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|Year=2018
 
|Year=2018
 
|Month=September
 
|Month=September
|Howpublished=https://arxiv.org/abs/1809.11099
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|Howpublished=arXiv
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|Note=https://arxiv.org/abs/1809.11099
 
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{{Publikation Details
 
{{Publikation Details
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|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.
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|Download=Which_KG_arXiv2018.pdf
 
|Link=https://arxiv.org/abs/1809.11099
 
|Link=https://arxiv.org/abs/1809.11099
|Projekt=ProData
 
 
|Forschungsgruppe=Web Science
 
|Forschungsgruppe=Web Science
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{{Forschungsgebiet Auswahl
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|Forschungsgebiet=Semantic Web
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{{Forschungsgebiet Auswahl
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|Forschungsgebiet=Ontologiemodellierung
 
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Aktuelle Version vom 17. November 2019, 17:09 Uhr


Which Knowledge Graph Is Best for Me?




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



Forschungsgruppe

Web Science


Forschungsgebiet

Ontologiemodellierung, Semantic Web