Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation
This provides a scalable, cost-effective solution for populating knowledge graphs without manual adaptation, benefiting applications in AI and data integration.
The paper tackles the problem of automatically extending knowledge graphs from web-scale corpora using distantly supervised relation extraction and validation, achieving error reductions of 50% and relative improvements up to 100% on benchmarks.
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.