Details

Autor(en) / Beteiligte

• Sherif, Mohamed Ahmed Mohamed
• Morim da Silva, Ana Alexandra
• Pestryakova, Svetlana
• Ahmed, Abdullah Fathi
• Niemann, Sven
• Ngonga Ngomo, Axel-Cyrille

Titel

IngridKG : a FAIR knowledge graph of graffiti

Ist Teil von

• Scientific Data, 10 (2023), 318, Seiten 1-12 (Alle Bände)

Ort / Verlag

Paderborn : Universitätsbibliothek

Erscheinungsjahr

2023

Link zu anderen Inhalten

• Volltext
• Volltext
• Volltext

Beschreibungen/Notizen

• Open Access
• eng: 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.