## 4 Human-Centered Knowledge Graph Representation for a Wire Harness Assembly Process
### Discussion on KG-Based Analytics of the Use Case  
Initially, Figure 9 shows the graphical representation of the complete knowledge graph (KG) related to the wire harness assembly case study. This visual depiction serves as a means to validate the manufacturing process. The complete network is illustrated on the left side, encompassing all properties and entities within the KG, while a more detailed view is presented on the right side. The orange node corresponds to the equipment \(E5\) and includes various associated data properties like locationID (18-4), equipmentCondition (86), equipmentID (\(E5\)), equipmentName (screwdriver C), and equipmentType (screwdriver). All the SPARQL queries that have been developed are accessible on our website at [[https://github.com/abonyilab/HCKG](https://github.com/abonyilab/HCKG)]([https://github.com/abonyilab/HCKG](https://github.com/abonyilab/HCKG)) (accessed on 14 March 2024). The initial instance of a SPARQL query-based data mapping, as referenced in [66], is illustrated in Figure 10. The left section of Figure 10 shows a graphical representation of the query, where four distinct rules are outlined to achieve the intended result. This particular example aims to identify RobotAssets categorized as IndustrialRobot s and seeks to present three associated data elements: Location, EnergySupply, and ManufacturingFacility. On the right side of Figure 10, a graphical representation of the result of the query is provided. Robot \begin{table}
\begin{tabular}{c c} \hline \hline
**Network Metrics** & **Analytical Features of KGs** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Centrality \\ computation \\ \end{tabular} } & Which are the critical objects in the network? \\ \cline{2-3}  & Detect the most significant influencing factors in the operator’s environment. \\ \hline \multirow{2}{*}{\begin{tabular}{c} Similarities \\ between nodes \\ and edges \\ \end{tabular} } & How similar are two objects based on their properties and how are they connected to other objects? \\ \cline{2-3}  & Solve allocation problems concerning operators and resources. \\ \hline \multirow{2}{*}{\begin{tabular}{c} Flows and paths \\ \end{tabular} } & What is the shortest, cheapest, or quickest way to perform a process step? \\ \cline{2-3}  & Optimise the shop floor layout to best match operator needs. \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Cycles \\ \end{tabular} } & Are there any cycles in the graph? If so, where are they? \\ \cline{2-3}  & Analyze tasks allocated to humans and machines in a collaborative work environment \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Network \\ communities \\ \end{tabular} } & What communities can be found in the production network? \\ \cline{2-3}  & Facilitate the design of human–machine collaboration or cell formation. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: KG metrics and analytical features. assets classified as IndustrialRobot s are depicted as orange nodes, each necessitating an EnergySupply referred to as g2 (electricity). Each IndustrialRobot node is linked to the corresponding workstation node (ManufacturingFacility ), which represents various assembly stations, illustrated in purple in this scenario. Lastly, the location data attributes of the robots are denoted by blue nodes (indicating the robots require two zones on the shop floor). This type of visual examination can aid in exploring dependencies concerning specific assets. The resulting graph in Figure 11 illustrates the connections between Actors (operators or robots) and the Capability entities they are linked to. This illustration can be utilized for a visual examination of production capability. In Figure 11, it is evident that the majority of actors possess the capability C8 (AGV loading/unloading). Moreover, robots are limited to a maximum of two capabilities, whereas operators can concurrently possess up to four capabilities. Figure 10: Visualization of the RobotAsset query (on the left-hand side) and the graph visualization of the result (on the right-hand side).