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Knowledge Graphs (KGs) extracted from text sources are often noisy and leadto poor performance in downstream application tasks such as KG-based questionanswering.While much of the recent activity is focused on addressing thesparsity of KGs by using embeddings for inferring new facts, the issue ofcleaning up of noise in KGs through KG refinement task is not as activelystudied. Most successful techniques for KG refinement make use of inferencerules and reasoning over ontologies. Barring a few exceptions, embeddings donot make use of ontological information, and their performance in KG refinementtask is not well understood. In this paper, we present a KG refinementframework called IterefinE which iteratively combines the two techniques - onewhich uses ontological information and inferences rules, PSL-KGI, and the KGembeddings such as ComplEx and ConvE which do not. As a result, IterefinE isable to exploit not only the ontological information to improve the quality ofpredictions, but also the power of KG embeddings which (implicitly) performlonger chains of reasoning. The IterefinE framework, operates in a co-trainingmode and results in explicit type-supervised embedding of the refined KG fromPSL-KGI which we call as TypeE-X. Our experiments over a range of KG benchmarksshow that the embeddings that we produce are able to reject noisy facts from KGand at the same time infer higher quality new facts resulting in up to 9%improvement of overall weighted F1 score

IterefinE: Iterative KG Refinement Embeddings using Symbolic Knowledge