Intelligent Knowledge Mining Framework: Bridging AI Analysis and Trustworthy Preservation
This addresses the problem of data silos impeding utilization and decision-making across data-intensive sectors, but it appears incremental as it builds on existing concepts without claiming specific breakthroughs.
The paper tackles the challenge of accessing and integrating valuable information from disparate digital data sources by introducing the Intelligent Knowledge Mining Framework (IKMF), a conceptual model that bridges AI-driven analysis with trustworthy preservation to transform static repositories into living ecosystems.
The unprecedented proliferation of digital data presents significant challenges in access, integration, and value creation across all data-intensive sectors. Valuable information is frequently encapsulated within disparate systems, unstructured documents, and heterogeneous formats, creating silos that impede efficient utilization and collaborative decision-making. This paper introduces the Intelligent Knowledge Mining Framework (IKMF), a comprehensive conceptual model designed to bridge the critical gap between dynamic AI-driven analysis and trustworthy long-term preservation. The framework proposes a dual-stream architecture: a horizontal Mining Process that systematically transforms raw data into semantically rich, machine-actionable knowledge, and a parallel Trustworthy Archiving Stream that ensures the integrity, provenance, and computational reproducibility of these assets. By defining a blueprint for this symbiotic relationship, the paper provides a foundational model for transforming static repositories into living ecosystems that facilitate the flow of actionable intelligence from producers to consumers. This paper outlines the motivation, problem statement, and key research questions guiding the research and development of the framework, presents the underlying scientific methodology, and details its conceptual design and modeling.