  We consider the problem of finding plausible knowledge that is missing from a
given ontology, as a generalisation of the well-studied taxonomy expansion
task. One line of work treats this task as a Natural Language Inference (NLI)
problem, thus relying on the knowledge captured by language models to identify
the missing knowledge. Another line of work uses concept embeddings to identify
what different concepts have in common, taking inspiration from cognitive
models for category based induction. These two approaches are intuitively
complementary, but their effectiveness has not yet been compared. In this
paper, we introduce a benchmark for evaluating ontology completion methods and
thoroughly analyse the strengths and weaknesses of both approaches. We find
that both approaches are indeed complementary, with hybrid strategies achieving
the best overall results. We also find that the task is highly challenging for
Large Language Models, even after fine-tuning.
