  We propose a novel approach to learn domain-specific plausible materials for
components in the vehicle repair domain by probing Pretrained Language Models
(PLMs) in a cloze task style setting to overcome the lack of annotated
datasets. We devise a new method to aggregate salient predictions from a set of
cloze query templates and show that domain-adaptation using either a small,
high-quality or a customized Wikipedia corpus boosts performance. When
exploring resource-lean alternatives, we find a distilled PLM clearly
outperforming a classic pattern-based algorithm. Further, given that 98% of our
domain-specific components are multiword expressions, we successfully exploit
the compositionality assumption as a way to address data sparsity.
