Learning to Update Knowledge Graphs by Reading News

Jizhi Tang, Yansong Feng, Dongyan Zhao
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
News streams contain rich up-to-date information which can be used to update knowledge graphs (KGs). Most current text-based KG updating methods rely on elaborately designed information extraction systems and carefully crafted rules, which are often domain-specific and hard to maintain or generalize. However, such methods may not pay enough attention to the implicit information that lies underneath texts, thus often suffer from coverage issues. In this paper, we propose a novel graph based
more » ... l network method, GUpdater, to tackle these problems 1 . GUpdater is built upon graph neural networks (GNNs) with a text-based attention mechanism to guide the updating message passing through the KG structures. Experiments on a real-world KG updating dataset show that our model can effectively broadcast the news information to the KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets.
doi:10.18653/v1/d19-1265 dblp:conf/emnlp/TangFZ19 fatcat:6pkvp5sa7jbq5bgh6wi4zmlpri