# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 3 Business-Model-Driven Task-Planning Knowledge Graph Construction Framework  
A business model is a formal representation of the business processes, knowledge entities, rules, and constraints of an enterprise or an organization, which can systematically capture and portray the business logic, processes, and rules of a specific domain, allowing the domain knowledge implicit in business operations to be explicitly and structurally expressed [21]. Therefore, using their business models as a source of knowledge in a specific task-planning domain ensures that the knowledge graph is constructed with domain expertise and accuracy. In this section, a business-model-driven knowledge graph construction framework for task planning is proposed, which consists of four modules: business knowledge acquisition, domain knowledge categorization, knowledge integrity testing, and knowledge graph storage. The framework structure of the business-model-driven knowledge graph construction method for task planning and the control flow between different modules are shown in Figure 1.  
The business information acquisition module integrates business information from all parts of the mission-planning business model. First, the operation mechanism of the domain task-planning business model is analyzed to extract business conceptual knowledge and data knowledge, including tabular data, document keywords, pictures and diagrams, 3D models and attribute lists, and domain rules; then, based on the business conceptual knowledge, the developed multidimensional task-planning ontology network expression method is used to construct the task-planning ontology; finally, through manual calibration and structured processing, the business data knowledge is organized into a data atlas to provide structured data support for the domain knowledge categorization module. The domain knowledge categorization module is used to categorize the entity knowledge described in the business data mapping to the corresponding concept nodes of the task-planning ontology network. First, according to the description information of each concept node, the task-planning ontology network is divided into multiple subgraph patterns, and subgraph templates are generated for categorization; then, using the improved Ullman-subgraph-matching algorithm, business data subgraphs with the same structure as the subgraph templates are matched from the business data mapping and categorized into corresponding domain knowledge subgraphs to achieve ontology-to-data mapping; finally, the ontology-to-data mapping is achieved by the attribute similarity weighted averaging method to fuse domain knowledge subgraphs and eliminate redundant information. The knowledge integrity check module is used to ensure that the task-planning knowledge graph contains complete domain knowledge. First, a set of test entity collections is constructed for evaluating the completeness of the knowledge graph; then, the task-planning knowledge graph is checked for the inclusion of these entities and the coverage of the graph over the test entity collections is calculated. If all of them are included, it indicates that the mapping is complete enough to enter the knowledge graph storage module; otherwise, it returns to the business information acquisition module and continues to extract the structured business knowledge required for task planning to continuously improve the mapping. Through the closed-loop feedback established between the business knowledge acquisition module, the domain knowledge categorization module, and the knowledge integrity checking module, continuous iteration ultimately ensures the integrity of the constructed task-planning knowledge graph. The knowledge graph storage module is used to store the task-planning knowledge graph that has been checked for knowledge integrity. In this paper, we use graph database Neo4j for storage, which is a graph-model-based NoSQL database [22] that can store and query relational data efficiently.