%0 Journal Article %1 nature_sd_ingridkg %A Sherif, Mohamed Ahmed %A da Silva, Ana Alexandra Morim %A Pestryakova, Svetlana %A Ahmed, Abdullah Fathi %A Niemann, Sven %A Ngomo, Axel-Cyrille Ngonga %D 2023 %J Scientific Data %K ahmed amsilva dice ingrid ngonga pestryakova sherif %T IngridKG: A FAIR Knowledge Graph of Graffiti %U https://papers.dice-research.org/2023/NSDJ_IngridKG/public.pdf %X 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.

@article{nature_sd_ingridkg, 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.}, added-at = {2023-05-02T18:40:33.000+0200}, author = {Sherif, Mohamed Ahmed and da Silva, Ana Alexandra Morim and Pestryakova, Svetlana and Ahmed, Abdullah Fathi and Niemann, Sven and Ngomo, Axel-Cyrille Ngonga}, bdsk-url-1 = {https://papers.dice-research.org/2023/NSDJ_IngridKG/public.pdf}, biburl = {https://www.bibsonomy.org/bibtex/2712c6bb2e3d000da2cc3dd074303a260/dice-research}, interhash = {d9a6ab78193da431d75f3dd2991b6058}, intrahash = {712c6bb2e3d000da2cc3dd074303a260}, journal = {Scientific Data}, keywords = {ahmed amsilva dice ingrid ngonga pestryakova sherif}, timestamp = {2026-02-07T09:52:53.000+0100}, title = {{IngridKG: A FAIR Knowledge Graph of Graffiti}}, url = {https://papers.dice-research.org/2023/NSDJ_IngridKG/public.pdf}, year = 2023 }