# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 5 Experimentation and Verification of the Proposed Method
### Construction Results  
#### 5.2.1 Construction Results of Space-Station Mission-Planning Ontology Network  
In this paper, the conceptual knowledge of the space-station mission-planning domain is modeled using the multidimensional mission-planning ontology network representation developed in Section 4.1 to obtain the space-station mission-planning ontology network, and the creation tool uses the ontology editing tool Protege [22]. The exact creation process is described below.  
First, we create five knowledge categories: task, resource, process, goal, and outcome, under the thing category; then, each major category of knowledge is subdivided into three levels according to macro, meso, and micro granularity, for example, under the mission category; the space-station mission-planning task is then divided into three subcategories: \"Month_event\", \"Flight_control_event\", and \"Command\"; the mission-planning task is then divided into two subcategories: \"Flight_control_event\" and \"Command\". For example, under the task class, the space-station planning task is divided into three subclasses: \"Month_event\", \"Flight_control_event\", and \"Command\".  
After that, for the subclass information in the task's broad category, the subclasses at each granularity level are portrayed according to the three dimensions of structure, behavior, and function, and the object attributes and data attributes describing the nodes of the subclass are obtained. Take the three subclasses under the task category as an example, define the object attributes \"monthTaskInclude\" and \"flyTaskInclude\" to describe the structural relationship between the three subclasses and define the object attributes \"monthTaskBefore\", \"monthTaskAfter\", and so on. Define 9 object attributes such as \"monthTaskBefore\" and \"monthTaskAfter\" to describe the behavioral relationship of the knowledge under the task category and define \"hasPriority\", \"hasLightDem\", \"hasDurationScope\", and the other 18 data attributes to describe the functional attributes of the three subclasses under the broad task category. The functional attributes of the three subclasses under the task category are described. Finally, define the role \"Domain\" and the attribute \"Range\" for the object attribute and the data attribute, respectively, with \"Domain\" indicating the class to which the attribute belongs and \"Range\" indicating the range of values for the attribute. The \"Range\" indicates the value range of the attribute. The classes, object attributes, and data attributes in the space-station mission-planning ontology network are shown in Figure 8.  
Figure 7: Space-station mission-planning business model.  
The space-station mission-planning ontology network constructed based on the multidimensional mission-planning ontology network expression method contains a total of 55 class nodes, 64 object attributes, and 125 data attributes. The five broad categories of knowledge defined by Figure 8 include tasks, resources, etc., object attributes, and data attributes. These defined properties provide the underlying framework for the construction of ontology networks. Based on this framework, the different knowledge elements and their interrelationships are expressed in more detail by further refining the object attributes and data attributes. The structure of the space-station mission-planning ontology network was demonstrated using Protege's visual presentation function, and the structure of the ontology is shown in Figure 9.