Correcting update anomalies in spatiotemporal knowledge graphs with uncertainties

Abstract

As a knowledge description framework certified by the W3C (World Wide Web Consortium), Resource Description Framework (RDF) has a strict yet flexible description syntax and is widely accepted as a carrier of knowledge graphs (KGs). Currently, a large amount of research work has been done on static knowledge graphs. However, dealing with dynamic knowledge graphs, especially spatiotemporal knowledge, remains an important research topic. Knowledge in the real world is not always deterministic. Consequently, some research on uncertain knowledge modeling and management has been proposed. This paper focuses on fuzzy spatiotemporal knowledge modeling within the context of the RDF model. We formally propose an extended RDF model to represent fuzzy spatiotemporal knowledge. This model can represent the continuous motion trajectory of fuzzy spatiotemporal entities and avoid repeated time intervals and location information in RDF. Specifically, we systematically identify the consistency constraints in the fuzzy spatiotemporal RDF model. Based on these constraints, we further propose a method for correcting timely inconsistencies when updating fuzzy RDF graph data. Experimental results demonstrate the effectiveness of the fuzzy spatiotemporal RDF model and the inconsistency correction method proposed in this paper.