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Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability

Summary: AnoT detects online anomalies in temporal knowledge graphs via a rule-graph summary, enabling interpretable inference. D-U-M pipeline supports offline summarization, online scoring, rule updates, and error estimation, yielding interpretable signals. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7056
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,202 | 23.15%
DOI
10.1145/3698823

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BibTeX Citation

@inproceedings{zhang_sigmod24,
        title = {{Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability}},
        author = {Zhang, Jiasheng and Ying, Rex and Shao, Jie},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3698823},
        url = {https://dl.acm.org/doi/10.1145/3698823},
        year = {2024}
}

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