Mining Multirelation Association Rules on the Web of Data
DOI:
https://doi.org/10.5753/isys.2020.830Keywords:
Multirelation Association Rule Mining, Web of Data, Graph Mining, Data MiningAbstract
The Web of Data is a growing and relevant data source that contains information distributed in interconnected datasets. Most data mining algorithms were designed to analyze a single dataset at a time and, hence, cannot explore the connections between the datasets in the Web of Data. To overcome this limitation, the present work proposes MRAR+, a graph mining method that searches for multirelation association rules in order to identify new and useful knowledge involving resources of multiple datasets connected to the Web of Data. To illustrate MRAR+'s feasibility, this paper reports on two experiments where the proposed method mined different datasets interconnected in the Web of Data and produced new and useful rules for the users.
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References
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Agrawal, R., Imieli´nski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. SIGMOD Rec., 22(2):207–216.
Ait-Mlouk, A., Jiang, L., and Vu, X.-S. (2019). Improving rdf data through semantic association rules mining. In 31st Sweedish AI Society Workshop (SAIS 2019).
Barati, M. (2019). Automated Knowledge Enrichment for SemanticWeb Data. PhD thesis, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
Barati, M., Bai, Q., and Liu, Q. (2017). Mining semantic association rules from RDF data. Knowl.-Based Syst., 133:183–196.
Basse, A., Gandon, F., Mirbel, I., Lo, M., Mirbel, I., and Fr (2010). Dfs-based frequent graph pattern extraction to characterize the content of rdf triple stores.
Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The semantic web. Scientific american, 284(5):34–43.
Bizer, C., Heath, T., and Berners-Lee, T. (2009). Linked data-the story so far. Int. journal
on Semantic Web and Information Systems, 5(3):1–22.
Bizer, C., Heath, T., Idehen, K., and Berners-Lee, T. (2008). Linked data on the web (ldow2008). In Proceedings of the 17th Int. Conf. on World Wide Web, WWW ’08, pages 1265–1266, New York, NY, USA. ACM.
Bytyc¸i, E., Ahmedi, L., and Kurti, A. (2016). Association rule mining with context ontologies: An application to mobile sensing of water quality. In Garoufallou, E., Subirats Coll, I., Stellato, A., and Greenberg, J., editors, Metadata and Semantics Research, pages 67–78, Cham. Springer International Publishing.
de Oliveira, F. A., Costa, R. L., Goldschmidt, R. R., and Cavalcanti, M. C. (2019). Multirelation association rule mining on datasets of the web of data. In Proceedings of the XV Brazilian Symposium on Information Systems, SBSI 2019, Aracaju, Brazil, May 20-24, 2019, pages 61:1–61:8.
Elseidy, M., Abdelhamid, E., Skiadopoulos, S., and Kalnis, P. (2014). Grami: Frequent subgraph and pattern mining in a single large graph. volume 7, pages 517–528.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3):37.
Goldschmidt, R., Bezerra, E., and Passos, E. (2015). Data Mining: Conceitos, técnicas, algoritmos, orientações e aplicações. Rio de Janeiro: Elsevier.
Hendrickx, T., Cule, B., Meysman, P., Naulaerts, S., Laukens, K., Goethals, B.”, e. T., Lim, E., Zhou, Z., Ho, T., Cheung, D., and Motoda, H. (2015). Mining Association Rules in Graphs Based on Frequent Cohesive Itemsets, pages 637–648. Springer Int. Publishing, Cham.
Natali, V. and Alfadian, P. (2019). Analisis dan perancangan domain specific language untuk data generator pada relational database. JUMANJI (Jurnal Masyarakat Informatika Unjani), 3(01):64–73.
Oliveira, F. A. (2018). Mineração de regras de associação de multirrelação em datasets na web de dados. Master’s thesis, Instituto Militar de Engenharia(IME), Rio de Janeiro.
Oliveira, F. A., Costa, R. L., Goldschmidt, R. R., and Cavalcanti, M. C. (2017). Mineração de regras de associação multirrelação em grafos: Direcionando o processo de busca. In Simpósio Brasileiro de Banco de Dados (SBBD’17), pages 270–275.
Pickler, M. E. V. (2007). Web semântica: ontologias como ferramentas de representação do conhecimento. Perspectivas em Ciência da Inf., 12(1):65–83.
Raad, J., Beek, W., van Harmelen, F., Pernelle, N., and Saïs, F. (2018). Detecting erroneous identity links on the web using network metrics. In Vrandeˇci´c, D., Bontcheva, K., Suárez-Figueroa, M. C., Presutti, V., Celino, I., Sabou, M., Kaffee, L.-A., and Simperl, E., editors, The Semantic Web – ISWC 2018, pages 391–407, Cham. Springer International Publishing.
Ramezani, R., Saraee, M., and Nematbakhsh, M. A. (2014). MRAR : Mining Multi-Relation Association Rules. J. of Computing and Security, 1(2):133–158.
Rehman, S. U., Khan, A. U., and Fong, S. (2012). Graph mining: A survey of graph mining techniques. In Seventh International Conference on Digital Information Management (ICDIM 2012), pages 88–92.
Sydow, M., Pikuła, M., and Schenkel, R. (2013). The notion of diversity in graphical entity summarisation on semantic knowledge graphs. J Intell Inf Syst, 41:109–149.
Tavares, A., Oliveira, H., and Lóscio, B. (2015). Rdfmat–um serviço para criação de repositórios de dados rdf a partir de crawling na web de dados. Revista da Escola Regional de Informática, 1(1):6.
Vieira, M. R., FIGUEIREDO, J. M. d., Liberatti, G., and Viebrantz, A. F. M. (2012). Bancos de dados nosql: conceitos, ferramentas, linguagens e estudos de casos no contexto de big data. Simpósio Brasileiro de Bancos de Dados, 27:1–30. 27/12/2017.
Zhang, X., Zhao, C., Wang, P., and Zhou, F. (2012). Mining link patterns in linked data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7418 LNCS:83–94. cited By 10.
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Published
2020-07-31
How to Cite
de Oliveira, F. A., Villote, G. dos S., Costa, R. L., Goldschmidt, R. R., & Cavalcanti, M. C. (2020). Mining Multirelation Association Rules on the Web of Data. ISys - Brazilian Journal of Information Systems, 13(4), 77–100. https://doi.org/10.5753/isys.2020.830
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