  Knowledge base construction is crucial for summarising, understanding and
inferring relationships between biomedical entities. However, for many
practical applications such as drug discovery, the scarcity of relevant facts
(e.g. gene X is therapeutic target for disease Y) severely limits a domain
expert's ability to create a usable knowledge base, either directly or by
training a relation extraction model.
  In this paper, we present a simple and effective method of extracting new
facts with a pre-specified binary relationship type from the biomedical
literature, without requiring any training data or hand-crafted rules. Our
system discovers, ranks and presents the most salient patterns to domain
experts in an interpretable form. By marking patterns as compatible with the
desired relationship type, experts indirectly batch-annotate candidate pairs
whose relationship is expressed with such patterns in the literature. Even with
a complete absence of seed data, experts are able to discover thousands of
high-quality pairs with the desired relationship within minutes. When a small
number of relevant pairs do exist - even when their relationship is more
general (e.g. gene X is biologically associated with disease Y) than the
relationship of interest - our system leverages them in order to i) learn a
better ranking of the patterns to be annotated or ii) generate weakly labelled
pairs in a fully automated manner.
  We evaluate our method both intrinsically and via a downstream knowledge base
completion task, and show that it is an effective way of constructing knowledge
bases when few or no relevant facts are already available.
