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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
10668
Venue
VLDB
Year
2013
Pagerank
6.1168322e-05
Overall Rank
4,521 | 68.55%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,398 Cleaning Inconsistencies in Information Extraction via Prioritized Repairs 2014 PODS 5.5295577e-05
9,423 Database Principles in Information Extraction 2014 PODS 4.3441378e-05
11,179 Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue 2023 SIGMOD 4.1945683e-05
11,811 TeCoRe: Temporal Conflict Resolution in Knowledge Graphs 2017 VLDB 4.1945683e-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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