  Few-shot Learning (FSL) is aimed to make predictions based on a limited
number of samples. Structured data such as knowledge graphs and ontology
libraries has been leveraged to benefit the few-shot setting in various tasks.
However, the priors adopted by the existing methods suffer from challenging
knowledge missing, knowledge noise, and knowledge heterogeneity, which hinder
the performance for few-shot learning. In this study, we explore knowledge
injection for FSL with pre-trained language models and propose
ontology-enhanced prompt-tuning (OntoPrompt). Specifically, we develop the
ontology transformation based on the external knowledge graph to address the
knowledge missing issue, which fulfills and converts structure knowledge to
text. We further introduce span-sensitive knowledge injection via a visible
matrix to select informative knowledge to handle the knowledge noise issue. To
bridge the gap between knowledge and text, we propose a collective training
algorithm to optimize representations jointly. We evaluate our proposed
OntoPrompt in three tasks, including relation extraction, event extraction, and
knowledge graph completion, with eight datasets. Experimental results
demonstrate that our approach can obtain better few-shot performance than
baselines.
