A Knowledge-Guided Spatio-Temporal Correlation Measure Considering Rules and Dependency Syntax for Knowledge Graph Adaptive Representation
文献类型：期刊论文
| 作者 | Qiu, Qinjun1,2,3,6; Li, Haiyan2,4; Hu, Xinxin2,4; Tian, Miao1; Ma, Kai2,4; Zhu, Yunqiang5; Sun, Kai5; Li, Weirong5; Wang, Shu5; Xie, Zhong1,3,6 |
| 刊名 | TRANSACTIONS IN GIS
 |
| 出版日期 | 2024-12-11 |
| 卷号 | N/A |
| 关键词 | dependent syntax geographic information retrieval geographic knowledge graph knowledge-guided approach spatio-temporal correlation |
| DOI | 10.1111/tgis.13288 |
| 产权排序 | 6 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Geographic knowledge graphs (KGs) mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic information retrieval and geographic spatio-temporal processes. By analyzing the spatio-temporal features and evolution of geographic elements, this paper measures the degree of correlation between spatio-temporal information and tuples and further accurately characterizes the semantic knowledge of tuples to support accurate computation and inference of KGs. This paper proposes a novel knowledge-guided quantitative measure framework for spatio-temporal correlation by considering rules and dependency syntax from natural language texts. Firstly, the natural language processing (NLP) stage preprocess the texts and extracts the candidate tuples by dependency syntactic analysis and rule matching. Secondly, we model the spatio-temporal correlation measures by considering semantic (entity types and tuple predicate) and syntactic features (dependency distance and dependency path). Finally, we establish a specific threshold value with the extracted candidates and performing multiple levels of categorization to form the final spatio-temporal correlation strength (strong, moderate, and weak). The experimental results with a large dataset indicate that the proposed method achieves an F-score of over 0.73, which is better than those of the existing methods. The proposed spatio-temporal correlation framework has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons. |
| WOS关键词 | EXTRACTION ; ONTOLOGY |
| WOS研究方向 | Geography |
| WOS记录号 | WOS:001374676100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210432]  |
| 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 | Ma, Kai; Sun, Kai |
| 作者单位 | 1.China Univ Geosci, Key Lab Geol Survey & Evaluat, Minist Educ, Wuhan, Peoples R China 2.China Three Gorges Univ, Hubei Key Lab Intelligent Vis Based Monitoring Hyd, Yichang, Peoples R China 3.China Univ Geosci, Sch Comp Sci, Wuhan, Peoples R China 4.China Three Gorges Univ, Coll Comp & Informat Technol, Yichang, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 6.China Univ Geosci, Key Lab Quantitat Resource Evaluat & Informat Engn, Minist Nat Resources, Wuhan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiu, Qinjun,Li, Haiyan,Hu, Xinxin,et al. A Knowledge-Guided Spatio-Temporal Correlation Measure Considering Rules and Dependency Syntax for Knowledge Graph Adaptive Representation[J]. TRANSACTIONS IN GIS,2024,N/A. |
| APA | Qiu, Qinjun.,Li, Haiyan.,Hu, Xinxin.,Tian, Miao.,Ma, Kai.,...&Xie, Zhong.(2024).A Knowledge-Guided Spatio-Temporal Correlation Measure Considering Rules and Dependency Syntax for Knowledge Graph Adaptive Representation.TRANSACTIONS IN GIS,N/A. |
| MLA | Qiu, Qinjun,et al."A Knowledge-Guided Spatio-Temporal Correlation Measure Considering Rules and Dependency Syntax for Knowledge Graph Adaptive Representation".TRANSACTIONS IN GIS 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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