Review, framework, and future perspectives of Geographic Knowledge Graph (GeoKG) quality assessment
文献类型：期刊论文
| 作者 | Wang, Shu6; Qiu, Peiyuan5; Zhu, Yunqiang6; Yang, Jie; Peng, Peng; Bai, Yan; Li, Gengze3,4; Dai, Xiaoliang; Qi, Yanmin2 |
| 刊名 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 出版日期 | 2024-09-21 |
| 卷号 | N/A |
| 关键词 | Geographic Knowledge Graph (GeoKG) Quality Assessment Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) assessment indicators metrics quality evaluation Geospatial Artificial Intelligence (GeoAI) |
| DOI | 10.1080/10095020.2024.2403785 |
| 产权排序 | 1 |
| 文献子类 | Review ; Early Access |
| 英文摘要 | 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. |
| WOS关键词 | LINKED DATA ; INFORMATION ; SYSTEMS |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:001317544200001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207983]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhu, Yunqiang |
| 作者单位 | 1.Univ Nottingham Ningbo, Sch Comp Sci, Ningbo, Peoples R China 2.Tsinghua Univ, Inst Transportat Engn, Beijing, Peoples R China 3.Univ Leeds, Inst Transport Study, Leeds, England 4.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Shu,Qiu, Peiyuan,Zhu, Yunqiang,et al. Review, framework, and future perspectives of Geographic Knowledge Graph (GeoKG) quality assessment[J]. GEO-SPATIAL INFORMATION SCIENCE,2024,N/A. |
| APA | Wang, Shu.,Qiu, Peiyuan.,Zhu, Yunqiang.,Yang, Jie.,Peng, Peng.,...&Qi, Yanmin.(2024).Review, framework, and future perspectives of Geographic Knowledge Graph (GeoKG) quality assessment.GEO-SPATIAL INFORMATION SCIENCE,N/A. |
| MLA | Wang, Shu,et al."Review, framework, and future perspectives of Geographic Knowledge Graph (GeoKG) quality assessment".GEO-SPATIAL INFORMATION SCIENCE N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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