# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 1 Introduction  
This section discusses the current status as well as the characteristics of two different types of knowledge graphs, pointing out that task-planning knowledge graphs are a type of domain knowledge graphs that integrate and mine knowledge for a specific task-planning domain. Based on the status quo that most of the existing related research focuses on specific applications and the lack of standardized frameworks, this paper proposes a framework for constructing a knowledge graph for mission planning based on a business model, proposes a multi-dimensional mission-planning ontology network expression method and a domain class knowledge categorization algorithm, and verifies the effectiveness of the method in space-station mission planning. Despite the wide range of applications of knowledge graphs in several domains, the existing research is still insufficient to face the specific needs of the task-planning domain. Based on this, this paper will propose a new knowledge graph construction method in thenext section, aiming to effectively integrate task-planning knowledge through a business model driven to optimize the construction efficiency and completeness of a task-planning knowledge graph. Task planning is the process of breaking down a large and complex task into a series of orderly small steps, rationally arranging the order and priority of execution, allocating the necessary resources, and defining the task executor and execution equipment so as to accomplish the goal in an efficient and orderly manner [1]. Task planning is a key step in improving production efficiency, optimizing resource utilization, enhancing production quality, and promoting production automation in order to achieve high efficiency, high quality, stability, and sustainability in industrial production [2]. From this, we can see, with the continuous progress of modern production technology and production tools, the complexity and diversity of the production process continues to increase, and this has led to an increasing number of factors that need to be weighed in mission planning, resulting in ever-increasing difficulty in mission planning. Currently, most of the model modeling processes involved in the various steps of mission planning are performed by planners with subjective experience. Due to the limitations of the planner's personal experience, the modeling approach that relies on subjective experience cannot fully reflect the actual situation and is difficult to meet the complex and changing needs. With the advent of the information age, the management technology of knowledge is constantly updated, especially the emergence of knowledge-mapping technology, which provides an effective solution for knowledge integration in the field of mission planning. In 2012, Google officially proposed the concept of knowledge graphs as the most important method for managing massive amounts of knowledge [3]. In essence, a knowledge graph is a semantic network and knowledge base with a directed graph structure [4]. It describes entities (concepts) and their relationships in the physical world in the form of triplets. Knowledge graphs can support applications such as knowledge retrieval, knowledge quiz, knowledge recommendation, knowledge visualization, etc. [5]. In the field of mission planning, knowledge mapping can systematically integrate dispersed knowledge, break the traditional \"knowledge island\" problem, and improve the intelligence and automation of the mission-planning process. However, there is often a lack of knowledge resources that can be directly utilized in the construction of scenario-specific knowledge graphs for task planning. As a result, much of the construction relies on expert experience, but this manual construction is limited by the one-sided nature of expert knowledge, which makes it difficult to comprehensively cover all the knowledge elements of task planning.