowledge graph completion

Abstract

Few-shot knowledge graph completion (FKGC) refers to the task of inferring missing facts in a knowledge graph by utilizing a limited number of reference entities. Most FKGC methods assume a single similarity metric, which leads to a single feature space and makes it difficult to separate positive and negative samples effectively. Therefore, we propose a multi-scale relational metric network (MSRMN) specifically designed for FKGC, which integrates multiple scales of measurement methods to learn a more comprehensive and compact feature space. In this study, we design a complete neighbor random sampling algorithm to sample complete one-hop neighbor information, and aggregate both one-hop and multi-hop neighbor information to enhance entity representations. Then, MSRMN adaptively obtains prototype representations of relations and integrates three different scales of measurement methods to learn a more comprehensive feature space and a more discriminative feature mapping, enabling positive query entity pairs to obtain higher measurement scores. Evaluation of MSRMN on two public datasets for link prediction demonstrates that MSRMN attains top-performing outcomes across various few-shot sizes on the NELL dataset.