Linking Open Descriptions of Social Events (LODSE): A New Ontology for Social Event Classification

Authors

Abstract

The digital era has brought a number of significant changes in the world of communications. Although technological evolution has allowed the creation of new social event platforms to disclose events, it is still difficult to know what is happening around a location. Currently, a large number of social events are created and promoted on social networks. With the massive quantity of information created in these systems, finding an event is challenging because sometimes the data is ambiguous or incomplete. One of the main challenges in social event classification is related to the incompleteness and ambiguity of metadata created by users. This paper presents a new ontology, named LODSE (Linking Open Descriptions of Social Events) based on the LODE (Linking Open Descriptions of Events) ontology to describe the domain model of social events. The aim of this ontology is to create a data model that allows definition of the most important properties to describe a social event and to improve the classification of events. The proposed data model is used in an experimental evaluation to compare both ontologies in social event classification. The experimental evaluation, using a dataset based on real data from a popular social network, demonstrated that the data model based on the LODSE ontology brings several benefits in the classification of events. Using the LODSE ontology, the results show an increment of correctly classified events as well as a gain in execution time, when comparing with the data model based on the LODE ontology.

Keywords

social events; social event classification; ontologies; machine learning; random forest.

Subject

social events,data mining, ontologies, machine learning

Journal

Information — Open Access Journal of Information Science, Vol. 9, #164, Prof. Dr. Willy Susilo, July 2018

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DOI