Capturing evolving patterns for ontology-based Web mining

Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004

Volumn , Issue , 2004, Pages 256-263

  Li, Yuefeng ^a   Zhong, Ning ^b  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b MAEBASHI INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Data mining; Data reasoning; Ontology learning; Ontology based Web mining; Pattern evolution

Indexed keywords

ALGORITHMS; DATA MINING; DATA STRUCTURES; MATHEMATICAL MODELS; SEMANTICS; SET THEORY; USER INTERFACES; WORLD WIDE WEB; XML;

DATA REASONING; ONTOLOGY LEARNING; ONTOLOGY-BASED WEB MINING; PATTERN EVOLUTION;

PATTERN RECOGNITION;

EID: 15544389848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (37)

1. nd IEEE International Conference on Data Mining, Japan, 2002, 19-26.
2. Y. Bi, T. Anderson, S. McClcan, A rough set model with ontologies for discovering maximal association rules in document collection, Knowledge-Based Systems, 2003, 16: 243-251.
3. G. Chang, M.J. Healey, J. A. M. McHugh, and J. T. L. Wang, Mining the World Wide Web: an information search approach, Kluwer Academic Publishers, 2001.
4. th International Conference on Distributed Computing Systems, 1996, Hong Kong, 385-392.
5. nd ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, 1996, 343-346.
6. R. Feldman, I. Dagen, and H. Hirsh, Mining text using keywords distributions, Journal of Intelligent Information Systems, 1998, 10(3): 281-300.
7. R. Feldman, M. Fresko, Y. Kinar, Y. Lindell, O. Liphstat, M. Rajman, Y. Schler, and O. Zamir, Text mining at the term level, Lecture Notes in AI 1510: Principle of data mining and knowledge discovery, Springer-Verlag, 1998, 65-73.
8. M. N. Garofalakis, R. Rastogi, S. Seshadri, and K. Shim, Data mining and the Web: past, present and future, WIDM99 conference, 1999, Kansas City, Missouri, 43-47.
9. D. A. Grossman and O. Frieder, Information retrieval algorithms and heuristics, Kluwer Academic Publishers, Boston, 1998.
10. rd IEEE International Conference on Data Mining, 2003, Melbourne, Florida, USA, 529-532.
11. rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2000, 176-183.
12. J. D. Holt and S. M. Chung, Multipass algorithms for mining association rules in text databases, Knowledge and Information Systems, 2001, 3: 168-183.
13. J. M. Kleinberg, Authoritative sources in a hyperlinked environment, Annual ACM-SIAM Symposium on Discrete Algorithms, 1998, New York, 668-677.
14. th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, China, 2003, 524-532.
15. Y. Li, C. Zhang, and J. R. Swan, An information filtering model on the Web and its application in JobAgent, Knowledge-based Systems, 2000, 13(5): 285-296.
16. Y. Li and N. Zhong, Web mining model and its applications on information gathering, to appear in Knowledge-Based Systems, 2004.
17. Y. Li and N. Zhong, Ontology-based Web Mining Model: representations of user profiles, IEEE/WIC International Conference on Web Intelligence, 2003, 96-103
18. rd IEEE International Conference on Data Mining, 2003, Melbourne, Florida, USA, 593-596.
19. rd IEEE International Conference on Data Mining, Melbourne, Florida, USA, 2003, pp. 179-186.
20. th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, 2001.
21. Z. Y. Lu, Y.Y. Yao, and N. Zhong, Web log mining, in: N. Zhong, J. Liu and Y. Y. Yao (eds.), ì Web Intelligenceî, Springer-Verlag, 2003, 174-194.
22. st International Conference on Data Warehousing and Knowledge Discovery, 1999, 303-312.
23. A. Maedche, Ontology learning for the semantic Web, Kluwer Academic Publishers, 2003.
24. A. Maedche, V. Pekar, and S. Staab, Ontology learning part one ñ on discovering taxonomic relations from the Web, in: N. Zhong, J. Liu and Y. Y. Yao (eds.), ì Web Intelligenceî;, Springer-Verlag, 2003, 302-319.
25. nd International Conference on Knowledge Discovery and Data Mining, 1996, Portland, Oregon, 146-151.
26. H. Motoda, Active mining, No. 79 in Frontiers in Artificial Intelligence and Application, IOS Press, 2002.
27. S. K. Pal and V. Talwar, Web mining in soft computing framework: relevance, state of the art and future directions, IEEE Transactions on Neural Networks, 2002, 13(5): 1163-11.77.
28. Z. Pawlak, Rough sets and intelligent data analysis, International J. of Information and Sciences, 2002, 147: 1-12.
29. M. Perkowitz, O. Etzioni, Adaptive Web sites, Communications of the ACM, 2000, 43(8): 152-158.
30. S. Schocken and R. A. Hummel, On the use of the Dempster Shafer model in information indexing and retrieval applications, Int. J. Man-Machine Studies, 1993, 39: 843-879.
31. P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, 1994, 66: 191-234.
32. J. Srivastava, R. Cooley, M. Deshpande, and P.ñN. Tan, Web usage mining: Discovery and applications of usage pattern from Web data, SIGKDD Explorations, 2000, 1(2):1-12.
33. rd IEEE International Conference on Data Mining, 2003, Melbourne, Florida, USA, 339-346.
34. C. Welty and N. Guarino, Supporting ontological analysis of taxonomic relationships, Data & Knowledge Engineering, 2001, 39: 51-74.
35. th International World Wide Web Conference, France, 1996.
36. th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, 2001, 473-478.
37. N. Zhong, Representation and construction of ontologies for web intelligence, International Journal of Foundation of Computer Science, 2002, 13(4): 555-570.