Massive volumes of raw astrophysical data are collected every day, and subsequently processed into public catalogues used for scientific research. While deep learning has accelerated astrophysics in recent years, the field has yet to benefit from the Semantic Web’s advances in data representation and management. As a result, most of those catalogues are typically stored as flat CSV/ASCII files in a tabular format, limiting their interoperability and reuse. In this work, we introduce a semantically enriched astrophysical catalogue, in the form of an ontology-backed Knowledge Graph (KG). Our KG is based on a custom, lightweight ontology (DOCBO), and was populated by transforming the Heraklion Extragalactic Catalogue (HECATE) into RDF using two alternative mapping approaches, X3ML and RML, whose characteristics and trade-offs are discussed and compared. Both the mapping rules and the resulting Knowledge Graphs are openly available to foster reuse, interoperability and community feedback. This work, which is part of the interdisciplinary PARSEC project, constitutes a first proof-of-concept effort towards a more generic methodology for representing astrophysical knowledge of various subdomains (e.g., Supernova Remnants and transients) using semantic technologies, thereby forming and automatically populating semantically-enriched KGs based on appropriately defined ontologies.