  Concept learning deals with learning description logic concepts from a
background knowledge and input examples. The goal is to learn a concept that
covers all positive examples, while not covering any negative examples. This
non-trivial task is often formulated as a search problem within an infinite
quasi-ordered concept space. Although state-of-the-art models have been
successfully applied to tackle this problem, their large-scale applications
have been severely hindered due to their excessive exploration incurring
impractical runtimes. Here, we propose a remedy for this limitation. We
reformulate the learning problem as a multi-label classification problem and
propose a neural embedding model (NERO) that learns permutation-invariant
embeddings for sets of examples tailored towards predicting $F_1$ scores of
pre-selected description logic concepts. By ranking such concepts in descending
order of predicted scores, a possible goal concept can be detected within few
retrieval operations, i.e., no excessive exploration. Importantly, top-ranked
concepts can be used to start the search procedure of state-of-the-art symbolic
models in multiple advantageous regions of a concept space, rather than
starting it in the most general concept $\top$. Our experiments on 5 benchmark
datasets with 770 learning problems firmly suggest that NERO significantly
(p-value <1%) outperforms the state-of-the-art models in terms of $F_1$ score,
the number of explored concepts, and the total runtime. We provide an
open-source implementation of our approach.
