  Mentions of new concepts appear regularly in texts and require automated
approaches to harvest and place them into Knowledge Bases (KB), e.g.,
ontologies and taxonomies. Existing datasets suffer from three issues, (i)
mostly assuming that a new concept is pre-discovered and cannot support
out-of-KB mention discovery; (ii) only using the concept label as the input
along with the KB and thus lacking the contexts of a concept label; and (iii)
mostly focusing on concept placement w.r.t a taxonomy of atomic concepts,
instead of complex concepts, i.e., with logical operators. To address these
issues, we propose a new benchmark, adapting MedMentions dataset (PubMed
abstracts) with SNOMED CT versions in 2014 and 2017 under the Diseases
sub-category and the broader categories of Clinical finding, Procedure, and
Pharmaceutical / biologic product. We provide usage on the evaluation with the
dataset for out-of-KB mention discovery and concept placement, adapting recent
Large Language Model based methods.
