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From Statistical Knowledge Bases to Degrees of Belief: An Overview

Summary: Proposes the random-worlds method to derive degrees of belief from rich knowledge bases (individuals, statistics, correlations, rules) by treating all possible worlds as equally likely (principle of indifference). Unifies qualitative defaults and quantitative probabilities, yielding direct-inference properties (specificity, inheritance, independence) and handling reasoning tasks beyond many non-deductive systems, with implications for database applications. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1382
Venue
PODS
Year
2006
Pagerank
4.6696171e-05
Overall Rank
7,717 | 46.32%
DOI
-

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Rank Citing Paper Year Venue Pagerank
627 Management of Probabilistic Data: Foundations and Challenges 2007 PODS 0.00018959005
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
74 Efficient Query Evaluation on Probabilistic Databases 2004 VLDB 0.00057857292
450 The Theory Of Probabilistic Databases 1987 VLDB 0.00022822073
2,268 OLAP Over Uncertain and Imprecise Data 2005 VLDB 9.1497575e-05
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