  In this paper, we propose a fully automated system to extend knowledge graphs
using external information from web-scale corpora. The designed system
leverages a deep learning based technology for relation extraction that can be
trained by a distantly supervised approach. In addition to that, the system
uses a deep learning approach for knowledge base completion by utilizing the
global structure information of the induced KG to further refine the confidence
of the newly discovered relations. The designed system does not require any
effort for adaptation to new languages and domains as it does not use any
hand-labeled data, NLP analytics and inference rules. Our experiments,
performed on a popular academic benchmark demonstrate that the suggested system
boosts the performance of relation extraction by a wide margin, reporting error
reductions of 50%, resulting in relative improvement of up to 100%. Also, a
web-scale experiment conducted to extend DBPedia with knowledge from Common
Crawl shows that our system is not only scalable but also does not require any
adaptation cost, while yielding substantial accuracy gain.
