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{{Publikation Details | {{Publikation Details | ||
+ | |Abstract=In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively in use, these knowledge graphs are hard to compare against each other in a given setting. Thus, it is a challenge for researchers and developers to pick the best knowledge graph for their individual needs. In our recent survey, we devised and applied data quality criteria to the above-mentioned knowledge graphs. Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting. With this paper we intend to ease the access to our in-depth survey by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. However, this paper does not intend to replace our previously introduced decision-support framework. For an informed decision on which KG is best for you we still refer to our in-depth survey. | ||
+ | |Download=Which_KG_arXiv2018.pdf | ||
|Link=https://arxiv.org/abs/1809.11099 | |Link=https://arxiv.org/abs/1809.11099 | ||
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|Forschungsgruppe=Web Science | |Forschungsgruppe=Web Science | ||
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+ | {{Forschungsgebiet Auswahl | ||
+ | |Forschungsgebiet=Semantic Web | ||
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+ | |Forschungsgebiet=Ontologiemodellierung | ||
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Aktuelle Version vom 17. November 2019, 17:09 Uhr
Veröffentlichung: 2018
September
Art der Veröffentlichung: arXiv
Bemerkung: https://arxiv.org/abs/1809.11099
BibTeX
Kurzfassung
In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively in use, these knowledge graphs are hard to compare against each other in a given setting. Thus, it is a challenge for researchers and developers to pick the best knowledge graph for their individual needs. In our recent survey, we devised and applied data quality criteria to the above-mentioned knowledge graphs. Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting. With this paper we intend to ease the access to our in-depth survey by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. However, this paper does not intend to replace our previously introduced decision-support framework. For an informed decision on which KG is best for you we still refer to our in-depth survey.
Download: Media:Which_KG_arXiv2018.pdf
Weitere Informationen unter: Link
Ontologiemodellierung, Semantic Web