# Semantics-Preserving RDB2 RDF Data Transformation Using Hierarchical Direct Mapping
## 5 Experimental Results
### Analysis  
Figure 19(a) presents the results for the number of triples transformed from the relational data. To perform a comparative analysis of cost efficiency of the mapping rules, this work employed OWL  
Figure 17: Examples of mapping integrity constraints (Rule 10,11).  
ontology-based augmented direct mapping [13], which provides implementation details of the mapping algorithm and demonstrates the improvement over other previous methods. The horizontal axis represents the relational data size of the input as mapping methods, and the vertical axis represents the number of semantic triples as output data. As viewed in Figure 17(a), our approach generates fewer triples compared with the previous method. Figure 17(b) shows the average number of triples that result from each transformation method. The horizontal axis represents each relational dataset, and the vertical axis represents the average number of triples generated from the transformation of a single relational element. Assuming that two output results are identical in terms of semantics, the method that generates a smaller-sized result is better with regard to both space and computation. On one hand, when the input data are transformed into RDF data, the mapping rule uses the hierarchical RDF data model. On the other hand, previous methods generated output data without a predefined RDF data model, and repetitive RDF data were generated using primitive semantic model languages when the input data had several referential relationships or constraints. Thus, the results show that the proposed approach generates more compact RDF data that express the same information with fewer resources.  
Figure 19 shows the failure rate of the mapping methods in each database. The horizontal axis represents each relational dataset, and the vertical axis represents the failure rate during the transformation of relational data into RDF data. The mapping failures of the previous approach result in the incorrect RDF data generation problem was discussed in Section 3. These failures occurred because the previous methods lacked support for handling the integrity constraints. Our approach followed Lemma 4 and guaranteed that the hierarchical mapping rules generated fewer false mapping results. By our method, the false results could occur when the input data are defined based on the practical SQL statements that are not included in standard SQL.  
Figure 19: RDB2 RDF failure rate of the mapping methods.  
Figure 18: Comparative results of the mapping methods.  
Figures 20 and 21 illustrate the soundness and completeness of mapping rules when the size of the relational input data varies from 20 to 100. This experiment duplicated the experiments of [PERSON] et al. [8], [PERSON] et al. [9], [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] et al. [15], and [PERSON] et al. [16]. The results show that the proposed method adheres to the definition of the semantics-preserving direct-mapping rule. The mapping rules generate only semantics in RDB data and generate most of the semantics of the RDF data.