  Large ontologies still pose serious challenges to state-of-the-art ontology
alignment systems. In this paper we present an approach that combines a neural
embedding model and logic-based modules to accurately divide an input ontology
matching task into smaller and more tractable matching (sub)tasks. We have
conducted a comprehensive evaluation using the datasets of the Ontology
Alignment Evaluation Initiative. The results are encouraging and suggest that
the proposed method is adequate in practice and can be integrated within the
workflow of systems unable to cope with very large ontologies.
