  Knowledge graphs (KGs) have been successfully applied to the analysis of
complex scientific and technological domains, with automatic KG generation
methods typically building upon relation extraction models capturing
fine-grained relations between domain entities in text. While these relations
are fully applicable across scientific areas, existing models are trained on
few domain-specific datasets such as SciERC and do not perform well on new
target domains. In this paper, we experiment with leveraging in-context
learning capabilities of Large Language Models to perform schema-constrained
data annotation, collecting in-domain training instances for a
Transformer-based relation extraction model deployed on titles and abstracts of
research papers in the Architecture, Construction, Engineering and Operations
(AECO) domain. By assessing the performance gain with respect to a baseline
Deep Learning architecture trained on off-domain data, we show that by using a
few-shot learning strategy with structured prompts and only minimal expert
annotation the presented approach can potentially support domain adaptation of
a science KG generation model.
