Abstract

Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools. However, many applications highly depend on ad-hoc models and expensive human labeling to understand scientific contents, hindering deployments into real products. To build a unified backbone language model for different knowledge-intensive academic applications, we pre-train an academic language model OAG-BERT that integrates both the heterogeneous entity knowledge and scientific corpora in the Open Academic Graph (OAG)—the largest public academic graph to date. In OAG-BERT, we develop strategies for pre-training text and entity data along with zero-shot inference techniques. OAG-BERT achieves outperformance over baselines on nine academic tasks including two demo applications, demonstrating its potential to serve as one foundation model for academic knowledge services. Its zero-shot capability furthers the path to mitigate the need of expensive annotations. OAG-BERT has been deployed for real-world applications, such as the reviewer recommendation function for National Nature Science Foundation of China (NSFC)—one of the larges