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  4. GeoWINE: Geolocation based Wiki, Image, News and Event Retrieval
 
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2021
Conference Paper
Title

GeoWINE: Geolocation based Wiki, Image, News and Event Retrieval

Abstract
In the context of social media, geolocation inference on news or events has become a very important task. In this paper, we present the GeoWINE (Geolocation-based Wiki-Image-News-Event retrieval) demonstrator, an effective modular system for multimodal retrieval which expects only a single image as input. The GeoWINE system consists of five modules in order to retrieve related information from various sources. The first module is a state-of-the-art model for geolocation estimation of images. The second module performs a geospatial-based query for entity retrieval using the Wikidata knowledge graph. The third module exploits four different image embedding representations, which are used to retrieve most similar entities compared to the input image. The last two modules perform news and even t retrieval from EventRegistry and the Open Event Knowledge Graph (OEKG). GeoWINE provides an intuitive interface for end-users and is insightful for experts for reconfiguration to individual setups. The GeoWINE achieves promising results in entity label prediction for images on Google Landmarks dataset. The demonstrator is publicly available at http://cleopatra.ijs.si/geowine/.
Author(s)
Tahmasebzadeh, Golsa
Kacupaj, Endri
Müller-Budack, Eric
Hakimov, Sherzod
Lehmann, Jens  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Ewerth, Ralph
Mainwork
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval  
Project(s)
CLEOPATRA
Funder
European Commission EC  
Conference
International Conference on Research and Development in Information Retrieval (SIGIR) 2021  
Open Access
DOI
10.1145/3404835.3462786
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • Geolocation Estimation

  • Computer Vision

  • Knowledge Graph

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