Abstract
The semantic web uses the domains ontologies related to different topics on the web. Its potential is making the data on the web understandable by the machine and automatically treatable by algorithms without explicit human intervention. In this context, this paper proposes a semantic approach through a generic and intelligent framework to respond to different analytical needs applicable to Online Social Networks (OSN) data. This semantic approach consists in reusing and aligning with the standard ontologies, recommended by the W3C consortium, to formalize and synthetize OSN data, exploiting the ontologies inference potential, to calculate and represent useful indicators for OSN different analytical needs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bachimont, B.: Engagement sémantique et engagement ontologique: conception et réalisation d’ontologies en ingénierie des connaissances. In: Charlet, J., Zacklad, M., Kassel, G., Bourigault, D. (eds.) Ingénierie des connaissances: évolutions récentes et nouveaux défis (p. chapitre 19). Eyrolles, Paris (2000)
Chaffey, D.: Global social media research summary 2017, 27 February 2017. Smart Insights. http://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/
Fürst, F.: L’ingénierie ontologique. Institut de Recherche en Informatique de Nantes. Université de Nantes (2002)
Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 211–220 (2009)
Gómez-Pérez, A., Fernández-López, M., Corch, O.: Methodologies and methods for building ontologies. In: Gómez-Pérez, A., Fernández-López, M., Corch, O. (eds.) Ontological Engineering, pp. 107–197. Springer, London (2004)
Grüninger, M., Fox, M.S.: Methodology for the design and evaluation of ontologies. In: Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI 1995, pp. 6.1–6.10, 13 April 1995
Hawke, S., Horridge, M., Parsia, B., Schneider, M.: OWL 2 web ontology language: W3C recommendation, 11 December 2012. W3C. https://www.w3.org/TR/owl2-overview/
Iqbal, R., Murad, M.A., Mustapha, A., Sharef, N.M.: An analysis of ontology engineering methodologies: a literature review. Res. J. Appl. Sci. Eng. Technol. 6(16), 2993–3000 (2013)
Kong, X., Zhang, J., Yu, P.S.: Inferring anchor links across multiple heterogeneous social networks. In: Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management, pp. 179–188 (2013)
Kosinskia, M., Stillwella, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 110(15), 5802–5805 (2013)
Kotis, K., Vouros, G.A.: Human-centered ontology engineering: the HCOME methodology. Knowl. Inf. Syst. 10(1), 109–131 (2006)
Lim, B.H., Lu, D., Chen, T., Kan, M.-Y.: #mytweet via Instagram: exploring user behaviour across multiple social networks. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, pp. 113–120, 25–28 August 2015
Lima, J.F., Amaral, C.M., Molinaro, L.F.: Ontology: an analysis of the literature. In: Proceedings of the International Conference, CENTERIS 2010 - Part II, pp. 426–435, 20–22 October 2010
Lister, M.: 40 essential social media marketing statistics for 2017, 20 January 2017. Word Stream. http://www.wordstream.com/blog/ws/2017/01/05/social-media-marketing-statistics
Musen, M.A.: The protégé project: a look back and a look forward. AI Matters 1(4), 4–12 (2015)
Nicola, A.D., Missikoff, M.: A lightweight methodology for rapid ontology engineering. Mag. Commun. ACM 59(3), 79–86 (2016)
Roberts, P.: Social media statistics for 2017 (2016). Our Social Times. http://oursocialtimes.com/7-social-media-statistics-for-2017/
Sawsaa, A.F.: Methodology of creating ontology of information science (OIS). In: Lu, J., Xu, Q. (eds.) Ontologies and Big Data Considerations for Effective Intelligence, pp. 435–442. IGI Global, Hershey (2017)
Stadlhofer, B., Salhofer, P., Durlacher, A.: An overview of ontology engineering methodologies in the context of public administration. In: The Proceedings of the Seventh International Conference on Advances in Semantic Processing, SEMAPRO 2013, pp. 36–42, 09 October–29 March 2013
Uschold, M., Grüninger, M.: Ontologies: principies, methods an applications. Knowl. Eng. Rev. 11(2), 93–155 (1996)
Zhang, J., Kong, X., Yu, P.: Predicting social links for new users across aligned heterogeneous social networks. In: Proceeding of 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1289–1294, 7–10 December 2013
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
El Kassiri, A., Belouadha, FZ. (2018). A Semantic Approach Towards Online Social Networks Multi-aspects Analysis. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2017. Advances in Intelligent Systems and Computing, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-319-76354-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-76354-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76353-8
Online ISBN: 978-3-319-76354-5
eBook Packages: EngineeringEngineering (R0)