Unified Structural–Metric Framework for Scientific Knowledge Navigation Integrating GDMP Structural Intelligence with Multi-Objective Conceptual Routing

Scientific knowledge forms a vast, evolving, directed graph of concepts, constraints, theories, data, and publications. Existing AI systems operate primarily in text space (LLMs) or embedding space (vector similarity), while classical graph algorithms operate in scalar metric space. None integrate structural topology, multi-component conceptual distance, and governance mechanisms into a unified computational framework.This paper presents a comprehensive synthesis of three previously independent paradigms:GDMP (Graph Dynamic Mapping Paradigm) — modeling knowledge in structure space with typed nodes, constraint mappings, spectral topology, and stability analysis.Multi-component conceptual edge weighting — transforming shortest path computation into a multi-objective optimization problem in ℝ⁶.Meta-cognitive governance layer — rule-based structural continuity and Human–AI role separation.We define a unified formal object:K = (G, W, M, R)whereG = structural knowledge graph,W = multi-component edge-weight tensor,M = routing modes (scalarization family),R = rule/meta-rule governance system.We demonstrate applicability to large-scale citation graphs (>10^8 nodes), novelty scoring, interdisciplinary bridge detection, structural entropy analysis, and Pareto-optimal conceptual routing.The work closes previously unresolved gaps between semantic retrieval, structural graph theory, and scientific navigation, establishing a second-order scientific intelligence infrastructure.