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• W3102381947 abstract "Event classification can add valuable information for semantic search and the increasingly important topic of fact validation in news. So far, only few approaches address image classification for newsworthy event types such as natural disasters, sports events, or elections. Previous work distinguishes only between a limited number of event types and relies on rather small datasets for training. In this paper, we present a novel ontology-driven approach for the classification of event types in images. We leverage a large number of real-world news events to pursue two objectives: First, we create an ontology based on Wikidata comprising the majority of event types. Second, we introduce a novel large-scale dataset that was acquired through Web crawling. Several baselines are proposed including an ontology-driven learning approach that aims to exploit structured information of a knowledge graph to learn relevant event relations using deep neural networks. Experimental results on existing as well as novel benchmark datasets demonstrate the superiority of the proposed ontology-driven approach." @default.
• W3102381947 created "2020-11-23" @default.
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• W3102381947 date "2020-11-09" @default.
• W3102381947 modified "2023-10-01" @default.
• W3102381947 title "Ontology-driven Event Type Classification in Images" @default.
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• W3102381947 hasPublicationYear "2020" @default.
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