The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
When Facts Expire: Benchmarking Temporal Validity in Knowledge Graphs
Open accessAbstract
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 This leads to outdated or incorrect inferences.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).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Temporal Knowledge Graph Completion: A Survey
Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/734 · Cited by 133
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Cai, L., Mao, X., Zhou, Y., Long, Z., Wu, C., & Lan, M. (2024) · arXiv · doi:10.48550/arxiv.2403.04782 · Cited by 68
Flagged first, then by citations
Research works printed but not linked: 26
- 10.1145/3460210.3493566 · DOI
- 10.1016/j.knosys.2025.113060 · DOI
- 10.18653/v1/D18-1225 · DOI · Paper page
- 10.18653/v1/D18-1516 · DOI · Paper page
- Julia Gastinger, Shenyang Huang, Mikhail Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Ioannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, and Guillaume Rabusseau. 2024. TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs. Vol. 37 (2024), 140199-140229. https://proceedings.neurips.cc/paper_files/paper/2024/hash/fda026cf2423a01fcbcf1e1e43ee9a50-Abstract-Datasets_and_Benchmarks_Track.html
- 10.1016/j.websem.2015.08.001 · DOI
- Duygu Sezen Islakoglu, Melisachew Wudage Chekol, and Yannis Velegrakis. 2024. Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs. In European Semantic Web Conference. Springer, 59-78.
- 10.18653/v1/2020.emnlp-main.305 · DOI
- 10.18653/v1/2020.emnlp-main.305 · DOI
- 10.18653/v1/2020.emnlp-main.541 · DOI
- 10.1016/j.datak.2024.102285 · DOI
- 10.1007/978-3-031-06981-9_15 · DOI
- 10.1007/978-3-030-00671-6_39 · DOI
- 10.1007/s10462-023-10465-9 · DOI · Paper page
- 10.1007/978-3-031-47240-4_25 · DOI
- 10.1038/s41598-024-74516-z · DOI
- 10.18653/v1/2020.acl-main.412 · DOI
- 10.1080/17460441.2025.2490253 · DOI
- 10.1109/ICDM.2017.105 · DOI
- 10.1007/978-3-031-94575-5_13 · DOI
- 10.1145/3626772.3657876 · DOI
- Go Sugimoto, Victor de Boer, and Jacco van Ossenbruggen. 2025. Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage. Semantic Web Journal (2025). Minor revision, submitted April 11, 2025.
- 10.1007/978-3-030-30793-6_36 · DOI
- 10.1145/2629489 · DOI
- Jiapu Wang Boyue Wang Meikang Qiu Shirui Pan Bo Xiong Heng Liu Linhao Luo Tengfei Liu Yongli Hu Baocai Yin and Wen Gao. 2023. A Survey on Temporal Knowledge Graph Completion: Taxonomy Progress and Prospects. arXiv:2308.02457 [cs] doi:10.48550/arXiv.2308.02457 · DOI
- 10.1145/3626772.3661370 · DOI
Metadata
- Authors
- Soulard, T., Saïs, F., & Raad, J.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- Proceedings of the 34th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2025-11-10
- Pages
- 6534-6538
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W4416017180
- Cited by
- 2
- References
- 2
- Citations by year
- 2026: 2
- FWCI
- 1.91
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion, cc-by)
- Funders
- Paris-Saclay University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- temporal knowledge graphs, benchmarking, Wikidata, large language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
