Abstract: For development of the intelligent airport
energy management system, a comprehensive airport data
model is required to describe all static knowledge
relevant for the airport energy management, enabling the
integration and interoperability of different technical
systems. One way of providing such comprehensive
airport data model is based on the ontology modelling
paradigm. Having in mind that airports are rather
complex infrastructures with numerous, heterogeneous
devices coming from different vendors, and therefore
characterized with the large quantities of the static data,
it is important to choose a suitable approach for ontology
modelling. This paper presents one of the possible
approaches to the airport ontology modelling and
population which combines three different, but yet
complementary methods: LODRefine tool, SPIN mapping
and SPARQL Update queries. The flexibility of the
proposed approach was seen in possibility to instantiate
any airport infrastructure of the given complexity. The
input for population of the airport ontology model was
extracted from the various data sources such as data
point lists, technical sheets, audits, interviews,
questionnaire etc. Finally, as a test-bed platform for the
ontology population, two specific airport infrastructures
were chosen, Malpensa airport in Milan and Fiumicino
airport in Rome.