  Knowledge graphs (KG) have served as the key component of various natural
language processing applications. Commonsense knowledge graphs (CKG) are a
special type of KG, where entities and relations are composed of free-form
text. However, previous works in KG completion and CKG completion suffer from
long-tail relations and newly-added relations which do not have many know
triples for training. In light of this, few-shot KG completion (FKGC), which
requires the strengths of graph representation learning and few-shot learning,
has been proposed to challenge the problem of limited annotated data. In this
paper, we comprehensively survey previous attempts on such tasks in the form of
a series of methods and applications. Specifically, we first introduce FKGC
challenges, commonly used KGs, and CKGs. Then we systematically categorize and
summarize existing works in terms of the type of KGs and the methods. Finally,
we present applications of FKGC models on prediction tasks in different areas
and share our thoughts on future research directions of FKGC.
