When Facts Expire: Benchmarking Temporal Validity in Knowledge Graphs
Year: 2025
Type: conference-paper
Abstract: Knowledge Graphs (KGs) are essential in applications like semantic search, question answering, and decision support. They structure knowledge, validate facts, enable inference, and increasingly enhance Large Language Models (LLMs) by grounding outputs in structured, factual data. However, KGs have treated facts as static and timeless, ignoring the temporal nature of many truths. This leads to outdated or incorrect inferences. Temporal Knowledge Graphs (TKGs), like Wikidata and YAGO, address this by modeling the time-bound validity of facts. Multiple recent work has focused on predicting missing temporal facts, yet validating existing temporal information, to ensure the reliability and accuracy of TKGs, remains underexplored. In order to advance this area of research, we introduce the first benchmark designed to evaluate temporal fact validation methods. Derived from Wikidata, this benchmark supports systematic, quantitative, and qualitative comparisons, incorporating diverse assumptions about temporal data (e.g., timestamps, intervals) and KG structures (e.g., density, depth).
Authors: Thibaut Soulard, Fatiha Saïs, Joe Raad
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416017180