QLever Wikipedia article

[WolfgangFahl]

Just added QLever to https://en.wikipedia.org/wiki/List_of_SPARQL_implementations

but unfortunately there is no proper article:

https://en.wikipedia.org/w/index.php?title=QLever&redirect=no

I did some brainstorming with Claude AI (see below) - this will not suffice yet and needs some love since Wikipedia will not accept AI generated articles

{{Infobox software
| name               = QLever
| logo               = 
| screenshot         = 
| caption            = 
| developer          = University of Freiburg
| released           = {{Start date|2017}}
| latest release version = 
| latest release date    = 
| programming language   = C++
| operating system   = Cross-platform
| platform           = 
| size               = 
| language           = 
| genre              = SPARQL query engine
| license            = {{AGPL}}
| website            = {{URL|https://qlever.cs.uni-freiburg.de/}}
}}

'''QLever''' (pronounced "Clever") is an open-source [[SPARQL]] query engine developed by the University of Freiburg. It is designed for efficient indexing and querying of very large [[knowledge graph]]s, capable of handling over 100 billion triples on a single standard PC or server.<ref name="readme">{{cite web |url=https://github.com/ad-freiburg/qlever/blob/master/README.md |title=QLever README |website=GitHub |access-date=YYYY-MM-DD}}</ref>

==Overview==

QLever is particularly notable for its speed in handling queries that involve large intermediate or final results, which are traditionally challenging for engines like Blazegraph or Virtuoso. It supports both SPARQL queries and text search within the associated knowledge base.<ref name="cikm2017">{{cite conference
  | last1 = Bast
  | first1 = Hannah
  | last2 = Buchhold
  | first2 = Björn
  | title = QLever: A Query Engine for Efficient SPARQL+Text Search
  | booktitle = CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
  | pages = 647–656
  | year = 2017
  | month = November
  | doi = 10.1145/3132847.3132921
  | url = https://doi.org/10.1145/3132847.3132921
  | publisher = ACM
  | access-date = YYYY-MM-DD
}}</ref> The engine also features context-sensitive autocompletion for SPARQL queries, enhancing ease of use.<ref name="cikm2022">{{cite conference |last1=Bast |first1=Hannah |last2=Korfhage |first2=Kenji |title=QLever Autocompletion: Fast, Context-Sensitive Query Autocompletion for SPARQL |booktitle=Proceedings of the 31st ACM International Conference on Information & Knowledge Management |pages=3635–3639 |year=2022 |doi=10.1145/3511808.3557335}}</ref>

==Features==

* Efficient indexing and querying of very large knowledge graphs (over 100 billion triples)
* High performance on queries with large intermediate or final results
* Integration of text search with SPARQL queries<ref name="cikm2017" />
* Context-sensitive autocompletion for SPARQL queries<ref name="cikm2022" />
* Support for multiple large-scale knowledge graphs, including Wikidata, Wikimedia Commons, OpenStreetMap, UniProt, PubChem, and DBLP
* Aims for full SPARQL 1.1 support, including Federated Query (SERVICE) and Update functionalities<ref name="readme" />

==Technology==

QLever is implemented in C++. It uses advanced indexing and query optimization techniques to achieve efficient querying of large-scale knowledge graphs. The engine can be deployed on a single standard PC or server, making it accessible for various research and application scenarios.<ref name="readme" />

==Applications==

QLever provides demos for various large knowledge graphs, regularly updated:

* Complete Wikidata
* Wikimedia Commons
* OpenStreetMap
* UniProt
* PubChem
* DBLP

These demonstrations showcase QLever's capabilities in handling diverse and extensive datasets.<ref name="readme" />

==Development and Deployment==

QLever can be used via the `qlever` script, which controls all aspects of QLever's operation. The script uses a configuration file called `Qleverfile`, and comes with several example configurations. QLever can be run using Docker, which automatically pulls the most recent image.<ref name="readme" />

==Research==

QLever has been the subject of several research papers, notably:

* A 2017 paper presented at CIKM detailing the combination of SPARQL and text search capabilities.<ref name="cikm2017" />
* A 2022 paper, also presented at CIKM, focusing on QLever's context-sensitive autocompletion functionality.<ref name="cikm2022" />

==See also==

* [[SPARQL]]
* [[Resource Description Framework]]
* [[Triplestore]]
* [[Semantic Web]]
* [[Wikidata]]

==References==
{{Reflist}}

==External links==

* {{Official website|https://qlever.cs.uni-freiburg.de/}}
* [https://github.com/ad-freiburg/qlever QLever GitHub Repository]

[[Category:Semantic Web]]
[[Category:Query engines]]
[[Category:Free software programmed in C++]]
[[Category:Cross-platform free software]]

[hannahbast]

@WolfgangFahl Thanks for the initiative. What do you propose, that we write a first draft of the Wikipedia article on QLever?

[tuukka]

I think that ideally the community would write the article and no project member was involved, in order to stay clearly within English Wikipedia's conflict of interest guidelines and to avoid any extra reviews triggered by ghostwriting.

• https://en.wikipedia.org/wiki/Wikipedia:Plain_and_simple_conflict_of_interest_guide
• https://en.wikipedia.org/wiki/Wikipedia:Ghostwriting

[WolfgangFahl]

I think for a stub we are fine to initiate it and then ask the community to elaborate. Unfortunately it is not easy these days to get a wikimedia stub article out there without a request for speedy deletion. On the other hands our friends from scholia/wikicite might also be interested in spreading the word.

[tuukka]

To get started, I tried to find some third-party sources and found a good one with some familiar names: 😁

• Getting and hosting your own copy of Wikidata.
  Wolfgang Fahl, Tim Holzheim, Andrea Westerinen, Christoph Lange and Stefan Decker. pdf

[hannahbast]

@WolfgangFahl @tuukka I think that paper is outdated since a lot of development has happened since then. For a more up-to-date performance evaluation and comparison with other engines, see https://github.com/ad-freiburg/qlever/wiki/QLever-performance-evaluation-and-comparison-to-other-SPARQL-engines .

[1ec5]

I just noticed this discussion – by coincidence, I started an article about QLever back in July.