用知识图谱追踪科学概念演变，预测论文未来影响力。
Optimizing Research Portfolio For Semantic Impact
- 构建32.4万篇生物医学论文的知识图谱，分析概念演化路径。
- 提前三年预测论文语义影响力，准确率高达R²=0.69。
- 可优化科研项目组合，降低选题风险，辅助资助决策。
引文指标虽广泛用于评估学术影响力，但存在机构声望与期刊可见性等社会偏见。本文提出rXiv语义影响力（XSI）框架，通过分析科学知识图谱（KG）中研究概念的演化来预测研究影响。基于2003–2025年间32.4万篇生物医学文献构建全面的科学知识图谱，我们证明XSI能以极高的准确性（R²=0.69）提前三年预测论文的未来语义影响力（SI）。利用该预测能力，我们开发出一种研究组合优化框架，系统性优于随机分配。提出将语义影响力（SI）作为引文指标的补充，并将XSI作为资助与出版决策工具，提升研究影响力同时降低风险。
原文摘要 · Abstract (English)
Citation metrics are widely used to assess academic impact but suffer from social biases, including institutional prestige and journal visibility. Here we introduce rXiv Semantic Impact (XSI), a novel framework that predicts research impact by analyzing how scientific semantic graphs evolve in underlying fabric of science. Rather than counting citations, XSI tracks the evolution of research concepts in the academic knowledge graph (KG). Starting with a construction of a comprehensive KG from 324K biomedical publications (2003-2025), we demonstrate that XSI can predict a paper's future semantic impact (SI) with remarkable accuracy ($R^2$ = 0.69) three years in advance. We leverage these predictions to develop an optimization framework for research portfolio selection that systematically outperforms random allocation. We propose SI as a complementary metric to citations and present XSI as a tool to guide funding and publishing decisions, enhancing research impact while mitigating risk.
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