Intelligent Knowledge Mining Framework

Updated 26 December 2025

The Intelligent Knowledge Mining Framework is a dual-stream approach that converts raw, unstructured data into unified, semantically enriched knowledge graphs, facilitating advanced reasoning.
It integrates a horizontal mining process using formal transformation functions with a vertical archiving stream that ensures complete provenance tracking and reproducibility.
The framework supports dynamic knowledge ecosystems by applying ontology-driven models and policy-based preservation to sustain long-term data utility.

The Intelligent Knowledge Mining Framework (IKMF) is a comprehensive architecture for transforming heterogeneous, unstructured data sources into machine-actionable, semantically-rich knowledge, while ensuring provenance, computational reproducibility, and reliable long-term preservation. IKMF addresses persistent challenges in the integration, interpretation, and stewardship of digital assets, particularly in data-intensive scientific and enterprise environments. Central to its design is a dual-stream architecture: a horizontal knowledge mining process that formalizes the transformation of raw data into a knowledge representation suitable for advanced reasoning, and a vertical trustworthy archiving stream that encases artifacts in preservation- and provenance-aware packages. By establishing formal transformation steps, explicit ontology-driven models, and policy-driven preservation workflows, the IKMF reference model serves as a blueprint for dynamic, living knowledge ecosystems supporting both advanced AI-driven analysis and verifiable, durable stewardship of digital artifacts (Vu, 19 Dec 2025).

1. Motivation and Foundational Problems

The conceptual foundation of IKMF is driven by three systemic gaps observed across data-intensive disciplines:

Semantic Gap (MS1): Absence of a machine-interpretable semantic layer impedes automated reasoning and integration, forcing heavy cognitive loads on humans to synthesize insights from disconnected, heterogeneous sources.
Reproducibility Gap (MS2): Weak provenance connections between data, code, and scholarly output undermine scientific integrity, replicability, and trust in computational results.
Preservation Gap (MS3): Passive, non-standardized storage of digital assets risks irretrievable loss, obsolescence, and destruction of context necessary for future utility.

IKMF poses three core research questions: RQ1: How can heterogeneous raw data be transformed into unified, formal knowledge graphs capable of supporting advanced inference? RQ2: How can complete computational and semantic provenance be modeled and captured for automated, verifiable reproduction of results? RQ3: How should an active, policy-driven preservation layer be constructed to guarantee future usability and trustworthiness of scientific and institutional knowledge assets? (Vu, 19 Dec 2025).

2. Dual-Stream Architecture and Transformation Pipeline

The framework’s architecture encompasses two interdependent streams:

2.1 Information and Knowledge Mining Process

This process implements a formalized four-stage pipeline:

Stage	Input / Output	Transformation Function
1. Data	$D_0$ (raw files, sensors)	$f_1: D_0 \to C_1$ parses format, generates structured content
2. Content	$C_1$ → annotated docs	$f_2: C_1 \to A_2$ applies IR (BM25, dense retrievers), NER, parsing
3. Knowledge	$A_2$ → knowledge triples	$f_3: A_2 \to K_3$ , entity linking, relation extraction, RDF mapping
4. Knowledge Base	$K_3$ + ontology $\mathcal{O}$ , rules $\mathcal{R}$	$f_4: (K_3, \mathcal{O}, \mathcal{R}) \to G_K$ ; semantic closure, multi-hop reasoning

Semantic enrichment fuses text, tabular, image, or other modalities into a unified triple-store of the form $K_3 \subseteq \{ (s,p,o)\ |\ s,o\in \mathrm{URI},\ p\in\mathrm{URI}\}$ . The reasoning stage admits closure under OWL ontologies and SWRL rules: $G_K = \mathsf{Infer}(K_3 \cup \mathcal{O} \cup \mathcal{R})$ , exposing inferred facts, inconsistencies, and multi-hop relationships (Vu, 19 Dec 2025).

2.2 Trustworthy Archiving Stream

This stream encapsulates outputs from the mining process into robust, reproducible, policy-compliant archival packages:

Assembly: Bundle all data and transformations $(D_0, C_1, A_2, K_3, G_K)$ with persistent identifiers (PIDs).
Provenance & Reproducibility: Model the full computational lineage as a provenance DAG $G_P = (V_P, E_P)$ (data, code, environment, etc.), using integrity constraints (e.g., $\forall v \in V_P : \mathrm{checksum}(v) = h(v)$ ) and reproducibility measure $R(P)$ (binary: 1 if target $G_K$ is regenerated; else 0).
Preservation: Execute policy-driven preservation via iRODS-style rules, wrapping artifacts in RO-Crate or BagIt Archival Information Packages (AIPs) with PREMIS, CERIF/CRIS metadata, and enabling format migration or emulation in response to systemic changes and user needs (Vu, 19 Dec 2025).

3. Scientific Methodology and Provenance Models

IKMF’s development adopts a Design Science paradigm incorporating iterative empirical study, formal theory-building, system prototyping, and experimentation. Derived from multicase analyses (MENHIR, STOP, SMILE, GenDAI, EUt+), the methodology precisely formalizes:

Semantic Enrichment Pipelines: Multi-stage transformations codified into $f_{1\ldots4}$ , each a mathematically-defined mapping with traceable provenance.
Provenance Ontologies: Full adherence to PROV-O standard, explicit DAGs that admit fine-grained queries, integrity checking, and enforceable computational reproducibility.
Preservation Models: Rule-driven policy engines encoding not only data fixity, but also contextual, project-level, and technical metadata for long-term stewardship (Vu, 19 Dec 2025).

4. Ontology, Entity–Relationship, and Packaging Schemas

Central to reasoning and preservation is rigorous modeling in standard semantic web and preservation metamodels:

Core Ontologies: Expressed in OWL 2 and SWRL, e.g.,

$\mathbf{Class}:\;\mathit{Project}\;, \quad \mathbf{Class}:\;\mathit{Person}\;, \quad \mathbf{Property}:\;\mathit{isPrincipalInvestigatorOf} \subseteq \mathit{worksOn}$

Example triple: :DrSmith :isPrincipalInvestigatorOf :ProjectX . Vocabulary control leverages SKOS taxonomies, e.g., ex:Topic1 skos:broader ex:Topic0 .

Preservation Packaging: Uses JSON-LD-compliant RO-Crate, supporting self-describing data, tools, and metadata; example:

{
  "@context": "https://w3id.org/ro/crate/1.1/context",
  "@graph": [
    { "@id": "crate-metadata.json", "@type": "CreativeWork", ... },
    { "@id": "data/raw.fastq", "checksum": "...", ... },
    { "@id": "analysis/container.sif", "@type": "SoftwareSourceCode", ... }
  ]
}

Policy-compliant AIP generation ensures future accessibility, machine-actionability, and provenance traceability (Vu, 19 Dec 2025).

5. Integration Mechanisms and Living Knowledge Ecosystem

IKMF embeds integration loops to sustain currency, adaptivity, and the evolution of institutional knowledge:

Feedback Loops: Inferred facts in $G_K$ can retrain sub-symbolic models (e.g., neurosymbolic cycles), and preservation metrics inform re-ingestion or migration strategies.
Dynamic Updates: Continuous data ingestion, automated policy-triggered re-packaging, and semantic governance enforce a “living” knowledge base.
User Interaction & Provenance: End-users perform semantic search and reasoning; all accesses and mutations generate new provenance traces, enabling transparent auditing and adaptive preservation (Vu, 19 Dec 2025).

6. Implementation Guidelines and Comparative Research Context

The IKMF’s implementation rests on modular, microservice-oriented stages, container-based execution for auto-capture of provenance, explicit semantic web technologies (OWL2 + SWRL), and policy engines (iRODS) for preservation. Essential practices include automated RO-Crate creation, semantic governance for ontological assets, and standards-based compliance for preservation and interoperability.

Distinctively, the IKMF advances beyond classical data mining frameworks and knowledge management systems by tightly coupling AI-driven semantic integration (knowledge graph construction and reasoning) with verifiable, policy-driven preservation. This differs from logic-based or agent-based knowledge mining approaches, which may lack formal preservation and computational reproducibility guarantees (cf. (Leandro et al., 2016, Jayabrabu et al., 2012)), and from trend-mining architectures focused on utility and recency without end-to-end provenance and archiving (Gan et al., 2019). IKMF’s rigorous formalization and dual-stream blueprint provide a scalable foundation for maintaining integrity, provenance, and analytic vitality throughout the lifecycle of digital knowledge (Vu, 19 Dec 2025).

References

"Intelligent Knowledge Mining Framework: Bridging AI Analysis and Trustworthy Preservation" (Vu, 19 Dec 2025)
Related architectures and data mining frameworks (Leandro et al., 2016, Kerdprasop et al., 2011, Jayabrabu et al., 2012, Gan et al., 2019)