Real property data (often referred to as real estate, realty, or immovable property data) represent a valuable asset that has the potential to enable innovative services when integrated with related contextual data (e.g., business data). Such services can range from providing evaluation of real estate to reporting on up-to-date information about state-owned properties. Real property data integration is a difficult task primarily due to the heterogeneity and complexity of the real property data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. The proDataMarket ontology is developed in the project as a key enabler for integration of real property data.
The proDataMarket ontology design and development process followed techniques and design choices supported by existing methodologies, mainly the one proposed by Noy . Requirements are extracted from a set of relevant business cases and competency questions  are defined for each business case, so as core concepts and relationships. A conceptual model is then developed based on the requirements mentioned above and international standards including ISO 19152:2012 and European Union’s INSPIRE data specifications. For example, the LADM conceptual model from ISO 19152:2012 is used as reference model to the proDataMarket cadastral domain conceptual model. Afterwards we implemented the conceptual model using RDFS/OWL linked data standard. RDFS is used to model concepts, properties and simple relationships such as rdfs:subClassOf. OWL is built upon RDFS and provides a richer language for web ontology modelling and it is used to model constraints and other advanced relationships, such as the cardinality constraint needed to express the relationship between properties and buildings.
The proDataMarket ontology can be accessed at http://vocabs.datagraft.net/proDataMarket/. The ontology has been divided into several sub-ontologies (see Table below), reflecting the cross-domain nature of the requirements. This modular approach also helped to handle the complexity of the model and made it easier to maintain. In the current version, there are 11 sub-ontologies with 43 native classes and 43 native properties.
|Domain/module||Namespace prefix||URL||Classes||Properties||Business cases|
|Cadaster||prodm-cad||http://vocabs.datagraft.net/proDataMarket/0.1/Cadastre#||6||16||SoE, RVAS, NNAS, SIM|
|State of Estate Report||prodm-soe||http://vocabs.datagraft.net/proDataMarket/0.1/SoE#||4||2||SoE, RVAS|
|Business Entity||–||Reuse the existing vocabularies, no new classes and properties||0||0||SoE, RVAS|
|Building Accessibility||–||Reuse the existing vocabularies, no new classes and properties||0||0||SoE|
|Land Parcel Identification System (LPIS)||prodm-lpis||http://vocabs.datagraft.net/proDataMarket/0.1/LPIS#||1||7||CAPAS|
|Landscape Elements (LiDAR data)||prodm-lid||http://vocabs.datagraft.net/proDataMarket/0.1/Lidar#||3||0||CAPAS|
More than 30 datasets have been published through the DataGraft platform   using the proDataMarket ontology as a central reference model. All seven business cases use the proDataMarket ontology in data publishing. More details on the proDataMarket vocabulary can be found in the paper under review: http://www.semantic-web-journal.net/content/prodatamarket-ontology-enabling-semantic-interoperability-real-property-data
-  Noy, Natalya F., and Deborah L. McGuinness. “Ontology development 101: A guide to creating your first ontology.” (2001).
-  Grüninger, Michael, and Mark S. Fox. “Methodology for the Design and Evaluation of Ontologies.” (1995).
-  Roman, D., et al. DataGraft: One-Stop-Shop for Open Data Management. 2017. Semantic Web, vol. Preprint, no. Preprint, pp. 1-19, 2017. DOI: 10.3233/SW-170263.
-  Roman, D., et al. DataGraft: Simplifying Open Data Publishing. ESWC (Satellite Events) 2016: 101-106.