Ontology-driven Event Type Classification in Images

TL;DR

This paper introduces an ontology-driven method for classifying newsworthy event types in images, utilizing a large-scale dataset and knowledge graph to improve accuracy over existing approaches.

Contribution

It presents a novel ontology-based framework and a large-scale dataset for event classification in images, advancing beyond limited prior work.

Findings

Ontology-driven approach outperforms baselines

Large-scale dataset improves training effectiveness

Knowledge graph integration enhances event relation learning

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.