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 logic rules. It evaluates the quality of any rule by asking a user to validate only a few 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.
Few-shot knowledge validation using rules
WWW 2021, 30th Web Conference, 19-23 April 2021, Ljubljana, Slovenia
© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in WWW 2021, 30th Web Conference, 19-23 April 2021, Ljubljana, Slovenia https://doi.org/10.1145/3442381.3450040
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