Intelligent Knowledge Mining Framework: Bridging AI Analysis and Trustworthy Preservation

TL;DR

The paper addresses data fragmentation, reproducibility, and digital decay in data-intensive science by proposing the Intelligent Knowledge Mining Framework (IKMF). IKMF combines a horizontal Mining Process that converts data into machine-actionable knowledge with a parallel Trustworthy Archiving Stream that preserves provenance and reproducibility. It advocates a neurosymbolic AI architecture grounded in formal ontologies and rules to ensure verifiability while leveraging LLMs for scalable knowledge extraction. A structured program of R&D projects and a minimum viable prototype plan are outlined to develop and evaluate the framework. If realized, IKMF could transform static repositories into living ecosystems that enable reliable discovery and durable knowledge preservation for the scientific community.

Abstract

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.

Figures (15)

• Figure 1: The Nunamaker Research Framework for Information Systems nunamaker1991systems, illustrating the four interdependent strategies of Observation, Theory Building, Systems Development, and Experimentation.
• Figure 2: Decomposition of the Research Program into Targeted R&D Projects. The overall Research Questions (RQs) are addressed through a portfolio of individual projects, each applying the full Nunamaker research cycle, with their cumulative outcomes ($\Sigma$) forming the basis for the integrated solution.
• Figure 3: A Conceptual Schema for Planning and Synthesizing R&D Project Contributions. This illustrates how different categories of projects (e.g., foundational, applied) can be strategically planned to provide weighted contributions across the three primary research questions. Adopted from marco2024.
• Figure 4: The SECI Model of Knowledge Creation nonaka1995knowledge, illustrating the spiral process through which tacit and explicit knowledge are converted and amplified within an organization.
• Figure 5: Layered Architecture of a Knowledge Management System. This model provides a structured view of the components required to build a comprehensive KMS, from data sources to the user-facing portal gronau2009knowledgecafe.