🤖 AI Summary
This work addresses critical limitations of traditional science, technology, and innovation (STI) analysis—namely its reliance on static indicators that suffer from time lags, shallow semantic representation, and an inability to capture the nonlinear dynamics of knowledge ecosystems. To overcome these challenges, the study proposes a validation-centric, five-layer hybrid framework that constructs a dynamic, versioned knowledge graph from open scholarly data and integrates constrained large language models (LLMs) for structured semantic enrichment. Reliability is ensured through a multi-tiered validation pipeline combining structural, evidential, comparative, and expert-based verification, augmented by a traceability mechanism. This approach significantly enhances the semantic depth, timeliness, and credibility of STI analysis while upholding scientific evidentiary standards, thereby enabling robust detection of emerging trends, mapping of technology transfer pathways, and policy-relevant gap analysis.
📝 Abstract
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.