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Event Extraction for Collective Knowledge in Multimedia Digital EcoSystem

Abstract : Emerging technologies like Internet and Web services enable a new form of collaboration. An Internet based collaborative environment allows actors with similar profile or interest to publish and share multimedia content. This results in the availability of massive, distributed, heterogeneous and fast-moving streamed multimedia content. In addition, most of the data in the Web describes events associated to people, activities and locations. An event describing a situation might be initiated by a user, followed by a number of users within a specified time period. Extracting events from user contributed multimedia content in collaborative environments is challenging due to three reasons: 1) the content is heterogeneous in source, size and format; 2) users might use different vocabulary to describe the same event; 3) the collaboration environment contains non-events. In this paper, we have proposed an event extraction approach that will be used to build an event-based collective knowledge management framework that assists the retrieval of multimedia contents from various social media sources. This approach is accompanied by experimental results with future works.
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Contributor : Julien Rabaud <>
Submitted on : Monday, October 29, 2018 - 5:44:20 PM
Last modification on : Thursday, March 5, 2020 - 7:12:28 PM




Minale Ashagrie Abebe, Fekade Getahun, Solomon Asres, Richard Chbeir. Event Extraction for Collective Knowledge in Multimedia Digital EcoSystem. Proceedings of the 2015 12th Ieee Africon International Conference - Green Innovation for African Renaissance (Africon), 2015, New York, United States. ⟨10.1109/AFRCON.2015.7331984⟩. ⟨hal-01908074⟩



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