Mining Association Rules from Semantic Web Data without User Intervention

Document Type : Research Article

Authors

1 Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Iran.

2 School of Computing, Science and Engineering, University of Salford, Manchester, UK.

Abstract

With the introduction and standardization of the semantic web as the third generation of the web, this technology has attracted and received more human attention than ever. Thus, the amount of semantic web data is continuously growing, which makes them a rich source of useful data for data mining techniques. Semantic web data have some complexities, such as the heterogeneous structure of data, the lack of well-defined transactions, and the existence of typed relations between items. In this paper, a new technique named SWApriori is presented, which by using both entities and relations in the extraction of frequent itemsets, generates a new class of association rules (ARs) from semantic web data. The proposed technique by considering the complex heterogeneous nature of semantic web data, without any need to a domain expert, and without any data conversion to transactional data format extracts ARs from semantic web data directly. For evaluation, the proposed technique is applied to Factbook and DBPedia datasets. The experimental results demonstrate the ability of the proposed technique in extracting relational ARs from semantic web data by considering the mentioned challenges. Supplementary experiments show that the proposed technique can extract interesting patterns that are not discoverable by state-of-the-art association rule mining techniques.

Keywords

Main Subjects

References

[1] C. Bobed, P. Maillot, P. Cellier, and S. Ferré. Data-driven assessment of structural evolution of RDF graphs. Semantic Web, page 1–23, April 2020. [ bib | DOI | http ]
[2] P. Ristoski. Exploiting semantic web knowledge graphs in data mining. IOS Press, 2019. [ bib ]
[3] X. Liu, K. Zhai, and W. Pedrycz. An improved association rules mining method. Expert Systems with Applications, 39(1):1362--1374, 2012. [ bib | DOI | http ]
[4] K. Yan, W. Cui, and T. Zhao. Frequent Pattern-based Graph Exploration. In Proceedings of the 12th International Symposium on Visual Information Communication and Interaction. ACM Press, 2019. [ bib | DOI | http ]
[5] C.S.R. Prabhu, A. S. Chivukula, A. Mogadala, R. Ghosh, and L.M. Jenila Livingston. Social Semantic Web Mining and Big Data Analytics. In Big Data Analytics: Systems, Algorithms, Applications, pages 217--231. Springer Singapore, 2019. [ bib | DOI | http ]
[6] Chengqi Zhang and Shichao Zhang. Association rule mining: models and algorithms. Springer, 2003. [ bib ]
[7] T. Herawan and M. M. Deris. A soft set approach for association rules mining. Knowledge-Based Systems, 24(1):186--195, February 2011. [ bib | DOI | http ]
[8] G. Barisevičius, M. Coste, D. Geleta, D. Juric, M. Khodadadi, G. Stoilos, and I. Zaihrayeu. Supporting Digital Healthcare Services Using Semantic Web Technologies. In Lecture Notes in Computer Science, pages 291--306. Springer International Publishing, 2018. [ bib | DOI | http ]
[9] T. Osadchiy, I. Poliakov, P. Olivier, M. Rowland, and E. Foster. Recommender system based on pairwise association rules. Expert Systems with Applications, 115:535--542, January 2019. [ bib | DOI | http ]
[10] G. F. Pelap, C. F. Zucker, F. Gandon, and L. Polese. Web Semantic Technologies in Web Based Educational System Integration. In Lecture Notes in Business Information Processing, pages 170--194. Springer International Publishing, 2019. [ bib | DOI | http ]
[11] M. A. Valle, G. A. Ruz, and R. Morrás. Market basket analysis: Complementing association rules with minimum spanning trees. Expert Systems with Applications, 97:146--162, May 2018. [ bib | DOI | http ]
[12] H. Paulheim. Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8(3):489–508, December 2016. [ bib | DOI | http ]
[13] S. Muggleton and L. d. Raedt. Inductive Logic Programming: Theory and methods. The Journal of Logic Programming, 19-20:629--679, 1994. [ bib | DOI | http ]
[14] L. A. Galárraga, C. Teflioudi, K. Hose, and F. Suchanek. AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In Proceedings of the 22nd international conference on World Wide Web. ACM Press, 2013. [ bib | DOI | http ]
[15] L. Galárraga, C. Teflioudi, K. Hose, and F. M. Suchanek. Fast rule mining in ontological knowledge bases with AMIE+. The VLDB Journal, 24(6):707--730, July 2015. [ bib | DOI | http ]
[16] B. T. Luong, S. Ruggieri, and F. Turini. Classification Rule Mining Supported by Ontology for Discrimination Discovery. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. [ bib | DOI | http ]
[17] S. Vojíř, V. Zeman, J. Kuchař, and T. Kliegr. EasyMiner.eu: Web framework for interpretable machine learning based on rules and frequent itemsets. Knowledge-Based Systems, 150:111--115, June 2018. [ bib | DOI | http ]
[18] J. S. Hong. A Methodology for Searching Frequent Pattern Using Graph-Mining Technique. Journal of Information Technology Applications and Management, 26(1):65--75, 2019. [ bib ]
[19] A. V. V. Rao and B. E. Rambabu. Association rule mining using FPTree as directed acyclic graph. In IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM-2012), pages 202--207. IEEE, 2012. [ bib ]
[20] V. Nebot and R. Berlanga. Finding association rules in semantic web data. Knowledge-Based Systems, 25(1):51--62, February 2012. [ bib | DOI | http ]
[21] W.X. Wilcke, V. d. Boer, M.T.M. d. Kleijn, F.A.H. v. Harmelen, and H.J. Scholten. User-centric pattern mining on knowledge graphs: An archaeological case study. Journal of Web Semantics, 59:100486, 2019. [ bib | DOI | http ]
[22] A. S. Heydari Yazdi and M. Kahani. A novel model for mining association rules from semantic web data. In 2014 Iranian Conference on Intelligent Systems (ICIS). IEEE, February 2014. [ bib | DOI | http ]
[23] R. Ramezani, M. Saraee, and M. A. Nematbakhsh. MRAR: mining multi-relation association rules. Journal of Computing and Security, 1(2):133--158, 2014. [ bib ]
[24] Z. Abedjan and F. Naumann. Improving RDF Data Through Association Rule Mining. Datenbank-Spektrum, 13(2):111--120, 2013. [ bib | DOI | http ]
[25] E. Bytyçi, L. Ahmedi, and F. A. Lisi. Enrichment of association rules through exploitation of ontology properties healthcare case study. Procedia Computer Science, 113:360--367, 2017. [ bib | DOI | http ]
[26] V. Narasimha, P. Kappara, R. Ichise, and O. Vyas. Liddm: A data mining system for linked data. In Workshop on Linked Data on the Web. CEUR Workshop Proceedings, page 108, 2011. [ bib ]
[27] Christian Bizer, Tom Heath, and Tim Berners-Lee. Linked data: The story so far. In Semantic services, interoperability and web applications: emerging concepts, pages 205--227. IGI Global, 2011. [ bib | DOI ]
[28] C. Bizer, T. Heath, and T. Berners-Lee. Linked Data - The Story So Far. International Journal on Semantic Web and Information Systems, 5(3):1--22, July 2009. [ bib | DOI | http ]
[29] R. Ramezani, M. Saraee, and M. A. Nematbakhsh. Finding association rules in linked data, a centralization approach. In 2013 21st Iranian Conference on Electrical Engineering (ICEE). IEEE, May 2013. [ bib | DOI | http ]
[30] M. A. Khan, G. A. Grimnes, and A. Dengel. Two pre-processing operators for improved learning from semanticweb data. In First RapidMiner Community Meeting And Conference (RCOMM 2010), 2010. [ bib ]
[31] C. Kiefer, A. Bernstein, and A. Locher. Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods. In Lecture Notes in Computer Science, pages 478--492. Springer Berlin Heidelberg, 2008. [ bib | DOI | http ]
[32] P. S.M Tsai and C. Chen. Mining interesting association rules from customer databases and transaction databases. Information Systems, 29(8):685--696, December 2004. [ bib | DOI | http ]
[33] A. Patel and S. Jain. Present and future of semantic web technologies: a research statement. International Journal of Computers and Applications, pages 1--10, January 2019. [ bib | DOI | http ]
[34] M. Barati, Q. Bai, and Q. Liu. Mining semantic association rules from RDF data. Knowledge-Based Systems, 133:183--196, 2017. [ bib | DOI | http ]
Journal of Computing and Security
Volume 7, Issue 1
January 2020
Pages 81-94

Files

History

  • Receive Date: 22 February 2020
  • Revise Date: 15 May 2020
  • Accept Date: 07 June 2020

Share

How to cite

Statistics