  KGCleaner is a framework to identify and correct errors in data produced and
delivered by an information extraction system. These tasks have been
understudied and KGCleaner is the first to address both. We introduce a
multi-task model that jointly learns to predict if an extracted relation is
credible and repair it if not. We evaluate our approach and other models as
instance of our framework on two collections: a Wikidata corpus of nearly 700K
facts and 5M fact-relevant sentences and a collection of 30K facts from the
2015 TAC Knowledge Base Population task. For credibility classification,
parameter efficient simple shallow neural network can achieve an absolute
performance gain of 30 $F_1$ points on Wikidata and comparable performance on
TAC. For the repair task, significant performance (at more than twice) gain can
be obtained depending on the nature of the dataset and the models.
