PapersFlow Research Brief
Experience-Based Knowledge Management
Research Guide
What is Experience-Based Knowledge Management?
Experience-Based Knowledge Management is a method of representing and managing knowledge derived from past experiences through structures such as Decisional DNA and Set of Experience Knowledge Structure within ontology systems.
The field encompasses 2,572 works focused on experience-based knowledge representation, including Decisional DNA and Set of Experience Knowledge Structure in ontology systems. Applications appear in renewable energy sources, international standards, Internet of Things, cognitive vision systems, and environmental management. These structures enable capturing and reusing decisional experiences in engineering contexts.
Topic Hierarchy
Research Sub-Topics
Set of Experience Knowledge Structure
Researchers formalize SOEKS as ontology-based representations of experiential knowledge for decision reuse in engineering domains. Developments include OWL implementations and applications in virtual engineering.
Decisional DNA Modeling
This field models decisions as DNA-like sequences for experience-based knowledge management in complex systems. Integrations with IoT and semantic networks support renewable energy and manufacturing applications.
Virtual Engineering Objects for Industry 4.0
Studies design VEOs and VEPs as specialized cyber-physical systems for experience-based manufacturing simulation. Focus includes process modeling and integration with Industrie 4.0 standards.
Experience-Based Knowledge in Renewable Energy
Researchers apply ontology systems to manage experiential data from renewable sources like wind turbines and solar plants. Emphasis is on reliability analysis and gearbox failure prediction.
Uncertainty Analysis in High-Dimensional Knowledge Systems
This sub-topic develops dependence modeling for uncertainty quantification in experience-rich datasets from environmental and engineering contexts. Techniques support GHG inventories and virtual factory simulations.
Why It Matters
Experience-Based Knowledge Management supports Industry 4.0 by enabling virtual representations of engineering objects and processes that store and improve upon past experiences. Shafiq et al. (2015) in "Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0" describe Virtual Engineering Objects (VEO) and Virtual Engineering Processes (VEP) as experience-based knowledge forms integrated with Cyber-Physical Systems, applied in manufacturing computerization. Similarly, Shafiq et al. (2016) in "Virtual Engineering Factory: Creating Experience Base for Industry 4.0" demonstrate Decisional DNA-based representation for round-the-clock connection of manufacturing objects, with 112 citations indicating practical adoption in engineering factories.
Reading Guide
Where to Start
"An OWL Ontology of Set of Experience Knowledge Structure" by Sanín et al. (2007), as it introduces the foundational Set of Experience Knowledge Structure ontology, providing the core formalism for experience representation used in later works.
Key Papers Explained
Sanín et al. (2007) in "An OWL Ontology of Set of Experience Knowledge Structure" establishes SOEKS as the base ontology for decisional experiences. Shafiq et al. (2015) in "Virtual Engineering Object (VEO): Toward Experience-Based Design and Manufacturing for Industry 4.0" builds on this by defining VEOs that embody experience knowledge for Industry 4.0. Shafiq et al. (2015) in "Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0" extends VEO to processes and CPS integration, while Shafiq et al. (2016) in "Virtual Engineering Factory: Creating Experience Base for Industry 4.0" applies Decisional DNA to factory-level experience bases.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Current work centers on applying Decisional DNA and VEOs to Industrie 4.0 manufacturing, as seen in the 2016 paper by Shafiq et al. No recent preprints or news indicate ongoing extensions to cognitive vision or environmental management frontiers.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | THE REFINEMENT TO THE 2006 IPCC GUIDELINES FOR NATIONAL GREENH... | 2019 | Fundamental and Applie... | 1.6K | ✓ |
| 2 | The Theory and Design of Electron Beams | 1950 | Students Quarterly Jou... | 963 | ✕ |
| 3 | Uncertainty Analysis with High Dimensional Dependence Modelling | 2006 | Wiley series in probab... | 435 | ✕ |
| 4 | Virtual Engineering Object / Virtual Engineering Process: A sp... | 2015 | Procedia Computer Science | 182 | ✓ |
| 5 | Virtual Engineering Object (VEO): Toward Experience-Based Desi... | 2015 | Cybernetics & Systems | 159 | ✕ |
| 6 | Product Environmental Footprint (PEF) Guide | 2012 | Lirias (KU Leuven) | 157 | ✓ |
| 7 | A Cost-Optimal Assessment of Buildings in Ireland Using Direct... | 2014 | Arrow - TU Dublin (Tec... | 147 | ✓ |
| 8 | Gearbox Reliability Collaborative Project Report: Findings fro... | 2011 | — | 117 | ✓ |
| 9 | Virtual Engineering Factory: Creating Experience Base for Indu... | 2016 | Cybernetics & Systems | 112 | ✕ |
| 10 | An OWL Ontology of Set of Experience Knowledge Structure | 2007 | Zenodo (CERN European ... | 77 | ✓ |
Latest Developments
Recent developments in Experience-Based Knowledge Management research include exploring the evolving roles, skills, and expertise in KM as of August 2025 (APQC), identifying emerging trends such as AI integration and cross-functional sharing shaping KM practices in 2025 (Bloomfire, November 2024), and proposing design principles for KM systems that support tacit and procedural knowledge, demonstrated through prototypes for knowledge dissemination (IIS, 2025). Additionally, systematic reviews highlight AI's role in transforming KM with techniques like machine learning and neural networks (Frontiers, July 2025).
Sources
Frequently Asked Questions
What is a Virtual Engineering Object (VEO)?
A Virtual Engineering Object (VEO) is an experience-based knowledge representation of an engineering object that embodies associated knowledge and experience. Shafiq et al. (2015) in "Virtual Engineering Object (VEO): Toward Experience-Based Design and Manufacturing for Industry 4.0" explain that VEOs add, store, improve, and reuse experience for Industry 4.0 design and manufacturing. This structure supports self-learning capabilities in engineering contexts.
How does Decisional DNA function in knowledge management?
Decisional DNA serves as a formal representation of decision events, acting as fingerprints of company experiences. Shafiq et al. (2016) in "Virtual Engineering Factory: Creating Experience Base for Industry 4.0" apply Decisional DNA for knowledge representation of manufacturing objects and processes in Industry 4.0. It enables collection, distribution, and sharing of decisional knowledge.
What is Set of Experience Knowledge Structure?
Set of Experience Knowledge Structure (SOEKS) is an OWL ontology for collecting and formalizing decisional knowledge as experience fingerprints. Sanín et al. (2007) in "An OWL Ontology of Set of Experience Knowledge Structure" present it as a structure for knowledge-explicit sharing in companies. SOEKS captures decision events for reuse in ontology systems.
What applications exist for experience-based knowledge in Industry 4.0?
Experience-based knowledge applies to Virtual Engineering Factories and Cyber-Physical Systems in manufacturing. Shafiq et al. (2015) in "Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0" link VEO and VEP to Industrie 4.0 through experience representation. These support computerization and communication of engineering objects.
How does experience-based management relate to ontology systems?
Ontology systems formalize experience-based knowledge using structures like SOEKS and Decisional DNA. Sanín et al. (2007) in "An OWL Ontology of Set of Experience Knowledge Structure" define SOEKS within OWL for decisional fingerprints. This integration facilitates structured knowledge sharing across domains like IoT and renewable energy.
Open Research Questions
- ? How can Decisional DNA be extended to fully autonomous self-improving Virtual Engineering Factories?
- ? What formal mappings exist between Set of Experience Knowledge Structure and Cyber-Physical Systems for real-time decision support?
- ? Which ontology extensions improve scalability of experience-based knowledge for large-scale IoT deployments?
- ? How do Virtual Engineering Objects integrate with international standards for renewable energy management?
Recent Trends
The field maintains 2,572 works with no specified 5-year growth rate.
Key developments include Decisional DNA for Virtual Engineering Factories in Shafiq et al. "Virtual Engineering Factory: Creating Experience Base for Industry 4.0" (112 citations) and VEO extensions to CPS in Shafiq et al. (2015) papers (182 and 159 citations).
2016No recent preprints or news coverage in the last 12 months.
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