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.

翻译：数字数据的空前激增给所有数据密集型领域在访问、整合和价值创造方面带来了重大挑战。有价值的信息通常被封装在分散的系统、非结构化文档和异构格式中，形成了阻碍高效利用和协作决策的数据孤岛。本文提出了智能知识挖掘框架（IKMF），这是一个旨在弥合动态人工智能驱动分析与可信长期保存之间关键差距的综合概念模型。该框架提出了一种双流架构：一个水平的挖掘过程，将原始数据系统性地转化为语义丰富、机器可操作的知识；以及一个并行的可信存档流，确保这些资产的数据完整性、来源可靠性和计算可复现性。通过定义这种共生关系的蓝图，本文为将静态存储库转变为促进可操作情报从生产者流向消费者的活体生态系统提供了基础模型。本文阐述了指导该框架研究与开发的动机、问题陈述和关键研究问题，介绍了基础科学方法论，并详细说明了其概念设计与建模过程。