# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 6 Conclusions and Future Work  
This paper proposes a business-model-driven task-planning knowledge mapping construction method, which is different from the previous domain knowledge mapping construction research that relies on the experience of domain experts with one-sidedness and has a lack of a unified knowledge construction framework, etc. This method proposes a business-model-driven task-planning knowledge mapping construction framework by systematically integrating the business knowledge in the business model of the task planning of a certain domain and by deep mining. The method proposes a business-model-driven task-planning knowledge mapping construction framework and a multidimensional task-planning ontology network expression method and a domain knowledge categorization algorithm based on Ullman subgraph matching to match the framework, thus providing a task-planning knowledge mapping construction method that can support planning and simulation modelling in this domain, and its domain generality is strong enough to be promoted and applied. Specifically, the method includes the steps of business knowledge acquisition, domain knowledge categorization, knowledge integrity testing, and graph storage. First, the task-planning ontology network is constructed by using the multidimensional task-planning ontology network expression method, and then the business data are transformed into business data mapping through manual calibration and structured processing to complete the acquisition of business knowledge. Subsequently, a domain knowledge categorization algorithm based on Ullman subgraph matching is used to complete the mapping of the ontology network to the business data mapping to complete the categorization of domain knowledge. Ultimately, the generated knowledge graph of the task-planning domain is stored in the Neo4j graph database. To ensure the completeness of the knowledge graph, an adaptive adjustment method is also developed that can detect and adjust the completeness of the knowledge graph. The method is unsupervised, does not rely on deep linguistic knowledge, and can be widely applied to the construction of knowledge graphs for task planning in different domains. Taking space-station mission planning as a research area, this paper validates the effectiveness of the proposed method by constructing a knowledge map of space-station mission planning. The experimental results show that the adoption of the multidimensional mission-planning ontology network expression method can ensure that the constructed space-station mission-planning ontology network has complete semantic expression capability and accurately describes the domain knowledge and its interrelationships. Figure 15: Performance of knowledge classification algorithms with different subgraph sizes. Meanwhile, the knowledge categorization algorithm based on Ullman subgraph matching ensures that the generated data network is highly accurate in terms of domain knowledge. However, we also recognize several limitations. First, our approach still requires manual intervention in the knowledge graph construction process, including the construction of ontology networks and the generation of business data graphs, which lacks full automation. Second, complex graph-matching computations are involved in the domain knowledge categorization module, and the performance of the method degrades as the number of graph nodes increases. Therefore, in our future work, we need to explore more automated and intelligent methods to improve the efficiency and scope of our approach. In addition, future research can focus on improving graph-matching algorithms, optimizing graph storage and computation, and adopting more efficient distributed computing methods or graph parallel computing techniques to meet the challenges of large-scale datasets. In conclusion, the research in this paper provides an innovative solution for task-planning knowledge graph construction and lays the foundation for future research directions and technological advances.