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