Few-Shot Knowledge Validations using Rules
Michael Loster, Davide Mottin, Paolo Papotti, Jan Ehmueller, Benjamin Feldmann, Felix Naumann
TheWebConf (WWW) 2021

How can be sure of a confidence of a rule in a knowledge graph?
For more information about COLT and our free dataset, please consult this page
TL;DR
Validate both confidence and quality a rule has on the facts of a knowledge graph with:
- A flexible model that use the power of the crowd
- Only few interactions with a user that validates the knowledge
- A method that finds the true confidence of a rule
- The largest dataset of manually annotated rules ever created - Link
In this paper:
- We propose Colt, a framework to assess quality and confidence of a rule by using expert-validated facts
- We enable the conditional application of rules and compute their confidence
- We establish a connection between our problem, the weighted-coverage problem, and quality-preserving Gaussian processes
- We show how our interactive learning approach only 20 user interactions, halves the error in confidence obtained with rule learning systems
- We publish our dataset consisting of 26 rules with more than 23,000 annotated facts.
- We provide code and data to be used for further research
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