# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 1 Introduction
In addition, the ontology construction of task-planning knowledge graphs often lacks a systematic and standardized approach, resulting in the inability to completely express domain knowledge. These problems limit the effectiveness of task-planning knowledge graphs, and there is an urgent need for a more efficient, reliable, and domain-specialized construction method. Currently, the construction of domain knowledge graphs faces three prominent problems [6; 7; 8]. First, there is a lack of reliable data sources, and the existing methods lack high-quality and systematic data support; second, ontology construction mainly relies on the experience of domain experts, but it is difficult to comprehensively cover the multi-level and multi-dimensional knowledge elements in the process of task planning due to the one-sidedness of experts' knowledge; and third, the filling of the data layer is based on the manual operation of the domain experts by virtue of their subjective experience, which is inefficient and with a limited scope of coverage, further restricting the data. This further limits the application effect of knowledge mapping. Aiming at these problems and combining the characteristics of multi-level and multi-dimensional knowledge in the task-planning domain, this paper proposes a business-model-driven task-planning knowledge graph construction method to realize the systematic integration and deep mining of task planning knowledge in a certain domain so as to construct a task-planning knowledge graph that can support planning modeling and simulation modeling. The main contributions of this work are as follows:1. A business-model-driven task-planning knowledge graph construction framework is proposed. The framework uses the business knowledge utilized in the operation of the business model for mission planning in a particular domain as the basic data source for knowledge graph construction. After the business knowledge acquisition, domain knowledge categorization, knowledge integrity testing, and knowledge graph storage, the task-planning knowledge graph with the ability to support planning modeling and simulation modeling is generated. After steps such as business data graph generation, knowledge-matching mapping, knowledge fusion, and knowledge integrity check, it generates a task-planning knowledge graph that has the ability to support planning modeling and simulation modeling. This framework can effectively solve the problem of the lack of high-quality knowledge sources in specific mission-planning domains and provides an efficient and reliable solution for building high-quality mission-planning knowledge graphs. 2. A multidimensional task-planning ontology network representation method is proposed. The method divides mission-planning knowledge into five categories according to the ontological perspective: tasks, resources, processes, goals, and results. Each major category is further subdivided into three levels according to the knowledge granularity, corresponding to the macro, meso, and micro levels, respectively. At the same time, based on the \"function-structure-behavior\" chain of thought in cognitive science, the method portrays the knowledge of each granularity level according to the three dimensions of function, structure, and behavior and constructs a multidimensional expression of the task-planning ontology network. This expression can comprehensively summarize all kinds of knowledge elements in the task-planning process and lay the foundation for the formal modeling of knowledge in specific task-planning domains. 3. A domain knowledge classification algorithm based on Ullmann subgraph matching is proposed.