  Ontologies have proven beneficial in different settings that make use of
textual reviews. However, manually constructing ontologies is a laborious and
time-consuming process in need of automation. We propose a novel methodology
for automatically extracting ontologies, in the form of meronomies, from
product reviews, using a very limited amount of hand-annotated training data.
We show that the ontologies generated by our method outperform hand-crafted
ontologies (WordNet) and ontologies extracted by existing methods (Text2Onto
and COMET) in several, diverse settings. Specifically, our generated ontologies
outperform the others when evaluated by human annotators as well as on an
existing Q&A dataset from Amazon. Moreover, our method is better able to
generalise, in capturing knowledge about unseen products. Finally, we consider
a real-world setting, showing that our method is better able to determine
recommended products based on their reviews, in alternative to using Amazon's
standard score aggregations.
