  Knowledge Graphs (KGs) extracted from text sources are often noisy and lead
to poor performance in downstream application tasks such as KG-based question
answering.While much of the recent activity is focused on addressing the
sparsity of KGs by using embeddings for inferring new facts, the issue of
cleaning up of noise in KGs through KG refinement task is not as actively
studied. Most successful techniques for KG refinement make use of inference
rules and reasoning over ontologies. Barring a few exceptions, embeddings do
not make use of ontological information, and their performance in KG refinement
task is not well understood. In this paper, we present a KG refinement
framework called IterefinE which iteratively combines the two techniques - one
which uses ontological information and inferences rules, PSL-KGI, and the KG
embeddings such as ComplEx and ConvE which do not. As a result, IterefinE is
able to exploit not only the ontological information to improve the quality of
predictions, but also the power of KG embeddings which (implicitly) perform
longer chains of reasoning. The IterefinE framework, operates in a co-training
mode and results in explicit type-supervised embedding of the refined KG from
PSL-KGI which we call as TypeE-X. Our experiments over a range of KG benchmarks
show that the embeddings that we produce are able to reject noisy facts from KG
and at the same time infer higher quality new facts resulting in up to 9%
improvement of overall weighted F1 score
