Email this article 
Open Access
Subscription or Fee Access
A Novel Approach for Extracting Optimized Association Rules using Knowledge Discovery Algorithm from Semantic Web Data
Abstract
The gradual increase in amount of ontology has given rise to heterogeneous and graph structured data, which is a complex data. This complex data of ontology is mainly represented in the form of RDF or OWL. The ontology can be used to extract useful knowledge about the user or the system using that particular ontology, user’s navigation, personalization, association, decision making, medical diagnosis, etc. The basic aim here, would be to find out efficient methods for improving probably user personalization, decision making and find out the association that is the relation between various entities of a particular domain, with semantic web mining, association rule mining, and other data mining techniques. With the help of KD-SWD algorithm, unique as well as relevant association rules from the semantic web data can be obtained. Relevancy of the algorithm can be achived with the help of user-specified query and other supporting parameters. While the uniqueness will be achieved by eliminating the rules base on subsumption.
Keywords
References
Victoria Nebot,Rafael Berlanga “Finding association rules in semantic web data” knowledge based system, 2012
R. Agrawal, T. Imielminski and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proc. ACM Intern. Conf. on Management of Data, ACM Press, pp. 207-216, June 1993.
Jodi Schneider, Tudor Groza “A Review of Argumentation for the Social Semantic Web” Proc. 25th Canadian Conf. on Artificial Intelligence (AI 2013), IOS Press, pp. 61-73, 2013.
Claudia Marinica and Fabrice Guillet, "Knowledge-Based Interactive Postmining of Association Rules Using Ontologies" IEEE Transactions On Knowledge And Data Engineering, Vol. 22, No. 6, June 2010.
G. Stumme, A. Hotho, B. Berendt , “Semantic Web Mining : State of the art and future directions”, Journal of web semantics, Web Semantics: Science, Services and Agents on the World Wide Web 4, pp.124–143,2006.
Vishal Jain, Dr. Mayank Singh "Ontology Based Information Retrieval in Semantic Web: A Survey" I.J. Information Technology and Computer Science, pp. 62-69, 2013.
Reynold S. Xin , Oktie Hassanzadeh, "Publishing Bibliographic Data on the Semantic Web using BibBase", IOS Press,pg-16-23, 2013.
Leyla Jael Garcia Castro, Rafael Berlanga "Connections across scientific publications based on semantic annotations”, Proceedings of the 3rd Workshop on Semantic Publishing, 26-may-2013.
J. Pei, J. Han, and R. Mao, “Closet: An Efficient Algorithm for Mining Frequent Closed Itemsets,” Proc. ACM SIGMOD Workshop Research Issues in Data Mining and Knowledge Discovery, pp. 21-30, 2000.
D. Burdick, M. Calimlim, J. Flannick, J. Gehrke, and T. Yiu, “Mafia: A Maximal Frequent Itemset Algorithm,” IEEE Transactions, Knowledge and Data Eng., vol. 17, no. 11, pp. 1490-1504, Nov. 2005.
M.J. Zaki and C.J. Hsiao, “Charm: An Efficient Algorithm for Closed Itemset Mining,” Proc. Second SIAM Int’l Conf. Data Mining, pp. 34-43, 2002.
M.J. Zaki, “Generating Non-Redundant Association Rules,” Proc. Int’l Conf. Knowledge Discovery and Data Mining, pp. 34-43, 2000.
N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, “Efficient Mining of Association Rules Using Closed Itemset Lattices,” Information Systems, vol. 24, pp. 25-46, 1999.
M. Hahsler, C. Buchta, and K. Hornik, “Selective Association Rule Generation,” Computational Statistic, vol. 23, no. 2, pp. 303-315, Kluwer Academic Publishers, 2008.
H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hatonen, and H. Mannila, “Pruning and Grouping of Discovered Association Rules,” Proc. ECML-95 Workshop Statistics, Machine Learning, and Knowledge Discovery in Databases, pp. 47-52, 1995.
A. Silberschatz and A. Tuzhilin, “What Makes Patterns Interesting in Knowledge Discovery Systems,” IEEE Trans. Knowledge and Data Eng. vol. 8, no. 6, pp. 970-974, Dec. 1996.
M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo, “Finding Interesting Rules from Large Sets of Discovered Association Rules,” Proc. Int’l Conf. Information and Knowledge Management (CIKM), pp. 401-407, 1994.
T. Imielinski, A. Virmani, and A. Abdulghani, “Datamine: Application Programming Interface and Query Language for Database Mining,” Proc. Int’l Conf. Knowledge Discovery and Data Mining (KDD), pp. 256-262, http://www.aaai.org/Papers/KDD/1996/KDD96-042.pdf, 1996.
A. An, S. Khan, and X. Huang, “Objective and Subjective Algorithms for Grouping Association Rules,” Proc. Third IEEE Int’l Conf. Data Mining (ICDM ’03), pp. 477-480, 2003.
N. Guarino, “Formal Ontology in Information Systems,” Proc. First Int’l Conf. Formal Ontology in Information Systems, pp. 3-15, 1998.
A. Bellandi, B. Furletti, V. Grossi, and A. Romei, “Ontology- Driven Association Rule Extraction: A Case Study,” Proc. Workshop Context and Ontologies: Representation and Reasoning, pp. 1-10, 2007.
Jiawei Han and Micheline Kamber, “Data Mining Concepts Techniiques”,Elsevier, 2011
P. Patel-Schneider, J. Simeon, “Building the semantic Web on XML”, The Semantic Web — ISWC 2002, Lecture Notes in Computer Science ,vol. 2342, pp 147-161, 2002.
P. Buitelaar, P. Cimiano, B. Magnini (Eds.), Ontology Learning from Text: Methods, Evaluation and Applications, Frontiers in Artificial Intelligence and Applications, vol. 123, IOS Press, Amsterdam, 2005.
F. Baader, D. Calvanese, D.L. McGuinness, D. Nardi, P.F. Patel-Schneider (Eds.), The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge University Press, 2003.
C. Kiefer, A. Bernstein, A. Locher, Adding data mining support to SPARQL via statistical relational learning methods, in: S. Bechhofer, M. Hauswirth, J. Hoffmann, M. Koubarakis (Eds.), ESWC, Lecture Notes in Computer Science, vol. 5021, Springer, pp. 478–492, 2008.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.