## 4 Human-Centered Knowledge Graph Representation for a Wire Harness Assembly Process
### Development of the Industry-Specific Human-Centered Knowledge Graph  
A section of the KG that has been utilized in the case study discussed in Section 4.1 is depicted in Figure 8, excluding the distinct data properties of the ontology classes. The structural illustration in Figure 8 is segmented into six sets of ontology classes since the KG comprises multiple sub-ontologies. Moreover, the object properties, representing relationships among classes, are indicated on the arrows. Figure 7: Gantt chart of collaboration scenarios. Prefixes indicating adapted namespaces from other industry-specific ontologies are included in the names of the ontology classes. The following list summarises these prefixes and the ontologies used:  
* smo--Smart Manufacturing Ontology [59]: An ontology to model 14.0 production lines and smart factories based on RAMI 4.0. It highlights the sequence of processes and machines required for a produced workpiece. * SOSA--Sensor, Observation, Sample, and Actuator ontology [60]: For modeling the interactions between the entities involved in terms of observation, actuation, and sampling. Together with SSN (Semantic Sensor Network), it can be used to describe sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, the feature's properties being observed or sampled, as well as actuators and the activities they trigger [61]. * var ontology y [35]: A core ontology for data exchange in a semantic-oriented framework to support adaptive, interactive, assistive, and collaborative assembly workplaces. * hckg--Human-Centric Knowledge Graph: The authors created a set of classes and properties to model the wire harness assembly-based case study semantically. The product ontology includes three categories: product, part, and component. The complexity of this domain, previously addressed by the authors in [62], is not further explored here. The Process ontology is composed of the subsequent categories: Activity, Result, and ManufacturingFacility, which encompasses additional subclasses like Storage, Buffer, AssemblyStation, CrimpingStation, and Capability. The primary category in the  
Figure 8: Partial structural diagram of the developed wire harness assembly-specific KG. Resource ontology is Resource, which includes various subclasses such as Tool, Machine, and Robot. The Robot category is further divided into MobileRobot and IndustrialRobot. The EnergySupply category is also part of the resource ontology. The Operator category, a central element of the human-centered HC, is highlighted in green at the center of the HC structure in Figure 8. The operator category, which semantically characterizes the processes and impacts related to personnel on the shop floor, is associated with six distinct object properties. The Monitoring ontology comprises three categories: Sensor, Observation, and HAR_Analysis, storing the semantic model of sensor devices, their measurements, observation, and human activity recognition. The Evaluating ontology contains two categories: KPIStore and LogDataStore, designed to handle data from the aforementioned three categories. Lastly, in the Operator Support Ontology, the DAS category defines the digital assistance system. Considering that the categories Operator and Activity are pivotal in the KG, Tables 3 and 4 provide details on the corresponding object properties.