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The quality assurance of the knowledge graph is a prerequisite for variousknowledge-driven applications. We propose KGClean, a novel cleaning frameworkpowered by knowledge graph embedding, to detect and repair the heterogeneousdirty data. In contrast to previous approaches that either focus on fillingmissing data or clean errors violated limited rules, KGClean enables (i)cleaning both missing data and other erroneous values, and (ii) miningpotential rules automatically, which expands the coverage of error detecting.KGClean first learns data representations by TransGAT, an effective knowledgegraph embedding model, which gathers the neighborhood information of each dataand incorporates the interactions among data for casting data to continuousvector spaces with rich semantics. KGClean integrates an active learning-basedclassification model, which identifies errors with a small seed of labels.KGClean utilizes an efficient PRO-repair strategy to repair errors using anovel concept of propagation power. Extensive experiments on four typicalknowledge graphs demonstrate the effectiveness of KGClean in practice.

KGClean: An Embedding Powered Knowledge Graph Cleaning Framework