  With the development of pre-trained language models, many prompt-based
approaches to data-efficient knowledge graph construction have been proposed
and achieved impressive performance. However, existing prompt-based learning
methods for knowledge graph construction are still susceptible to several
potential limitations: (i) semantic gap between natural language and output
structured knowledge with pre-defined schema, which means model cannot fully
exploit semantic knowledge with the constrained templates; (ii) representation
learning with locally individual instances limits the performance given the
insufficient features, which are unable to unleash the potential analogical
capability of pre-trained language models. Motivated by these observations, we
propose a retrieval-augmented approach, which retrieves schema-aware Reference
As Prompt (RAP), for data-efficient knowledge graph construction. It can
dynamically leverage schema and knowledge inherited from human-annotated and
weak-supervised data as a prompt for each sample, which is model-agnostic and
can be plugged into widespread existing approaches. Experimental results
demonstrate that previous methods integrated with RAP can achieve impressive
performance gains in low-resource settings on five datasets of relational
triple extraction and event extraction for knowledge graph construction. Code
is available in https://github.com/zjunlp/RAP.
