The exponentially growing volume of astronomical data from large-scale multi-wavelength surveys highlights the need for a new approach in astronomical catalogue curation. Such catalogues are key for the rapid characterization of astronomical sources (particularly important in the era of time-domain and multi-messenger Astrophysics) and to maximize the impact of major surveys by providing a comprehensive picture of the characteristics of the different sources. However, the commonly used approach of manual catalogue curation cannot be applied in this new era, as the ever-increasing pace of data, which are presented not only in organized catalogues, but also in publications on individual objects or samples of objects, poses major challenges rendering catalogues outdated quite soon.

In the meantime, the modelling of domain knowledge in the form of semantically enriched knowledge graphs (supported by ontologies) is becoming increasingly popular for various domains (e.g., medicine, biology). A knowledge graph (KG) is a knowledge base (KB) using a graph-structured data model for interlinking concepts (that represent objects, events or other entities), forming meaningful connections among them. The use of an ontology in tandem with a KG is the standard way of giving semantics to the entities and relationships of a KG. This machine-understandable, formal definition of entities allows richer inference, analytics and querying capabilities for the information encoded in the KG.