A Knowledge‐Guided Spatio‐Temporal Correlation Measure Considering Rules and Dependency Syntax for Knowledge Graph Adaptive Representation

ABSTRACT 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.