OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services

TL;DR

OAG-BERT is a unified academic language model that integrates entity knowledge and scientific texts, enabling zero-shot inference to reduce annotation costs and support various academic applications.

Contribution

The paper introduces OAG-BERT, a novel pre-trained model combining heterogeneous academic knowledge and texts, with strategies for zero-shot inference to enhance academic knowledge services.

Findings

OAG-BERT effectively supports reviewer recommendation and paper tagging.

Zero-shot inference reduces the need for expensive annotations.

The model is deployed in real-world academic applications.

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. In OAG-BERT, we develop strategies for pre-training text and entity data along with zero-shot inference techniques. 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 largest funding agencies in China -- and paper tagging in AMiner. All codes and pre-trained models are available via the CogDL toolkit.