"QLever is the fastest engine overall, but is slower for distinct answers. Virtuoso is fast but diverges the most by far mostly due to several known causes. MillenniumDB and Blazegraph are the slowest. MillenniumDB is fast on simple queries, but slow on complex queries." https://ceur-ws.org/Vol-4108/paper3.pdf
"QLever is the fastest engine overall, but is slower for distinct answers. Virtuoso is fast but diverges the most by far mostly due to several known causes. MillenniumDB and Blazegraph are the slowest. MillenniumDB is fast on simple queries, but slow on complex queries." https://ceur-ws.org/Vol-4108/paper3.pdf
#ClimateKG - the #IPCC #AR6 reports are a pretty big beast. Authored over 7 years they are the ultimate #statusupdate of planet earth. Quant data: https://doi.org/10.5281/zenodo.17521936 - Reports 7, Pages 10,047, Words 8,047,000, Citations 48,400, Data 66,834, Figures 1,672, Authors 1,106, Glossary 925, Acronyms 3,041, Lang 5+. The entity relationship model is need to map the parts of the reports, relations, then to allow markup of entities, concepts, etc
#ClimateKG will be used for the #IPCC #AR6 corpus of 7 main reports of 10,000 pages. For corpus browsing, republishing, community enrichment. The constructed knowledge graph in Wikibase/MediaWiki allows browsing using the familiar #MediaWiki interface with #Wikidata enhancements like infoboxes, and #scholia like interfaces https://scholia.toolforge.org/ | Republishing is intended for sharing or reviewing search results on climate topics | Enrichment is for data scientists to make use of reports >>>
it takes quite a bit of effort to update all the SPARQL queries (we have more than 300) to accommodate for the RDF graph split of @wikidata
Want to help? Check out https://www.wikidata.org/wiki/Wikidata:Scholia/Events/2025_11
it takes quite a bit of effort to update all the SPARQL queries (we have more than 300) to accommodate for the RDF graph split of @wikidata
Want to help? Check out https://www.wikidata.org/wiki/Wikidata:Scholia/Events/2025_11