Classification Analysis in Complex Online Social Networks Using Semantic Web Technologies

The Online Social Networks (OSN) have a positive evolution due to the diversity of social media and the increase in the number of users. The revenue of the social media organizations is generated from the analysis of users' profiles and behaviors, knowing that surfers maintain several accounts on different OSNs. To satisfy its users, the social media organizations have initiated projects for ensuring interoperability to allow for users creating other accounts on other OSN using an initial account, and sharing content from one media to others. Believing that the future generations of Internet will be based on the semantic web technologies, multiple academic and industrial projects have emerged with the objective of modeling semantically the OSNs to ensure interoperability or data aggregation and analysis. In this chapter, we present related works and argue the necessity of a unified semantic model (USM) for OSNs; we introduce a kernel of a USM using standard social ontologies to support the principal social media and it can be extended to support other future social media.

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Enhancing Social Networks with Agent and Semantic Web Technologies

Advances in Human and Social Aspects of Technology - Collaboration and the Semantic Web ◽

10.4018/978-1-4666-0894-8.ch005 ◽

2012 ◽

pp. 83-100 ◽

Cited By ~ 4

Author(s):

Federico Bergenti ◽

Enrico Franchi ◽

Agostino Poggi

Keyword(s):

Social Networks ◽

Semantic Web ◽

Collaborative Work ◽

Multi Agent Systems ◽

Semantic Web Technologies ◽

Agent Based ◽

Web Technologies ◽

Agent Systems ◽

Multi Agent ◽

Relevant Work

In this chapter, the authors describe the relationships between multi-agent systems, social networks, and the Semantic Web within collaborative work; they also review how the integration of multi-agent systems and Semantic Web technologies and techniques can be used to enhance social networks at all scales. The chapter first provides a review of relevant work on the application of agent-based models and abstractions to the key ingredients of our work: collaborative systems, the Semantic Web, and social networks. Then, the chapter discusses the reasons current multi-agent systems and their foreseen evolution might be a fundamental means for the realization of the future Semantic Social Networks. Finally, some conclusions are drawn.

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Towards a Unified Semantic Model for Online Social Networks to Ensure Interoperability and Aggregation for Analysis

Advances in Wireless Technologies and Telecommunication - Graph Theoretic Approaches for Analyzing Large-Scale Social Networks ◽

10.4018/978-1-5225-2814-2.ch016 ◽

2018 ◽

pp. 267-292

Author(s):

Asmae El Kassiri ◽

Fatima-Zahra Belouadha

Keyword(s):

Social Networks ◽

Social Media ◽

Online Social Networks ◽

Semantic Model ◽

Semantic Web Technologies ◽

Web Technologies ◽

Knowing That ◽

The Social ◽

Industrial Projects ◽

Media Organizations

The Online Social Networks (OSN) have a positive evolution due to the diversity of social media and the increase in the number of users. The revenue of the social media organizations is generated from the analysis of users' profiles and behaviors, knowing that surfers maintain several accounts on different OSNs. To satisfy its users, the social media organizations have initiated projects for ensuring interoperability to allow for users creating other accounts on other OSN using an initial account, and sharing content from one media to others. Believing that the future generations of Internet will be based on the semantic web technologies, multiple academic and industrial projects have emerged with the objective of modeling semantically the OSNs to ensure interoperability or data aggregation and analysis. In this chapter, we present related works and argue the necessity of a unified semantic model (USM) for OSNs; we introduce a kernel of a USM using standard social ontologies to support the principal social media and it can be extended to support other future social media.

Download Full-text