Oag

Linking entities from different sources is a fundamental task in building open knowledge graphs. Despite much research conducted in related fields, the challenges of linkinglarge-scale heterogeneous entity graphs are far from resolved. Employing two billion-scale academic entity graphs (Microsoft Academic Graph and AMiner) as sources for our study, we propose a unified framework --- LinKG --- to address the problem of building a large-scale linked entity graph. LinKG is coupled with three linking modules, each of which addresses one category of entities. To link word-sequence-based entities (e. g. , venues), we present a long short-term memory network-based method for capturing the dependencies. To link large-scale entities (e. g. , papers), we leverage locality-sensitive hashing and convolutional neural networks for scalable and precise linking. To link entities with ambiguity (e. g. , authors), we propose heterogeneous graph attention networks to model different types of entities. Our extensive experiments and systematical analysis demonstrate that LinKG can achieve linking accuracy with an F1-score of 0. 9510, significantly outperforming the state-of-the-art. LinKG has been deployed to Microsoft Academic Search and AMiner to integrate the two large graphs. We have published the linked results---the Open Academic Graph (OAG): //www. openacademic. ai/oag/, making it the largest publicly available heterogeneous academic graph to date.