The last few years have witnessed some remarkable success of the stateof-
the art unsupervised knowledge extraction systems like NELL and REVERB.
These systems are gifted with typically web-scale coverage but are often plagued
with ambiguity due to lack of proper schema or unique identifiers for the instances.
This classifies them apart from extraction systems like DBPEDIA, YAGO or FREEBASE
which have precise information content but have smaller coverage. In this
work we bring together the former to enrich the later with high precision novel
facts and present a statistical approach to discover new knowledge. In particular,
we semantify NELL triples using DBPEDIA.
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