Review, framework, and future perspectives of Geographic Knowledge Graph (GeoKG) quality assessment

High-quality Geographic Knowledge Graphs (GeoKGs) are highly anticipated for their potential to provide reliable semantic support in geographical knowledge reasoning, training Geographic Large Language Models (Geo-LLMs), enabling geographical recommendation, and facilitating various geospatial knowledge-driven tasks. However, there is a lack of a standardized quality assessment methodology and clearly defined evaluative indicators in the field of GeoKGs research. This research uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to conduct a systematic review of literature and standards in the field of GeoKG in an effort to fill the gap. First, using the lifecycle theory as a guide, we outline and propose five groups including twenty assessment criteria and their accompanying calculation techniques for evaluating GeoKG quality. Then, expanding on this foundation, we present a streamlined evaluation scheme for GeoKGs that relies on just seven key measures, discussing their applicability, utility, and weight scheme in greater detail. After applying the GeoKG quality framework, we stated three key tasks emerge as priorities: the creation of specialized assessment tools, the formation of worldwide standards, and the building of large-scale, high-quality GeoKGs. We believe this thorough and systematic GeoKG quality assessment technique will help construct high-quality GeoKGs and promote GeoKGs as an engine for geo-intelligence applications including Geospatial Artificial Intelligence (GeoAI) systems, Sustainable Development Goals (SDGs) analyzers, and Virtual Geographic Environments (VGEs) models.高质量地理知识图谱（Geographic Knowledge Graphs, GeoKGs）因其能够为地理知识推理、地理大语言模型（Geographic Large Language Models, Geo-LLMs）训练、地理推荐以及各类地理空间知识驱动任务提供可靠语义支撑，而广受期待。然而，当前地理知识图谱研究领域仍缺乏标准化的质量评估方法与明确定义的评价指标。本研究采用系统评价与元分析首选报告条目（Preferred Reporting Items for Systematic Reviews and Meta-Analyses, PRISMA）方法，对地理知识图谱领域的文献与标准开展系统综述，以填补这一空白。首先，本研究以生命周期理论为指导，梳理并提出了包含20项评估准则及其配套计算方法的5大类地理知识图谱质量评估框架。在此基础上，本研究进一步提出了仅依托7项核心指标的轻量化地理知识图谱评估方案，并对其适用性、实用价值与权重分配方案展开了详细论述。通过应用该地理知识图谱质量评估框架，本研究明确了三项核心优先任务：开发专用评估工具、制定全球统一标准以及构建大规模高质量地理知识图谱。我们认为，这套全面且系统的地理知识图谱质量评估方法将助力高质量地理知识图谱的构建，并推动地理知识图谱成为地理智能应用的核心引擎，此类应用涵盖地理空间人工智能（Geospatial Artificial Intelligence, GeoAI）系统、可持续发展目标（Sustainable Development Goals, SDGs）分析工具以及虚拟地理环境（Virtual Geographic Environments, VGEs）模型等。