Foundations of intelligent knowledge management

Human Systems Management

Volumn 28, Issue 4, 2009, Pages 145-161

  Zhang, Lingling ^a,b   Li, Jun ^a,b   Shi, Yong ^b,c   Liu, Xiaohui ^d  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^c UNIVERSITY OF NEBRASKA AT OMAHA   (United States)

^d BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Data; Data mining; Intelligent knowledge; Intelligent knowledge management; Knowledge management

Indexed keywords

BUSINESS DECISIONS; BUSINESS INTELLIGENCE; DATA; HIDDEN PATTERNS; INTELLIGENT KNOWLEDGE; KNOWLEDGE BASE; KNOWLEDGE BASIS; KNOWLEDGE COMPONENTS; LARGE DATA; LARGE DATABASE; NEW OPPORTUNITIES; NON-TRIVIAL; QUANTITATIVE ANALYSIS; TECHNICAL PROCESS;

KNOWLEDGE BASED SYSTEMS; KNOWLEDGE MANAGEMENT; MANAGEMENT; MANAGEMENT SCIENCE;

DATA MINING;

EID: 74549121384     PISSN: 01672533     EISSN: None     Source Type: Journal    
DOI: 10.3233/HSM-2009-0706     Document Type: Article

References (71)

1. C.C. Aggarwal and P.S. Yu, A New Framework for Itemset Generation, ACM Press, New York, NY, USA, 1998.
2. S.S. Anand, D.A. Bell and J.G. Hughs, EDM: A general framework for data mining based on evidence theory, Data and Knowledge Engineering 18 (1996), 189-223.
3. J.A. Arias, J. Pinquier and R. André-Obrecht, Evaluation of classification techniques for audio indexing, in: Proceedings of 13th European Signal Processing Conference, Antalya, Turkey, 2005.
4. T. Beckman, The current state of knowledge management, in: Knowledge Management Handbook, J. Liebowitz, ed., CRC Press, Boca Raton, 1999, pp. 1-1-1-22.
5. P. Berger and T. Luckman, The Social Construction of Reality, Doubleday, Garden City, NY, 1966.
6. P. Brezillion and J. Pomerol, Contextual knowledge sharing and cooperation in intelligent assistant systems, Le Travail Humain 62(3) (1999), 223-246.
7. S. Cauvin and B. Braunschweig, Graphical knowledge representation in the ALEXIP system for petrochemical process supervision, Application of Artificial Intelligence in Engineering 2 (1993), 219-233.
8. C. Despres and D. Chauval, A thematic analysis of the thinking in knowledge management, in: The Present and the Promise of Knowledge Management, C. Despres and D. Chauvel, eds, Butterworth Heinemann, Boston, MA, 2000, pp. 55-86.
9. Dictionary of Military and Associated Terms, US Department of Defense, 2005.
10. R. Dieng, O. Corby, A. Giboin and M. Ribière, Methods and tools for corporate knowledge management, International Journal of Human Computer Studies 51(3) (1999), 567-598.
11. J.C. Dunn, Well separated clusters and optimal fuzzy partitions, Journal of Cybernetics 4(3) (1974), 95-104.
12. U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth, From data mining to knowledge discovery: An overview, in: Advances in Knowledge Discovery and Data Mining, AAAI Press/TheMIT Press, Menlo Park, 1996, pp. 1-34.
13. E.A. Feigenbaum, The art of artificial of intelligence: Themes and case studies of knowledge engineering, in: The Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA, 1977, pp. 1014-1029.
14. W.J. Frawley, G. Piatetsky-Shapiro and C.J. Matheus, Knowledge discovery in databases: An overview, in: Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W.J. Frawley, eds, AAAI/MIT Press, 1991.
15. A. Freitas, On rule interestingness measures, Knowledge Based Systems 12(5,6) (1999), 309-315.
16. L. Geng and H.J. Hamilton, Choosing the right lens: Finding what is interesting in data mining, Studies in Computational Intelligence 43 (2007), 3-24.
17. G. Goldkuhl and E. Braf, Contextual knowledge analysis - understanding knowledge and its relations to action and communication, in: Proceedings of 2nd European Conference on Knowledge Management, IEDC-Bled School of Management, Slovenia, 2001.
18. B. Gray and M.E. Orlowska, CCAIIA: Clustering categorical attributes into interesting association rules, in: Proceedings of Second Pacific Asia Conference on Knowledge Discovery in Databases (PAKDD98), Melbourne, 1998, pp. 132-143.
19. J. Gu and X. Tang, How to synthesize experts opinions building consensus form different perspectives, KSS'2004: The Fifth International Symposium on Knowledge Management and Systems Sciences, JAIST, Japan, pp. 291-295.
20. F. Guillet and H.J. Hamilton, eds, Quality Measures in Data Mining (Studies in Computational Intelligence), Springer- Verlag, New York, 2007.
21. J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd edn, Morgan Kaufmann, San Fransisco, CA, USA, 2006.
22. J.L. Hou, M.T. Sun and H.C. Chuo, An intelligent knowledge management model for construction and reuse of automobile manufacturing intellectual properties, The International Journal of Advanced Manufacturing Technology 26(1) (2005), 169-182.
23. http://searchdatamanagement.techtarget.com/sDefinition/0,sid91-gci211894, 00.html, 2005.
24. M. Kamber and R. Shinghal, Evaluating the interestingness of characteristic rules, in: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, 1996.
25. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and A.I. Verkamo, Finding interesting rules from large sets of discovered association rules, in: Proceedings of the 3rd International Conference on Information and Knowledge Management (CIKM'94), Gaithersburg, MD, ACM Press, 1994.
26. W. Klosgen, Explora: A multipattern and multistrategy discovery assistant, in: Advances in Knowledge Discovery and Data Mining, Fayyad et al., eds, AAAI Press/MIT Press, Menlo Park, CA, 1996, pp. 249-271.
27. R. Kotagiri, Contrast pattern mining and application, in: IEEE Data Mining Forum, Hong Kong, 2008.
28. N. Lavrac, P. Flach and B. Zupan, Rule evaluation measures: A unifying view, in: Proceedings of the Ninth International Workshop on Inductive Logic Programming (ILP'99), Bled, Slovenia, S. Dzeroski and P.A. Flach, eds, Springer-Verlag, 1999, pp. 174-185.
29. W. Lee and S.J. Stolfo, A framework for constructing features and models for intrusion detection systems, ACM Transactions on Information and System Security 3(4) (2000), 227-261.
30. B. Liu,W. Hsu and S. Chen, Using general impressions to analyze discovered classification rules, in: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, Vancouver, Canada, 1997, pp. 31-36.
31. J. Ludwig and G. Livingstone, What's new using prior models as a measure of novelty in knowledge discovery, in: Proceedings of the 12th IEEE Conference on Tools with Artificial Intelligence, 2000.
32. K. Mcgarry, A survey of interestingness measures for knowledge discovery, The Knowledge Engineering Review 20(1) (2005), 39-61.
33. K. Mertins, P. Heisig and J. Vorbeck, KnowledgeManagement- Concepts and Best Practices, London, Springer-Verlag, 2003.
34. H.R. Nemati, D.M. Steiger, L.S. Iyer and R.T. Herschel, Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing, Decision Support Systems 33 (2002), 143-161.
35. I. Nonaka, Speech at the Chinese Academy of Sciences, April 2009.
36. I. Nonaka, R. Toyama and N. Konno, SECI, Ba, and leadership: A unifying model of dynamic knowledge creation, in: New Perspectives on Knowledge-Based Firm and Organization, D.J. Teec and I. Nonaka, eds, Oxford University Press, New York, 2000.
37. D. Olson and Y. Shi, Introduction to Business Data Mining, McGraw-Hill/Irwin, Englewood Cliffs, NJ, 2007.
38. X. Pan, Research on Some Key Knowledge of Knowledge Management Integrating Context, Graduate University of Zhejiang University, 2005, pp. 23-28 (in Chinese).
39. Y. Peng, G. Kou, Y. Shi and Z. Chen, A descriptive framework for the field of data mining and knowledge discovery, International Journal of Information Technology and Decision Making 7(4) (2008), 639-682.
40. G. Piatetsky-Shapiro and G.L. Matheus, Knowledge discovery work-bench for exploring business databases, International Journal of Intelligent System 7 (1992), 675-686.
41. G. Piatetsky-Shapiro, W.J. Frawley, S. Brin et al., Discovery, analysis, and presentation of strong rules, Computational Statistics 16 (1997), 387-398.
42. G. Piatetsky-Shapiro and C.J. Matheus, Knowledge discovery in databases: An overview, in: Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W.J. Frawley, eds, AAAI Press/MIT Press, Menlo Park, CA, 1999, pp. 1-27.
43. G. Polese, M. Troiano and G. Tortora, A data mining based system supporting tactical decisions, in: SEKE'2002, Ischia, Italy, 2002, pp. 681-684.
44. D. Rachkovskij, Representation and processing of structures with binary sparse distributed codes, IEEE Transactions on Knowledge and Data Engineering 13(2) (2001), 261-276.
45. Y. Shi, Humancasting: A fundamental method to overcome user information overload, Information 3(1) (2000), 127-143.
46. Y. Shi and X. Li, Knowledge management platforms and intelligent knowledge beyond data mining, in: Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom, Y. Shi et al., eds, IOS Press, 2007, pp. 272-288.
47. A. Silberschatz and A. Tuzhilin, On subjective measures of interestingness in knowledge discovery, in: First International Conference on Knowledge Discovery and Data Mining, Montreal, Canada, 1995, pp. 275-281.
48. A. Siberschatz and A. Tuzhilin, What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering 8(6) (1996), 970-974.
49. P. Smyth and R.M. Goodman, An information theoretic approach to rule induction from databases, IEEE Transactions on Knowledge and Data Engineering 4(4) (1992), 301-316.
50. P. Tan, V. Kumar and J. Srivastava, Selecting the right IM for association patterns, in: Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining (KDD'02), Edmonton, Canada, 2002, pp. 32-41.
51. The American Heritage Dictionary of the English Language, Houghton Mifflin Company, 2003.
52. H. Toivonen, M. Klemettinen, P. Ronkainen et al., Pruning and grouping discovered association rules, in: ECML-95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases, New York, NY, USA, 1995.
53. Z. Wang, Knowledge System Engineering, Science Publication, Beijing, 2004 (in Chinese).
54. G. Webb, Finding the real pattern, in: IEEE Data Mining Forum, Hong Kong, 2008.
55. What is data? - A definition from Whatis.com.
56. What is data? - A word definition from the Webopedia, 2003, available at: http://www.webopedia.com/TERM/D/data.html.
57. K.M. Wiig, Knowledge Management: The Central Management Focus for Intelligent-Acting Organization, Schenma Press, Arlington, TX, 1995, p. 157.
58. K.M. Wiig, Knowledge management: Where did it come from and where it go?, Expert System with Applications 13(1) (1997), 1-14.
59. Wikipedia, Knowledge, 2008.
60. A.K.C.Wong, Association pattern analysis for pattern pruning, pattern clustering and summarization, in: IEEE Data Mining Forum, Hong Kong, 2008.
61. Y.Y. Yao and N. Zhong, An analysis of quantitative measures associated with rules, in: Proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, Beijing, China, 1999, pp. 479-488.
62. H. Yao, H.J. Hamilton and C.J. Butz, A foundational approach to mining itemset utilities from databases, in: Proceedings of the 4th SIAM International Conference on Data Mining, 2004.
63. J.P. Yoon and L. Kerschberg, A framework for knowledge discovery and evolution in database, IEEE Transactions on Knowledge and Data Engineering 5 (1993), 973-979.
64. M. Zeleny, Knowledge of enterprise: Knowledge management technology?, International Journal of Information Technology & Decision Making 1(2) (2002), 181-207.
65. M. Zeleny, From knowledge to wisdom: On being informed and knowledgeable, becoming wise and ethical, International Journal of Information Technology & Decision Making 4(5) (2006), 751-762.
66. M. Zeleny, Human Systems Management: Integrating Knowledge, Management and Systems, World Scientific, Hackensack, NJ, 2007.
67. L.L. Zhang, J. Li and Y. Shi, Study on improving efficiency of knowledge sharing in knowledge-intensive organization, in: WINE'2005, 2005, pp. 816-825.
68. L.L. Zhang, J. Li, X. Zheng, X. Li and Y. Shi, Study on a process-oriented knowledge management model, International Journal of Knowledge and Systems Sciences 5(1) (2008), 37-44.
69. Y.X. Zhong, An introduction to cognetics - unified theory of information, knowledge and intelligence, China EngineeringScience 6(6) (2004) (in Chinese).
70. Y.X. Zhong, On the laws of information-knowledge-intelligence transforms, Journal of Beijing University of Posts and Telecommunications 30(1) (2007), 1-8 (in Chinese).
71. Z. Zhu, Knowledge management: Learning from diverse styles, in: Proceedings of KSS'2002 & MCS'2002, Shanghai, China, August 7-8, 2002.