Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Knowledge graph,Data quality,Information retrieval,Computer science,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1809.11099 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Färber | 1 | 56 | 22.11 |
| Achim Rettinger | 2 | 409 | 36.04 |