Temporal Evolution and Quality Strategies in Knowledge Graphs
Yahia Atig, Nadri Khiati, Aissam Bendida · 2025
Knowledge Graphs are foundational in applications ranging from semantic web technologies to artificial intelligence and decision support. While considerable progress has been made in KG construction, enrichment, and quality assessment, the temporal dimension of their evolution remains a critical yet underexplored area. This chapter investigates the challenges and methodologies related to temporal KG evolution, with a focus on preserving data accuracy, consistency, and reliability as knowledge changes over time. It examines how temporal factors affect KG quality, trigger anomalies, and raise issues of repair versus reconstruction. Correction frameworks are reviewed, emphasizing their extension to handle time-dependent inconsistencies. The chapter also explores Temporal Knowledge Graphs, highlighting their role in capturing and reasoning over evolving knowledge. By synthesizing recent research and outlining key challenges and approaches, this chapter offers a comprehensive foundation for managing time-aware knowledge in large-scale, dynamic KGs.