# Knowledge-based citation reasoning for biomedical domain

## TL;DR

This paper introduces a framework that uses biomedical knowledge to explain why certain papers are cited, improving transparency in academic literature searches.

## Contribution

A novel encoder-decoder framework that generates structured explanations for citation motivations using curated biomedical knowledge.

## Key findings

- The model outperforms pre-trained language models in generating citation motivations with higher precision, recall, and F1 scores.
- Over 10,000 citation relations were annotated with bio-triplets for training and evaluation in cancer-focused experiments.
- The approach enhances interpretability of citation rankings in biomedical research.

## Abstract

Citation is central to scholarly communication, enabling researchers to navigate rapidly expanding literature and identify relevant prior work. Yet the ‘reasoning’ behind why a particular paper is cited is often implicit or opaque. Although academic search engines and literature tools rank candidate papers for a query, the motivations underlying these rankings are rarely transparent, making it difficult for scholars to interpret and act on retrieved results—especially in biomedical research where domain knowledge is essential.

We propose an encoder–decoder framework that leverages curated biomedical knowledge to generate ‘explanations of citation motivation’ in a structured bio-triplet format. We evaluate the approach against recent families of pre-trained language models for text generation, including BERT-style (and variants) and GPT-style (and variants) models. In cancer-focused experiments using PubMed Central, we annotate over 10 000 citation relations with bio-triplets grounded in curated knowledge from multiple biomedical databases. Trained on these annotations, our model outperforms strong sequence-generation baselines, improving precision, recall, and F1 for citation-motivation generation.

Code and data are available at Zenodo (archival DOI: 10.281/zenodo.14893445) and GitHub: https://github.com/zhongxiangboy/Knowledge-based-Citation-Reasoning-for-Biomedical-Domain.