# An Ontology-Based Representation of Valued System for HBIM
## 5 HBIM and Ontology Data Integration: Case Study
An ontology-based database explicitly represents ontologies, data schema, data and links among data and their schema and between the ontology [53]. Figure 7 shows a schematic view of part of the Magio Palace knowledge base. It points out how different instances of the BuildingElement are connected with the three different vaults used as a case study in this paper. We can also model the relationship in the vaulted system ontology. Figure 8 shows the representation of a BuildingElement; Manfredini Hall One. In particular, this vault is related to the historical phases, building level, and rooms. In particular, Manfredini Hall One belongs to the current historical phase and it is located in the room named as \"Hall\" at the first level (L1). In addition, we can infer that the Manfredini Hall One is a cloister vault, with in foglio single layer texture, has an arrangement with courses along the directrix, policentric geometry and is made of bricks. The Manfredini Hall One vault hasComponent and hasSurvey relations with component and survey elements. The vault also presents some specific properties (hasID and hasDescription). Figure 6: Ontology-based database of vaulted system creation process. The Manfredini Hall One vault is composed by a set of components. A partial view of the elements composing the vaults is presented in Figure 9. For example, the Manfredini Hall One vault is composed by a VaultElement with specific material and geometry and by Frenelli with specific material. In addition to the above-mentioned relations, a HBIM knowledge database must also include survey properties. Survey properties include element like survey location (intrados or extrados), typology of the survey (geometrical, material or crack) and the survey products. Figure 10 shows the relations of two different geometrical surveys with Manfredini Hall Two. Figure 8: View of the Manfredini Hall Two relations. Figure 7: A schematic view of part of the Magio Palace knowledge base. As mentioned before, the ontology-based database is exposed using a RDF endpoint for further query and retrieval. Thanks to the ontology-based structure and the ontology reasoner, new added elements (vaults) are at the content-level encoding information and allows assessing the logical consequences of adding new knowledge and it may also infer new knowledge and add it to the vault HBIM knowledge base. The main objectives for using an ontological-base knowledge and a shared ontology are to provide query mediation and facilitate information integration across different domains. The data knowledge is stored as RDF/OWL format, allowing to query it by using a SPARQL endpoint. For example, the knowledge base can be used to perform simple queries like finding all cloister vaults built with clay bricks (all SPARQL queries are presented in an abbreviated form to avoid clutter). SELECT?element  WHERE {?element rdf:type cloistervault. ?element hbimvault:hasMaterial?material. FILTER (str(?material) = 'clay-brick'). Figure 10: A schematic view of part of the Manfredini Hall Two vault with the survey elements. Figure 9: A schematic view of part of the Manfredini Hall Two relations with components. Query 1 may be easily extended to retrieve properties for the selected building elements. For example, if we want to retrieve the survey products for this building, we can perform the Query 2. SELECT?element?product  WHERE {?element rdf:type cloistervault. ?element hbimvault:hasMaterial?material. FILTER (str(?material) = 'clay-brick'). ?element hbimsurvey:hasSurveyProduct?product.  } The advantage of those SPARQL queries is that the different ontologies developed in the different domains can be easily integrated to provide a simple tool for a fast data search and retrieval. We have here to underline the role of the \"foundation\" ontology that works as a mediator. The fact that all building domain ontologies are derived from a shared ontology enables all building domain knowledge bases to understand queries composed of the shared ontology vocabulary.