Understanding large ontologies - by humans or machines - is both a struggle and crucially important for performing ontology engineering tasks such as ontology reuse, ontology matching, ontology evaluation, and (federated) querying [2]. According to [6], existing visualisation tools fail in providing overviews of large ontologies, which is crucial for ontology understanding, while none of them allows to compare multiple ontologies. Besides the layout and interaction features, the problem lays in the lack of effective methods for producing summaries of large ontologies. Many summarisation approaches focus on analysing the data level, e.g. to reduce the size of a knowledge graph and allow simplified queries for testing its coverage [16, 3]. Available summarisation methods addressing the conceptual level are based on extractive approaches that select and return a subset of nodes from the original ontology, i.e. the key concepts, as a summary [16]. However, an overall understanding of all the facts an ontology can represent, and a comparison between multiple ontologies, are not supported. For example, we may identify that in a cultural heritage ontology the concepts Cultural Property and Collection are key ones, however this is insufficient to understand if one ontology allows to answer whether a cultural property has been in a collection. Two ontologies having the same key concept would appear they address the same modelling problem, which may not be the case.