# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 5 Experimentation and Verification of the Proposed Method
### Construction Results
#### 5.2.2 Construction Results of the Space-Station Mission-Planning Knowledge Graph  
The complete ontology network for space-station mission planning is shown in Figure 9, which provides a semantic model for the space-station mission-planning knowledge graph. In this section, the domain knowledge categorization algorithm based on  
Figure 8: Construction of ontology class (**left**), object properties (**middle**), and data properties (**right**).  
Figure 9: Visual display of ontology structure.  
Ullman subgraph matching developed in Section 4.2 is utilized to realize the matching mapping between The operational DATability knowledge and the ontology network, thus completing the construction of the space-station mission-planning knowledge graph. Before carrying out the matching mapping work, it is necessary to transform the business-data-based knowledge into business data mapping. Taking the planning of the platform function initialization monthly event and the rendezvous and docking monthly event as an example, the business data used in the planning process of these two monthly events are extracted from the business model of the space-station mission planning, including the table, the document keywords, the picture and illustration information, and the 3D model and checklist, as well as the domain rules. After manual calibration and structuring, the ternary representation of the business data in the above five items is obtained and dumped as a business data mapping.  
The business data graph obtained from Figure 10 is only a structured storage of domain data, which lacks the class labels and semantic information in the domain knowledge graph, and thus can only store the data but cannot provide functions to support reasoning and semantic querying. Business data mapping can be used to view and organize the connectivity relationships of domain data but lacks the semantic level of domain knowledge mapping to enable intelligent reasoning and querying at a higher level.  
Taking the constructed space-station mission-planning ontology network and business data mapping as inputs (i.e., the results obtained from Figures 9 and 10 are taken as inputs), the domain knowledge categorization algorithm based on the matching of Ullman subgraphs is utilized to realize the matching mapping between business-data-study knowledge and ontology network, and the knowledge mapping of the space-station mission planning in Figure 11 is formed. The ontology network (including concepts, object attributes, data attributes, etc.) in Figure 9 is interconnected with the specific data instances of the business data mapping in Figure 10, which establishes a bridge between the specific data and the high-level domain knowledge. The knowledge mapping in Figure 11 not only retains the conceptual and relational structure of Figure 9 but also adds the business data in Figure 10, which is able to demonstrate the actual data application in mission planning. The knowledge map formed by matching mapping has visualized domain relationships and potential \"knowledge island\" correlations, which facilitates the discovery of implicit relationships between knowledge and supports the mining of correlation paths in task planning, providing a new way for knowledge management and application in complex tasks.  
Figure 10: Business data graph (taking the business information of two-month event tasks as an example).