Mapping the Evolution of Research Contributions using KnoVo
This addresses the need for researchers to assess originality and track knowledge evolution beyond traditional citation metrics, though it is incremental as it builds on existing citation analysis and LLM methods.
The paper tackles the problem of quantifying research novelty by introducing KnoVo, a framework that uses LLMs to compare papers along extracted dimensions within citation networks, resulting in quantitative novelty scores and visualizations for analyzing knowledge evolution, as demonstrated on 20 diverse papers.
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.