Ontology-driven Event Type Classification in Images
This addresses the need for better semantic search and fact validation in news by improving image-based event classification, though it appears to be an incremental advance over existing limited approaches.
The paper tackles the problem of classifying event types in images for news applications by creating a comprehensive event ontology from Wikidata and introducing a novel large-scale dataset through web crawling. Their ontology-driven learning approach using knowledge graphs and deep neural networks demonstrates superiority over baselines on benchmark datasets.
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.