# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 2 Related Work  
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 graph that integrates and mines 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. Knowledge graphs are divided into general knowledge graphs and domain knowledge graphs. At present, most of the research results of knowledge graphs are general knowledge graphs. In addition to the universal knowledge graph constructed by Google,famous universal knowledge graphs include the DBpedia language knowledge base jointly constructed by the University of Leipzig and Mannheim [9], YAGO comprehensive knowledge base proposed by German Marker Institute [10], Probase, a large-scale knowledge graph developed and maintained by Microsoft Research Asia [11], \"Zhishi.me\" [12], \"CN-DBpedia\" constructed by Fu dan University [13], and so on. These general knowledge graphs each contain hundreds of millions of entities and relations. For example, Google Knowledge Graph includes 500 million entities and 2.5 billion relations, and Probase contains 1.68 billion web pages and 2.7 million concepts. Generic knowledge graphs are large in scale, breadth, and automation, but the lack of domain details makes it difficult to meet the individual needs of specific tasks and cannot be directly applied to specific industries. For this reason, domain knowledge graphs need to be constructed to solve this problem. Domain knowledge graphs usually involve a specific industry object and have deeper research depth and higher accuracy. Amazon [14] has constructed an Internet movie database called IMDb, which contains basic information about movie actors, movies, and TV shows, as well as deeper content such as movie-related footage, holes in the film, and the film's audio track. China Academy of Traditional Chinese Medicine [15] formed a domain knowledge graph for pediatric disease prediction by combining pediatric disease prediction rules with an established weighted knowledge graph based on plain Bayes. Shanghai Jiao Tong University [16] proposed AceKG, a large-scale domain knowledge graph for academic domains based on consistent ontology, which describes 3.13 billion ternary academic facts, including necessary attributes of papers, authors, research fields, locations and institutes, and the relationships among them. Wolfram Research [17] has developed WolframAlpha, a knowledge base for the field of mathematics, which has powerful computational capabilities to help students perform better calculus tasks and contribute to mathematical calculations. The task-planning knowledge graph belongs to the category of domain knowledge graph, which is formed through the systematic integration and mining of domain-specific task-planning knowledge. To date, research on constructing task-planning knowledge graphs is still relatively limited. [PERSON] et al. [18] discussed the construction process of UAV mission-planning knowledge graph in detail and also proposed a knowledge inference method based on graph neural network, which utilizes the advantages of graph structure and path inference techniques to predict the missing entities or relationships in the UAV mission-planning knowledge graph, and finally verified the validity and applicability of the method through simulation. [PERSON] et al.