# Ontology-Based Representation and Reasoning in Building Construction Cost Estimation in China
## 5 Experiment of Cost Estimation Ontology Reasoning  
### Execution of Interation Reasoning  
This research conducts experiments to demonstrate the proposed cost estimation ontology construction process and its application in an actual situation. Protege is adopted as the experiment platform. The cost estimation ontology is built on the basis of Measurement Specification of Building Construction and Decoration Engineering in GB 50500, and it is necessary to examine the implementation of the cost estimation ontology. In the experimental case, the beam of a building product is studied. The beam information from the GB 50500, construction document and construction company, is selected to build an ontology to verify the feasibility of the proposed cost estimation ontology. As shown in Figure 11, the new cost estimation knowledge of the beam can be entered using the ontology editor software Protege. When the material feature and geometry feature of the beam are set up in the cost estimation ontology, the building product represented by the cost item can be reasoned by the defined reasoning rule. For example, the cost item instance \"_ID_010503002_\" represents \"_Rectangular_Beam_\" by the property \"_hasBuildingProduct_\". Once the concept model ontology is triggered, Rule 2 is executed. The property \"_hasSectionShape_\" of a beam's instance \"_Rectangular_Beam_\" indicates the section shape of the beam. Then, the ontology model and SWRL are translated into the Jess knowledge base via OWL/SWRL transformers, and the reasoning is executed. Finally, through reasoning by the Jess inference engine and the Jess transformer, the result is obtained, as shown in Figure 12. The content in shaded parts is the related classes and instances in the reasoning process. The reasoning result shows \"_ID_010503002_\", which represents \"_Rectangular_Beam_of_Cast-in-Place_Concrete_\". _Rule 2: Beam(?x) \(\wedge\) Geometry_Feature(?y) \(\wedge\)Material_Feature(?z) \(\wedge\)hasMaterialFeature(?x, Cast-in-Place_Concrete) \(\wedge\)hasSectionShape(?x, rectangular)\(\rightarrow\)representsBuildingProduct (id_010503002,?x)_ \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Type Property in OWL** & **Jess Type** \\ \hline xsd: string & RUSTRING \\ xsd: boolean & The atoms “TRUE” and “FALSE” \\ xsd: decimal, xsd: double, xsd: int, xsd: long, etc. & RU.INTEGER, RU.FLOAT, RU LONG \\ ARRAY\_XX\_TYPE class, etc. & A Jess multifield \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The data type property transformation between OWL and Jess. In the cost estimation process, the building product can be a combination of multiple building materials, which is usually not known by professionals. The input building product and its material feature are matched to the list of established classes and instances to determine its corresponding building materials. The aforementioned process involves two ontology models: the concept model ontology and the work item ontology. Once the work item ontology and concept model ontology are triggered, Rule 3 is triggered to compare these two ontologies so the result can be determined (the reasoning result is shown in Figure 13). The property _\"hasMaterialFeature\"_ of beam instance _\"Rectangular_Beam\"_, which indicates a rectangular beam of concrete, has the material feature _\"Cast-in-Place Concrete\"_; cast in place concrete includes materials, i.e., _\"Cement\"_, _\"Sand\"_, _\"Stone\"_, _\"Water\"_; and then the reasoning rule is triggered. Next, the ontology model and SWRL are translated into the knowledge base. The Jess inference engine is run, and the result is returned to the ontology, as shown in Figure 13 (Reasoning result), which shows that the rectangular beam has the materials _\"Cement\"_, _\"Sand\"_, _\"Stone\"_, _\"Water\"_. The rectangular beam and the property are in the concept model ontology, and cement, sand, stone, and water are in the work item ontology, so the reasoning is realized by combining the concept model and work item ontologies.