Knowledge graphs (KGs) form the basis of modern intelligent search systems – their network structure helps with the semantic reasoning and interpretation of complex tasks. A KG is a highly dynamic structure in which facts are continuously updated, added, and removed. A typical approach to ensure data quality in the presence of continuous changes is to apply logic rules. These rules are automatically mined from the data using frequency-based approaches. As a result, these approaches depend on the data quality of the KG and are susceptible to errors and incompleteness. To address these issues, we propose Colt, a few-shot rule-based knowledge validation framework that enables the interactive quality assessment of logical rules. It evaluates the quality of any rule by asking a user to validate only a small percentage of the facts entailed by such rule on the KG. We formalize the problem as learning a validation function over the rule’s outcomes and study the theoretical connections to the generalized maximum coverage problem. Our model obtains (i) an accurate estimate of the quality of a rule with fewer than 20 user interactions and (ii) 75% quality (F1) with 5% annotations in the task of validating facts entailed by any rule.