Review on Knowledge Graph and Its Application in Smart Forestry Monitoring and Decision-making

As a powerful tool for organizing and expressing knowledge, knowledge graphs can semantically integrate, describe, and reason about heterogeneous data from multiple sources. By connecting dispersed data resources into a unified knowledge network, knowledge graphs transform data silos into knowledge interconnections. Benefiting from the development of semantic technologies and artificial intelligence, knowledge graphs provide a solid foundation for intelligent information management and high-level decision-making. This article summarizes the current state of knowledge graph technology and standardization, systematically elaborates on the knowledge graph technical architecture encompassing knowledge acquisition, knowledge fusion, knowledge representation, knowledge storage, knowledge modeling, knowledge computation, and knowledge operations. It also introduces and summarizes several knowledge graph-related standards developed by organizations such as the W3C, ISO/IEC JTC 1/SC 42, IEEE C/SAB/KG_WG, and the National Information Technology Standardization Technical Committee. Knowledge graph-based forestry management systems can integrate multi-source data, including geospatial information, species classification, ecological environment, health status, climate change, and pest and disease control, to establish intelligent analysis models that couple multiple factors, assisting managers in real-time monitoring, dynamic assessment, and informed decision-making regarding the status of forestry resources. Focusing on the application of knowledge graphs in forestry management and decision-making, this paper discusses the current status and development of knowledge graphs in forestry management and decision-making. It also summarizes a series of challenges faced by knowledge graphs in forestry management and decision-making, such as data dispersion and relevance, during the application data preparation, construction and maintenance, and application system integration and deployment stages. It also provides prospects for future research and application. Knowledge graphs will further promote the transformation of forestry management towards data-driven, intelligent decision-making, providing stronger technical support.