Mapping the Evolution of Research Contributions using KnoVo

TL;DR

KnoVo is an innovative framework that quantifies research novelty by analyzing a paper's evolution and comparison within multilayered citation networks using LLMs, enabling detailed insights into originality and knowledge progression.

Contribution

This work introduces KnoVo, a novel LLM-based framework for quantifying research novelty relative to prior and subsequent work, surpassing traditional impact-focused citation analysis.

Findings

KnoVo effectively quantifies research novelty across multiple scientific fields.

The framework visualizes knowledge evolution through dynamic graphs and radar charts.

Performance evaluation of open-source LLMs within KnoVo demonstrates its practical utility.

Abstract

This paper presents KnoVo (Knowledge Evolution), an intelligent framework designed for quantifying and analyzing the evolution of research novelty in the scientific literature. Moving beyond traditional citation analysis, which primarily measures impact, KnoVo determines a paper's novelty relative to both prior and subsequent work within its multilayered citation network. Given a target paper's abstract, KnoVo utilizes Large Language Models (LLMs) to dynamically extract dimensions of comparison (e.g., methodology, application, dataset). The target paper is then compared to related publications along these same extracted dimensions. This comparative analysis, inspired by tournament selection, yields quantitative novelty scores reflecting the relative improvement, equivalence, or inferiority of the target paper in specific aspects. By aggregating these scores and visualizing their progression, for instance, through dynamic evolution graphs and comparative radar charts, KnoVo facilitates researchers not only to assess originality and identify similar work, but also to track knowledge evolution along specific research dimensions, uncover research gaps, and explore cross-disciplinary connections. We demonstrate these capabilities through a detailed analysis of 20 diverse papers from multiple scientific fields and report on the performance of various open-source LLMs within the KnoVo framework.