Large ontologies still pose serious challenges to state of the art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively segment an input ontology matching task into smaller and more tractable (sub)matching 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 methods are adequate in practice and can be integrated within the workflow of state of the art systems.

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