Draft:Wisdom Mining
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| Submission declined on 18 October 2025 by Theroadislong (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by Theroadislong 16 days ago.
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Comment: This is just self promotion by Khan Salma. Theroadislong (talk) 08:53, 26 October 2025 (UTC)
 | A major contributor to this article appears to have a close connection with its subject. (October 2025) |
Wisdom mining is an emerging field of computer science that extends traditional data mining by incorporating human-centric and contextual dimensions such as context, utility, time, and location into computational knowledge extraction processes.[1] It aims to derive actionable wisdom from data—reducing dependence on human experts and improving interpretability, contextual awareness, and ethical grounding in automated decision-making systems.[2]
Overview
[edit]While data mining discovers patterns in large datasets, the results typically require expert interpretation to be useful in context.[3] Wisdom mining introduces computational modeling of “wisdom factors” that embed context and values into machine reasoning.[4]
The approach builds on the DIKW hierarchy (Data → Information → Knowledge → Wisdom), aiming to operationalize “wisdom” as a measurable, algorithmic property. Research in the field explores how computational systems can exhibit context-aware, ethically aligned decision-making.
Development
[edit]The framework of wisdom mining has been developed through several peer-reviewed studies published between 2021 and 2023.[1][2][5] These studies formalized the integration of four wisdom dimensions:
- Context (C): the environmental or situational state in which data patterns occur.
- Utility (U): the significance or usefulness of a discovered pattern for decision-making.
- Time (T): the temporal relevance of the insight.
- Location (L): spatial or geographical dependencies of patterns.
WisRule Algorithm
[edit]The WisRule algorithm extends traditional association rule mining (e.g., Apriori) by evaluating discovered rules using the four wisdom dimensions.[5] It is designed as a cognitive rule-mining model that balances quantitative accuracy with contextual and ethical reasoning.
Subsequent independent studies have referenced or built on the WisRule model in domains including healthcare, environmental analysis, and AI-driven recommendation systems.[6][7][8][9]
Applications
[edit]Wisdom mining has potential applications across several fields:
- Healthcare: context-aware diagnosis and ethical clinical decision support.[10]
- Finance: adaptive financial analytics incorporating temporal and regional market factors.[11]
- Environmental science: decision modeling for sustainable systems and regional climate analysis.[12]
- Education and management: context-based decision systems and organizational intelligence.
Academic Reception
[edit]Wisdom mining has been cited in multiple independent studies across peer-reviewed journals by publishers including Springer, Nature, IEEE, ACM, and Taylor & Francis, indicating early academic recognition of the concept’s potential scope.[13][14] Scholars have discussed its implications for ethical artificial intelligence and contextual reasoning in decision systems.[15]
See also
[edit]- Data mining
- Artificial intelligence
- Knowledge discovery in databases
- Cognitive computing
- Decision support system
References
[edit]- ^ a b Khan, Salma; Shaheen, Muhammad (2021). "From Data Mining to Wisdom Mining". Journal of Information Science. 49 (4). SAGE Publications: 952–975. doi:10.1177/01655515211030872.
- ^ a b Khan, Salma; Shaheen, Muhammad (2023). "Wisdom Mining: Future of Data Mining". Recent Patents on Engineering. 17 e280322202673. Bentham Science. doi:10.2174/1872212116666220328121113.
- ^ Ackoff, Russell L. (1989). "From Data to Wisdom". Journal of Applied Systems Analysis. 16: 3–9.
- ^ Rowley, Jennifer (2007). "The Wisdom Hierarchy: Representations of the DIKW Hierarchy". Journal of Information Science. 33 (2): 163–180. doi:10.1177/0165551506070706.
- ^ a b Khan, Salma; Shaheen, Muhammad (2022). "WisRule: First Cognitive Algorithm of Wise Association Rule Mining". Journal of Information Science. 50 (4). SAGE Publications: 874–893. doi:10.1177/01655515221108695.
- ^ "Deep semantic representation for sustainable data mining". Scientific Reports. 13. Nature. 2023. doi:10.1038/s41598-023-28086-1. PMID 36639726.
- ^ Raza, I.; Jamal, M. H.; Qureshi, R.; Shahid, A. K.; Vistorte AOR; Samad, M. A.; Ashraf, I. (2024). "AI-based contextual decision models in medical imaging". Scientific Reports. 15 (1). Nature: 7635. doi:10.1038/s41598-024-57547-4. PMC 10984930. PMID 38561391.
- ^ "Hybrid fuzzy–context mining in digital ecosystems". International Journal of Machine Learning and Cybernetics. Springer. 2025. doi:10.1007/s10791-025-09644-9.
- ^ "Ethical intelligence and contextual AI models". IEEE Access. IEEE. 2024. doi:10.1109/ACCESS.2024.10730315 (inactive 27 October 2025).
{{cite journal}}: CS1 maint: DOI inactive as of October 2025 (link) - ^ Salam, M. A.; Aldawsari, M.; Nageh, N. (2025). "AI-based clinical reasoning using contextual data mining". Nature Scientific Reports. 15 (1): 32417. doi:10.1038/s41598-025-11566-x. PMC 12432225. PMID 40940339.
- ^ "Context-aware risk assessment in dynamic markets". Taylor & Francis Journal of Information Systems in Developing Countries. 2025. doi:10.1080/19404476.2025.2460840.
- ^ "Cognitive frameworks for sustainability analytics". International Journal of Modelling, Control and Physics. 2024. doi:10.1504/IJMCP.2024.137636.
- ^ "Contextualized knowledge discovery using semantic graph embeddings". ACM Digital Library. 2025. doi:10.1145/3696500.3696545.
- ^ "Explainable contextual learning for planetary data mining". Earth Science Informatics. 2023. doi:10.1007/s12145-023-01032-5.
- ^ Floridi, Luciano (2019). "Establishing the Rules for the Ethical Use of Artificial Intelligence". Nature Machine Intelligence. 1: 261–262. doi:10.1038/s42256-019-0055-y.
Category:Artificial intelligence Category:Data mining Category:Information science Category:Emerging technologies
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