Wisdom Mining

From Wikitia
Jump to navigation Jump to search

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

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

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

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

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

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

  • Data mining
  • Artificial intelligence
  • Knowledge discovery in databases
  • Cognitive computing
  • Decision support system

References

  1. ↑ 1.0 1.1 Khan, Salma; Shaheen, Muhammad (2021). "From Data Mining to Wisdom Mining". Journal of Information Science. SAGE Publications. 49 (4): 952–975. doi:10.1177/01655515211030872.
  2. ↑ 2.0 2.1 Khan, Salma; Shaheen, Muhammad (2023). "Wisdom Mining: Future of Data Mining". Recent Patents on Engineering. Bentham Science. 17 e280322202673. doi:10.2174/1872212116666220328121113.
  3. ↑ Ackoff, Russell L. (1989). "From Data to Wisdom". Journal of Applied Systems Analysis. 16: 3–9.
  4. ↑ Rowley, Jennifer (2007). "The Wisdom Hierarchy: Representations of the DIKW Hierarchy". Journal of Information Science. 33 (2): 163–180. doi:10.1177/0165551506070706.
  5. ↑ 5.0 5.1 Khan, Salma; Shaheen, Muhammad (2022). "WisRule: First Cognitive Algorithm of Wise Association Rule Mining". Journal of Information Science. SAGE Publications. 50 (4): 874–893. doi:10.1177/01655515221108695.
  6. ↑ Kumagai, A.; Jeong, S.; Kim, D.; Kong, H. J.; Oh, S.; Lee, S. S. (2023). "Deep semantic representation for sustainable data mining". Scientific Reports. Nature. 13 (1): 726. doi:10.1038/s41598-023-28086-1. PMC 9839668. PMID 36639726.
  7. ↑ 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. Nature. 15 (1): 7635. doi:10.1038/s41598-024-57547-4.
  8. ↑ "Hybrid fuzzy–context mining in digital ecosystems". International Journal of Machine Learning and Cybernetics. Springer. 2025. doi:10.1007/s10791-025-09644-9.
  9. ↑ "Ethical intelligence and contextual AI models". IEEE Access. IEEE. 2024. doi:10.1109/ACCESS.2024.10730315.
  10. ↑ 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.
  11. ↑ Tsarkos, Athanasios (2025). "Integrating Organizational Wisdom in Middle Level Schools: An Integrative Literature Review". RMLE Online. Taylor & Francis. 48 (3): 1–24. doi:10.1080/19404476.2025.2460840.
  12. ↑ "Cognitive frameworks for sustainability analytics". International Journal of Modelling, Control and Physics. 2024. doi:10.1504/IJMCP.2024.137636.
  13. ↑ "Contextualized knowledge discovery using semantic graph embeddings". ACM Digital Library. 2025. doi:10.1145/3696500.3696545.
  14. ↑ "Explainable contextual learning for planetary data mining". Earth Science Informatics. 2023. doi:10.1007/s12145-023-01032-5.
  15. ↑ 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.

External links

Add External links

This article "Wisdom Mining" is from Wikipedia. The list of its authors can be seen in its historical. Articles taken from Draft Namespace on Wikipedia could be accessed on Wikipedia's Draft Namespace.

Retrieved from "https://wikitia.com/index.php?title=Wisdom_Mining&oldid=227618"