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International Multi-Conference on Advanced Computer Systems

ACS 2016: Hard and Soft Computing for Artificial Intelligence, Multimedia and Security pp 355–365Cite as

Ontology-Based Approaches to Big Data Analytics

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 534))

Abstract

The access to relevant information is one of the determining factors which directly influences on decision-making processes. Huge amounts of data have been accumulated by entities from large variety of sources in many different formats. Due to large amounts of information and continuous processes of generation of new parts, it is necessary to ensure the most effective way of information or data extraction and analysis. The Web of Data provides great opportunities for ontology-based services. The combination of ontology-based approaches and Big Data may help in solving some problems related to extraction of meaningful information from various sources. This paper presents the selected ontology-based approaches to Big Data analytics as well as a proposal of a procedure for ontology-based knowledge discovery.

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References

  1. Rodríguez-Muro, M., Kontchakov, R., Zakharyaschev, M.: Ontology-based data access: ontop of databases. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 558–573. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41335-3_35

    Google Scholar 

  2. Savo, D.F., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., Romagnoli, V., Ruzzi, M., Stella, G.: MASTRO at work: experiences on ontology-based data access. In: Proceedings of the 23rd International Workshop on Description Logics (DL2010), CEUR WS 573, Waterloo, Canada (2010)

    Google Scholar 

  3. Lembo, D., Mora, J., Rosati, R., Savo, D.F., Thorstensen, E.: Mapping analysis in ontology-based data access: algorithms and complexity. In: Arenas, M., et al. (eds.) ISWC 2015, Part I. LNCS, vol. 9366, pp. 217–234. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25007-6_13

    Chapter  Google Scholar 

  4. Heymans, S., et al.: Ontology reasoning with large data repositories. In: Hepp, M., et al. (eds.) Ontology Management. Computing for Human Experience, vol. 7, pp. 89–128. Springer, US (2008)

    Chapter  Google Scholar 

  5. Konys, A.: Knowledge-based approach to question answering system selection. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015, Part I. LNCS (LNAI), vol. 9329, pp. 361–370. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24069-5_34

    Chapter  Google Scholar 

  6. Ajani, S.: An ontology and semantic metadata based semantic search technique for census domain in a big data context. Int. J. Eng. Res. Technol. 3(2), 1–5 (2014)

    Google Scholar 

  7. Kitchin, R., McArdle, G.: What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data Soc. 3, 1–10 (2016)

    Google Scholar 

  8. Murthy, P., Bharadwaj, A., Subrahmanyam, P.A., et al.: Big Data Taxonomy. Big Data Working Group, Cloud Security Alliance (2014)

    Google Scholar 

  9. Konys, A.: A tool supporting mining based approach selection to automatic ontology construction. IADIS J. Comput. Sci. Inf. Syst., 3–10 (2015)

    Google Scholar 

  10. Hellmann, S., Auer, S.: Towards web-scale collaborative knowledge extraction. In: Gurevych, I., Kim, J. (eds.) The People’s Web Meets NLP, Theory and Applications of Natural Language Processing, pp. 287–313. Springer, Heidelberg (2013)

    Google Scholar 

  11. Unbehauen, J., Hellmann, S., Auer, S., Stadler, C.: Knowledge extraction from structured sources. In: Ceri, S., Brambilla, M. (eds.) Search Computing. LNCS, vol. 7538, pp. 34–52. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34213-4_3

    Chapter  Google Scholar 

  12. Gruninger, M., Obst, L.: Semantic web and big data meets applied ontology. Appl. Ontol. 9, 155–170 (2014)

    Google Scholar 

  13. Kuiler, E.W.: From big data to knowledge: an ontological approach to big data analytics. Rev. Policy Res. 31(4), 311–318 (2014)

    Article  Google Scholar 

  14. Kitchin, R.: The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. Sage, London (2014)

    Book  Google Scholar 

  15. Calvanese, D., et al.: The mastro system for ontology-based data access. Semant. Web J. 2(1), 43–53 (2011)

    Google Scholar 

  16. Kozaki K.: Ontology engineering for big data. In: Ontology and Semantic Web for Big Data (ONSD2013) Workshop in the 2013 International Computer Science and Engineering Conference (ICSEC2013), Bangkok, Thailand (2013)

    Google Scholar 

  17. Gruber, T.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum Comput Stud. 43(5–6), 907–928 (1995)

    Article  Google Scholar 

  18. Tsai, C.W., et al.: Big data analytics: a survey. J. Big Data 2, 21 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, Żołnierska 49, 71-210, Szczecin, Poland

    Agnieszka Konys

Authors
  1. Agnieszka Konys
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Correspondence to Agnieszka Konys .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineeri, Ehime University Graduate School of Science and Engineeri, Ehime, Japan

    Shin-ya Kobayashi

  2. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Andrzej Piegat

  3. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Jerzy Pejaś

  4. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Imed El Fray

  5. Polish Academy of Sciences, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Konys, A. (2017). Ontology-Based Approaches to Big Data Analytics. In: Kobayashi, Sy., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J. (eds) Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. ACS 2016. Advances in Intelligent Systems and Computing, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-48429-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-48429-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48428-0

  • Online ISBN: 978-3-319-48429-7

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