# Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification

arXiv: https://arxiv.org/abs/2004.06153 · HTML: https://mlsanity.com/paper/2004.06153/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Automated Mining of Leaderboards for Empirical AI Research](https://mlsanity.com/paper/2109.13089/) (2021)
- [ORKG-Leaderboards: A Systematic Workflow for Mining Leaderboards as a Knowledge Graph](https://mlsanity.com/paper/2305.11068/) (2023)
- [On the Effectiveness of Large Language Models in Automating Categorization of Scientific Texts](https://mlsanity.com/paper/2502.15745/) (2025)
- [Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models](https://mlsanity.com/paper/2604.23430/) (2026)

## Abstract (arXiv)

With the rapid growth of research publications, there is a vast amount of scholarly knowledge that needs to be organized in digital libraries. To deal with this challenge, techniques relying on knowledge-graph structures are being advocated. Within such graph-based pipelines, inferring relation types between related scientific concepts is a crucial step. Recently, advanced techniques relying on language models pre-trained on the large corpus have been popularly explored for automatic relation classification. Despite remarkable contributions that have been made, many of these methods were evaluated under different scenarios, which limits their comparability. To this end, we present a thorough empirical evaluation on eight Bert-based classification models by focusing on two key factors: 1) Bert model variants, and 2) classification strategies. Experiments on three corpora show that domain-specific pre-training corpus benefits the Bert-based classification model to identify the type of scientific relations. Although the strategy of predicting a single relation each time achieves a higher classification accuracy than the strategy of identifying multiple relation types simultaneously in general, the latter strategy demonstrates a more consistent performance in the corpus with either a large or small size of annotations. Our study aims to offer recommendations to the stakeholders of digital libraries for selecting the appropriate technique to build knowledge-graph-based systems for enhanced scholarly information organization.