bbbbb
published 
DOI

http://dx.doi.org/10.25098/4.1.22

Issue 
Vol 4- Issue 1

JUNE 2020


Integration of OWL data with Apriori Algorithm

Payman Othman Rahim1, Wria Mohammed Salih Mohammed2

1,2Department of Computer Science, College of Science, University of Slemani, Slemani, Iraq

[email protected]1

 

Received : 15-6-2019                                       Revised:19-8-2019

Accepted :  1-9-2019                                    Published :30-6-2020

Abstract:

Ontology data is one of the well-known dataset on web and it is the main dataset of semantic web. Apriori algorithm is one of the best-known algorithms of association rule mining. The result of ontology will be effective by using association rule mining with it. Ontology is rich sources of data to feed Association rule mining algorithms. This paper focuses on how Ontology and data mining can combine. First of all, the semantic web data (ontology, RDF, RDFS or RDFa) need to be prepare and valid. Then, from the semantic web data, traditional dataset will be extracted from ontology using SPARQL as a query language for semantic web. Next, we are going to mining the OWL data using one of the association rule mining (ARM) algorithm. Apriori is one of association rule mining algorithm, which can use to mine frequent item-sets. Furthermore, the proposed model transforms semantic web to traditional dataset using SPARQL query language. Then, to obtain important information from the traditional dataset, therefore new relationships produced between semantic web and data mining, will be generated.

Keywords: Ontology, Semantic web, Association rule mining, Aprioir, SPARQL.

Bibliography:

AbedjanEmail, Z., & Naumann, F. (2013, July). Improving RDF Data Through Association Rule Mining. Datenbank Spektrum, 13(2).

Aggarwal, C. C., & Han, J. (2014). Frequent Pattern Mining. Springer.

Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. SIGMOD ’93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data . 22, pp. 207-216. IBM Almaden Research Center.

Baratia, M., Baia, Q., & Liub, Q. (2017, October 1). Mining semantic association rules from RDF data . 133, 183-196 .

Berendt, B., Hotho, A., & Stumme, G. (2002). Towards Semantic Web Mining. International Semantic Web Conference, 264–278.

Borgelt, C. (2012, December). Frequent item set mining . WIREs Data Mining And Knowledge discovery, 2(6), 437–456.

Bringas, P. G., Hameurlain, A., & Quirchmayr, G. (2010). Database and Expert Systems Applications: 21st International Conference. Springer.

Dean Allemang, J. H. (2008). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL . Morgan Kaufmann.

DECKER, S., MELNIK, S., HARMELEN, F. V., FENSEL, D., KLEIN, A. M., BROEKSTRA, J., . . . HORROCKS, I. (2000, September). The Semantic Web: The Roles of XML and RDF. IEEE Internet and Computing, 4(5), 63-74.

Domingue, J., Fensel, D., & Hendler, J. A. (2011). Handbook of Semantic Web Technologies. Springer.

DuCharme, B. (2013). Learning SPARQL: Querying and Updating with SPARQL 1.1 (Second edition ed.). O’Reilly Media.

Gayo, J. E., Prud’hommeaux, E., Boneva, I., & Kontokostas, D. (2018). Validating RDF Data. Morgan & Claypool .

Gayo, o. E., Prud’hommeaux, E., Boneva, I., & Kontokostas, D. (2018). Validating RDF Data (Vol. 7). Morgan & Claypool.

Giri, K. (2011, March). Role of Ontology in Semantic Web. DESIDOC Journal of Library & Information Technology, 31(2), 116-120.

Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques . Waltham, MA: Elsevier Inc.

Hart, G. (2013). Linked Data: A Geographic Perspective . CRC Press.

Hidber, C. (1999, June 3). Online Association Rule Mining . Proceeding SIGMOD ’99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data, 28(2), 145-156 .

Juneja, D. S., & Dixit, A. (2019). Improving Search Results Based on Users’ Browsing Behavior Using Apriori Algorithm. Software Engineering , 73-82.

kaur, M., & Grag, U. (2014, December). ECLAT Algorithm for Frequent Item sets Generation. International Journal of Computer Systems, 1(2).

Kaur, S., & Kaur, B. (2015, May). Semantic Web Mining – A Review. International Journal of Computer Applications, 117(12), 16 – 19.

Koh, Y. S., & Rountree, N. (2010). Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection . SCOPUS.

Lacy, L. W. (2006). Owl: Representing Information Using the Web Ontology Language . Victoria, Canada: Trafford Publishing.

Luo, Q. (2003). Advancing Computing, Communication, Control and Management . Spinger.

Maedche, A., & Staab, S. (2001). Ontology learning for the Semantic Web. 16(2).

Maimon, O., & Rokach, L. (2005). Data Mining and Knowledge Discovery Handbook . springer.

Mohammed, W. M. (2016). Mining XML data using K-means and Manhattan. 7(7).

Mohammed, W. M., & Saraee, M. M. (2016). Mining Semantic Web Data Using K-means Clustering Algorithm. British Journal of Mathematics & Computer Science, 13(1), 1-14.

Mohammed, W. M., & Saraee2, M. M. (2016). Mining Semantic Web Data Using K-means Clustering. British Journal of Mathematics & Computer Science, 1-14.

Mohammed, W., & Saraee, M. M. (2016). Sematic Web Mining Using Fuzzy C-means Algorithm. 16(4), 1-16.

ReynaudEmail, J., Toussaint, Y., & Napoli, A. (2019, May 23). Using Redescriptions and Formal Concept Analysis for Mining Definitions in Linked Data. International Conference on Formal Concept Analysis, 11511, 241-256.

Shimada, K., Hirasawa, K., & Hu, J. (2006). Association Rule Mining with Chi-Squared Test Using Alternate Genetic Network Programming . Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining, 202-216.

Shukla, A., Akanksha, & Yadav, P. (2013). International Journal on Recent and Innovation Trends in Computing and Communication , 1(12), 919 – 922.

Singh, J., Ram, H., & Sodhi, D. J. (2013, January). Improving Efficiency of Apriori Algorithm Using Transaction Reduction. International Journal of Scientific and Research Publications, 3(1).

Singh, S., & Aswal, M. S. (2018). Semantic Web Mining: Survey and Analysis. Journal of Web Engineering & Technology, 5(3), 20-31.

Uschold, M. (2018). Demystifying OWL for the Enterprise . Morgan & Claypool Publishers .

Venkata, N. p., Kappara, P., & Ichise, R. (2011). LiDDM: A Data Mining System for Linked Data . Workshop on Linked Data on the Web, 813.