Semantic approach using unified and summarised ontologies for analysing data from social media

Abstract

Aggregating and analysing web social data is an important and interesting issue having an added value in various domains. Nevertheless, a major challenge to this issue is how to aggregate huge data scattered over a multitude of social media and be able to meet different analysis requirements and objectives such as recommendation, community detection, link prediction and sentiment analysis. In this context, we propose to use a summarised ontology of different inferred metrics that could be mutually reused to perform various analysis processes without redundant computing. According to the continuous evolution of online social networks (OSN), these metrics are dynamically inferred from a unified semantic model that extends standard ontologies used in Social web field. The proposed extension allows representing and aggregating data from a multitude of the most popular OSN.