## 4 Semantic Web
### Semantic Data  
Semantic data are data in XML format with the structure of subject-predicate-object. They are mainly defined by defined structure words, such as RDF and OWL. RDF provides a way to describe information about the data. A widely used relational database uses a table structure to describe information about the data. However, linked of data (LOD) pursued by the Semantic Web is based on a standard called RDF to describe information about the data. This is suitable for describing Unified Resource Identification (URI) in a graph form. Table 6 shows an example of RDF [16].  
As shown in Figure 8, there is a rolling process in a virtual factory. The subject-predicate-object can be expressed at each step by giving a URI that can identify the worker.  
OWL is a language designed to implement applications that can directly process information content. It does not just display information. As OWL includes a rich vocabulary and formal semantics, it is possible to create machine-interpretable web contents [17].  
SPARQL is W3C's standard query language for data constructed in LOD format. It has a form similar to SQL for retrieving the desired data from a relational database [18].  
As shown in Figure 9, the desired information is retrieved using SPARQL, a language similar to SQL but with a different syntax.  
Figure 8: RDF sample.  
Figure 7: Example of a facility-oriented semantic expansion.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Technical Type** & **Reference** & **Reflect** & **Description** \\ \hline \multirow{6}{*}{Data Acquisition for Monitoring} & DDS [8] & X & Introduce the techniques used in the past \\ \cline{2-3}  & OPC [9] & X & Introduce the techniques used in the past \\ \cline{2-3}  & OPC-UA [10] & X & \\ \cline{2-3}  & A cyber-physical machine tools platform using & O & \\ \cline{2-3}  & OPC UA and MTConnect [11] & O & Describes the features of \\ \cline{2-3}  & MTConnect [12] & O & MTConnect \\ \cline{2-3}  & MTConnect standard [13] & O & \\ \hline \multirow{6}{*}{Autonomous Control} & Autonomic computing: principles, design, and  implementation [14] & X & \\ \cline{2-3}  & Self-healing components in robust software & & Introduces the traditional \\ \cline{2-3}  & architecture for concurrent and distributed & X & autonomous control \\ \cline{2-3}  & systems [15] & & technology methodology and \\ \cline{2-3}  & Personal autonomic computing reflex reactions & X & derives the need for a goal \\ \cline{2-3}  & and self-healing [16] & X & model and error model \\ \cline{2-3}  & A model-based diagnosis with fault event & & \\ \cline{2-3}  & models [17] & X & \\ \cline{2-3}  & An approach to applying goal model and fault & & We introduce the process of \\ \cline{2-3}  & tree for autonomic control [18] & O & creating an overall error \\ \cline{2-3}  & & tree for autonomic control [18] & O & model, such as error analysis \\ \cline{2-3}  & & tree for autonomic control [18] & and error state definition, with \\ \cline{2-3}  & & tree for autonomic control [19] & O & Introduces the traditional \\ \cline{2-3}  & Incorporating autonomous vehicles in the  traditional four-step model [19] & O & four-step autonomous control \\ \cline{2-3}  & Ontology building for cyber–physical systems: & & \\ \cline{2-3}  & application in the manufacturing domain [20] & O & Introduction to semantic web \\ \cline{2-3}  & A. Ontology building for cyber–physical systems: & & technology \\ \cline{2-3}  & application in the manufacturing domain [21] & X & \\ \cline{2-3} Semantic Web & Design quality metrics to determine the & & Methodologies proposed to \\ \cline{2-3}  & suitability and cost-effect of self- capabilities for & X & create a semantic-based smart \\ \cline{2-3}  & autonomic computing systems [22] & & factory \\ \hline \end{tabular}
\end{table}
Table 6: Reference description.  
Figure 9: SPARQL sample.