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. 2023 May 25;10(1):318.
doi: 10.1038/s41597-023-02199-8.

INGRIDKG: A FAIR Knowledge Graph of Graffiti

Mohamed Ahmed Sherif  1 , Ana Alexandra Morim da Silva  2 , Svetlana Pestryakova  2 , Abdullah Fathi Ahmed  3 , Sven Niemann  4 , Axel-Cyrille Ngonga Ngomo  2
Affiliations

INGRIDKG: A FAIR Knowledge Graph of Graffiti

Mohamed Ahmed Sherif et al. Sci Data. .

Abstract

Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (INGRID) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within INGRID, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on INGRID targeted especially by researchers. In particular, we present INGRIDKG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update INGRIDKG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of INGRIDKG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Screenshot of the resource page grfr:64681.
Fig. 2
Fig. 2
Annotations categories.
Fig. 3
Fig. 3
List of all used vocabularies in IngridKG.
Fig. 4
Fig. 4
UML class diagram for the IngridKG ontology.
Fig. 5
Fig. 5
Example graffiti resource.
Fig. 6
Fig. 6
Example of a crew representation.
Fig. 7
Fig. 7
Example of a crew member.
Fig. 8
Fig. 8
Example of near duplicate resources of grfr:64681.
Fig. 9
Fig. 9
How many crews painted each graffiti?
Fig. 10
Fig. 10
In which crews does the crew member “REAL” work?
Fig. 11
Fig. 11
List all graffiti text on the “68159” postal code.
Fig. 12
Fig. 12
Find all graffiti drawn by a crew named “ASKER” and item field contains “!”.
Fig. 13
Fig. 13
Retrieve the count of graffiti per individual technique.
See this image and copyright information in PMC

References

    1. Ngomo, A.-C. N., Auer, S., Lehmann, J. & Zaveri, A. Introduction to linked data and its lifecycle on the web. In Reasoning Web International Summer School, 1–99 (Springer, 2014).
    1. Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Scientific data3 (2016). - PMC - PubMed
    1. Sherif MA, da Silva AAM, Pestryakova S, Ahmed AF, Ngomo A-CN. 2023. IngridKG: A FAIR Knowledge Graph of Graffiti. Zenodo. - DOI - PMC - PubMed
    1. Ngonga Ngomo, A.-C. et al. LIMES - A Framework for Link Discovery on the Semantic Web. KI - Künstliche Intelligenz, German Journal of Artificial Intelligence - Organ des Fachbereichs “Künstliche Intelligenz” der Gesellschaft für Informatik e.V. (2021).
    1. Jain, T., Lennan, C., John, Z. & Tran, D. Imagededup.https://github.com/idealo/imagededup (2019).

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