从静态文献计量学到动态知识图谱：一个由大语言模型驱动的框架，用于实现科学、技术与创新（STI）分析的现代化

中文摘要 AI 辅助

文献计量指标，如被引频次、h指数、共同作者网络，长期以来一直是科学、技术与创新（STI）分析的基础，但存在时间滞后、语义浅薄以及无法捕捉当代知识生态系统非线性动态的问题。动态知识图谱和大语言模型（LLMs）虽各被提议作为补救方法，但单独使用都不足够。本文提出一个混合的、以符号为先的框架，在明确的方法约束下整合这三种传统。该框架由五层组成，将LLMs严格定位为临时候选丰富内容的生成器。候选内容只有在通过结构、证据、比较和选择性专家验证后才被分析认可，且每个阶段都记录完整出处。分析层支持既定的文献计量指标和基于图的扩展分析。框架的核心理论贡献是将验证视为语义灵活性和认知规范之间的中介原则。还讨论了关于可重复性、偏差和可审计性的治理考量。

英文摘要

Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI) analytics, yet suffer from temporal lag, semantic shallowness, and an inability to capture the non-linear dynamics of contemporary knowledge ecosystems. Dynamic knowledge graphs and large language models (LLMs) have each been proposed as remedies, but neither is sufficient alone: existing scholarly knowledge graphs remain largely static, while LLM-driven pipelines are prone to hallucination, opacity, and corpus bias without structured grounding. This paper proposes a hybrid, symbolic-first framework integrating all three traditions under explicit methodological constraint. Organized across five layers - an open scholarly data backbone, a dynamic versioned knowledge graph, a constrained LLM-assisted semantic augmentation layer, a multi-layer validation pipeline, and an analytics layer - the framework positions LLMs strictly as generators of provisional candidate enrichments. Candidates become analytically admissible only after passing structural, evidentiary, comparative, and selective expert validation, with full provenance recorded at every stage. The analytics layer supports both established bibliometric indicators and extended graph-based analyses, including trend emergence detection, science-to-technology pathway mapping, and policy-oriented gap analysis. The framework's central theoretical contribution is treating validation as the mediating principle between semantic flexibility and epistemic discipline, enabling STI analytics that is semantically richer and temporally more responsive than static bibliometrics while remaining aligned with the evidentiary standards of science-of-science research. Governance considerations addressing reproducibility, bias, and auditability are also discussed.

发表机构

• Arab International University（阿拉伯国际大学）

机构由 AI 辅助整理，请以论文原文为准。