Inconsistency Detection for Spatiotemporal Knowledge Graph with Entity Semantics and Spatiotemporal Features.

Autor: XIAO-WEN ZHANG, JING SHAN, WEN-YI TANG, LI YAN, ZONGMIN MA
Předmět:
Zdroj: Journal of Information Science & Engineering; Nov2023, Vol. 39 Issue 6, p1421-1436, 16p
Abstrakt: Knowledge graph (KG) can model and manage the massive metadata, have received a lot of attention in recent years. Information in the real world is not always static, aim to model and manage the dynamic information (e.g., time interval, location), some research works for spatiotemporal KG have been proposed. Due to the spatiotemporal knowledge is constantly changing, data operations will be more frequent in the process of spatiotemporal KG construction and management, therefore, inconsistency may exist in spatiotemporal KG. The current work on handling the inconsistencies in spatiotemporal KG mainly focuses on providing consistency constraints and fixing rules when operating the spatiotemporal KG, and no work on actively detects the existing inconsistency in spatiotemporal KG. In this paper, we discuss and summarize the inconsistency in spatiotemporal KG firstly. Then we design algorithms to extract inconsistency semantic feature in spatiotemporal KG, and finally we propose a spatiotemporal KG inconsistency detection model. The experimental results show that our method is scientific and effective. [ABSTRACT FROM AUTHOR]
Databáze: Supplemental Index