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A Temporal-Probabilistic Database Model for Information Extraction

Summary: Temporal-probabilistic DB model for cleaning uncertain temporal facts from information extraction, combining temporal deduction, constraints, and possible-worlds inference. Scalable engine handles millions of facts and hundreds of thousands of rules; robust against ILP/MLN baselines with competitive runtimes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10856
Venue
VLDB
Year
2013
Pagerank
6.6335067e-05
Overall Rank
4,556 | 68.75%
DOI
10.14778/2556549.2556564

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{dylla_vldb13,
        title = {{A Temporal-Probabilistic Database Model for Information Extraction}},
        author = {Dylla, Maximilian and Miliaraki, Iris and Theobald, Martin},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {14},
        doi = {10.14778/2556549.2556564},
        url = {https://doi.org/10.14778/2556549.2556564},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
4,431 Database Principles in Information Extraction 2014 PODS 6.7088601e-05
4,499 Cleaning Inconsistencies in Information Extraction via Prioritized Repairs 2014 PODS 6.6608401e-05
11,385 Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue 2023 SIGMOD 5.093636e-05
12,014 TeCoRe: Temporal Conflict Resolution in Knowledge Graphs 2017 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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