#SWAT4HCLS nice to see #qlever with a 1 trillion triple dataset. Or four UniProts ;)

The QLever SPARQL engine: fast, scalable, with autocompletion and text search

Hier die Daten, dies schon nach #qlever geschafft haben: https://qlever.dev/wikidata/ZVyIxB

Kann das Procedere gerne in einem Blog-Beiträg schildern, wenn Dein Call noch offen ist, @JensB

Rescuing Scholia #3: We did it! It was not a set up, when I openly wondered if we would be able to rescue Scholia in time. I honestly did not know. Three weeks and some serious hacking by an international team later I was more optimistic. Actually, just before christmas, we started writing a SWAT4HCLS 2026 demonstration abstract. This was accepted and you can read the _Scholia 2026: Compliance with SPARQL 1.1_ preprint here and here. This paper describes the work that had to be done, and I am deeply grateful to everyone who contributed with smaller or bigger contributions (Daniel, Peter, Konrad, Johannes, Lars, Wolfgang, Hannah). I am merely first author for the demo, and just another contributor to the long series of patches, in a branch started by Prof. Hannah Bast. The work actually started long before that, with the _Robustifying Scholia_ grant (see doi:10.3897/rio.5.e35820), where we explored alternatives. The Wikidata graph (RDF) split has been long coming, and I can recommend this recent The Signpost article by Lane for a good overview. So, this would not have been possible with the many people who contributed over the years. But this last sprint really made a difference. The developments of the QLever software in the past year are very important, and the SPARQL endpoint we run now is live updated, just like we knew from the Wikidata Query Service (WDQS). Recent improvement allowed us to replace all the Wikidata and Blazegraph specific aspects of the SPARQL queries, and good discussions let to pragmatic approaches to keep localization features Scholia had for displaying query results from Wikidata. The work is not completed, however. All queries are SPARQL 1.1 now, but some can still be further optimized, and some still need some fixing. For example, I still spot some QIDs here and there, instead of the localized labels that should be shown instead. Also, we are actively looking in getting everything running again on WMF servers (see this overview issue), so that _scholia.toolforge.org_ works again. For now, however, please use qlever.scholia.wiki.

new blog: "Rescuing Scholia #3: We did it!" chem-bla-ics.linkedchemistry.info/2026/02/28/rescuing-scho... https://doi.org/10.59350/kd793-2fe02

"For now, however, please use qlever.scholia.wiki." https://qlever.scholia.wiki/

#wikidata #scholia #qlever #sparql

What is in #wikidata? The answer to this question on the Wikidata:Statistics page (https://www.wikidata.org/wiki/Wikidata:Statistics hasn't been updated for a while. This #sparql query run in #qlever provides a snapshot of the current situation (it's not […]

[Original post on openbiblio.social]

Für einen kurzen Impuls habe ich mir die Präsenz der Stiftung Preußischer Kulturbesitz im #Wikiversum angesehen und ein paar Daten dazu zusammengetragen. Neben #qlever, #petscan und #glamorgan habe ich inbesondere versucht, die Revisionshistorie der Items […]

[Original post on openbiblio.social]

The QLever SPARQL engine: fast, scalable, with autocompletion and text search

A list of literary works that will enter the public domain in 2026: https://qlever.dev/wikidata/KSfM4e

And here the corresponding language statistics: https://qlever.dev/wikidata/AmAEAn

#publicdomain #publicdomainday #wikidata #qlever

Original post on openbiblio.social

Ok, it now seems as if #qlever mapped the data-namespace (data:Q42 a schema:Dataset) onto the wd namespace (wd:Q42 a wikibase:Item) which makes it more compatible with #wikidata. I find it still a bit confusing as it differs from the RDF source. If you want to get, say, the number of sitelinks […]

Karte Berlins mit 67 blauen Punkten, die für je eine Fahrradreparaturstation stehen

kleiner Service-#SPARQL-Query für Radfahrer:innen in Berlin: Fahrradreparaturstationen in Berlin laut #openstreetmap, abgefragt mit #qlever: https://qlever.dev/osm-planet/7rq0zw?exec=true

Original post on openbiblio.social

Gestern kam im #SPARQL-Workshop auf, wieso man überhaupt noch den #WDQS-Service benutzen sollte und nicht gleich den performanteren #wikidata-Endpunkt von #qlever . Gründe sind für mich aktuell noch:

- Autocomplete funktioniert in #wdqs insofern besser, als man ihn gezielter triggern kann
- […]

According to #openstreetmap (as imported into #qlever) there are 5446 "Via Dante Alighieri" in Italy (https://qlever.dev/osm-planet/GT1RO7 The distribution is not perfectly even.

situation is somewhat different when it comes to collection items with image on #wikicommons: https://qlever.dev/wikidata/ixgIcV

#sparql #qlever #federation

Original post on openbiblio.social

Die @DNB_Aktuelles hat jetzt den #qlever-Endpunkt in eine eigene Subdomain geholt, über den der Datenbestand der DNB (und #GND) als #LOD performant ausgewertet werden: https://sparql.dnb.de/

Es gibt auch schon ein Dokument mit einer Beispielabfrage. Ich gehe davon aus, dass das dank des #DNBLab […]

Scholarly timelined network screenshot

Another in-browser JavaScript code that retrieve data from Qlever (Wikidata) and display the network on a timeline !!

Great for history of science by tracing backward citations of renown academic works pmartinolli.github.io/code/timelin... #qlever #wikidata #javascript #skystorian #vibecoded

Scholarly timelined networks screenshot

Another in-browser JavaScript code that retrieve data from Qlever (Wikidata) and display the network on a timeline !!

Great for history of science by tracing backward citations of renown academic works (Qlever has an un-divided graph so we can retrive easily […]

[Original post on mastodon.social]

#Dagstuhl Seminar "Open Scholarly Information Systems":
Hannah Bast gives an interactive introduction to Knowledge Graphs and the #QLever #SPARQL engine.

Hannah Bast & team created a #benchmark evaluating the #SPARQL query performance on the #dblp #knowledgegraph. According to the results, using the #Qlever engine was the right decision. ~MRA

qlever.cs.uni-freiburg.de/evaluation-p...
ad-publications.cs.uni-freiburg.de/ISWC_sparqlo...
sparql.dblp.org

Original post on openbiblio.social

Service-Link zur heutigen @digiSberlin -Veranstaltung anlässlich des #Digitaltag2025 (vgl. openbiblio.social/@digiSberlin/11473257709...

Folgender Query listet die Online-Kataloge/Online-Sammlungen der Berliner #GLAM-Einrichtungen: https://qlever.cs.uni-freiburg.de/wikidata/jGfXMD […]

Original post on openbiblio.social

Spätestens jetzt, wo auch die #loc ins Fadenkreuz des Trumpismus gekommen ist, lohnt es sich vermutlich, sich zu überlegen, welche Services/Projekte der LOC man gerne auch zukünftig zur Verfügung hätte. Hier die Dinge, von denen #wikidata weiß:

https://w.wiki/E3yD

(Alternative über #qlever […]

The QLever SPARQL engine: fast, scalable, with autocompletion and text search

Check out #Qlever!

https://qlever.cs.uni-freiburg.de/wikidata

OpenStreetMap as central tool Coordinates plus OSM = Context So Data becomes Information

SPARQL query in QLever

OpenStreetMap: Get the building (way/relation) that encloses a node using the QLever SPARQL endpoint.

#OSM #QLever #SPARQL #Wikidata

katharinabrunner.de/2025/03/open...

OpenStreetMap: Get the building (way/relation) that encloses a node using the QLever SPARQL endpoint When investigating commercially available location data, I use outlines of buildings from OpenStreetMap a lot. > The OpenStreetMap gives this context and transforms the raw data to information. For example, on the screenshot you can see the Bundesnachrichtendient in Berlin, the foreign intelligence service. I can download that orange line as a polygon. But sometimes, this is not enough. Take this scenario: A company has its office or shop in a building that it shares with others. This occurs often in urban areas where different organizations or comanies rent space in the same building. A simple example from Munich: There is a pharmacy in the building of the city hall: https://www.openstreetmap.org/node/254057401. How can I get the enclosing building? On a Wednesday afternoon in December 2024 I was not able to write the query in Overpass Turbo that solves this task. Luckily, the next day I attended a Wikidata workshop organised by WikiMuc about Federated Queries. One of the talks was by Hannah Bast called „Federated Queries with QLever“. Bast is a professor at the University of Freiburg, she holds the Chair for Algorithms and Data Structures. QLever is a SPARQL engine that allows us to query multiple data sources from one endpoint, e.g. Wikidata and OpenStreetMap. On Github the developers describe QLever as „a very fast SPARQL engine, much faster than most existing engines. It can handle graphs with more than hundred billion triples on a single machine with moderate resources.“ I took advantage of the moment and asked her how she would write the query in QLever to get the corresponding building of a node. Bast wrote the query live in no time. She truly lived up to her reputation. So, let’s return to the pharmacy in Munich. Let’s generalize the question a bit and query for all pharmacies in Munich that are nodes and get their enclosing buildings: Link to QLever query PREFIX osmkey: <https://www.openstreetmap.org/wiki/Key:> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX wd: <http://www.wikidata.org/entity/> PREFIX wdt: <http://www.wikidata.org/prop/direct/> PREFIX geo: <http://www.opengis.net/ont/geosparql#> PREFIX osm2rdfkey: <https://osm2rdf.cs.uni-freiburg.de/rdf/key#> PREFIX ogc: <http://www.opengis.net/rdf#> PREFIX osm: <https://www.openstreetmap.org/> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX osmrel: <https://www.openstreetmap.org/relation/> SELECT DISTINCT ?osm_id ?type ?building ?building_geometry WHERE { { osmrel:62428 ogc:sfContains ?osm_id . ?osm_id osmkey:amenity 'pharmacy' . ?osm_id rdf:type ?type . ?osm_id rdf:type osm:node . ?building ogc:sfContains ?osm_id . ?building osmkey:building [] . ?osm_id geo:hasCentroid/geo:asWKT ?node_geometry . ?building geo:hasGeometry/geo:asWKT ?building_geometry . } } The result on a map: And how about the pharmacy in the city hall? QLever returns the whole city hall, not just the part that can be seen in the screenshot above. Why? That little part is not a building in OSM, since it’s a part of a building, see here the edit: „building=yes Tags entfernt, da es sich um Gebäudeteile handelt“. This clearly shows that such a query and the spatial joins based on it make the results less certain – but perhaps also reveal connections that would not have been found otherwise. ## Links * QLever Wiki * QLever Queries: Comparisons between QLever, Overpass Turbo, PostGIS, Sophox * All the examples in QLever * Paper „An Efficient RDF Converter and SPARQL Endpoint for the Complete OpenStreetMap Data“: https://ad-publications.cs.uni-freiburg.de/SIGSPATIAL_osm2rdf_BBKL_2021.pdf

OpenStreetMap: Get the building (way/relation) that encloses a node using the QLever SPARQL endpoint.

#OSM #QLever #SPARQL #Wikidata

katharinabrunner.de/2025/03/openstreetmap-ge...

Original post on mapstodon.space

Want the raw data? Here it is using #QLever and #OverpassUltra, exportable as #GeoJSON and in the #PublicDomain […]

OpenHistoricalMap/QGIS - OpenStreetMap Wiki

Want to create a time series animation like the one @bmacs001 posted, but for your favorite region? The #OSMWiki has the rudiments of a guide to creating one with {#OverpassTurbo or #OverpassUltra or #QLever} + #QGIS + #FFmpeg:

wiki.openstreetmap.org/wiki/OpenHistoricalMap/Q...

QLever is back and even more meta You might’ve noticed that QLever has been stuck without updates of OHM data for a few weeks. The QLever team has resumed daily updates and also upgraded the endpoint to the latest version of the software. The upgrade comes with breaking changes that might affect any queries you might have stashed away, due to the elimination of the osm2rdfmember: prefix: Old predicate New predicate Description osmrel:member osmrel:member A member within the relation osmrel:member/osm2rdfmember:id osm...

If you’ve been querying our data using #QLever, updates have resumed with some breaking changes and new features:

forum.openhistoricalmap.org/t/qlever-is-back-and-eve...

#qlever #wikidata backend has been struggling recently, right?

The QLever SPARQL engine: fast, scalable, with autocompletion and text search

Quote: "At #mpievolbio, to answer this and similar questions, we map both resources to #RDF #KnowledgeGraphs using the #ontop framework, serve the materialized #KGs via #qLever (qlever.cs.uni-freiburg.de) and run a #SPARQL query. Answered in no time!"

@nfdi4bioimage.bsky.social
@openmicroscopy.org

Original post on openbiblio.social

#federation with #qlever is really fantastic! You can easily supply missing information from other sources. This query get street addresses of cultural heritage institutions from #openstreetmap #osm where there is no corresponding statement on #wikidata […]

Social-Media-Accounts Berliner Museen (nach #wikidata ): https://qlever.cs.uni-freiburg.de/wikidata/2iuKFt

#Berlin #openGlam #qlever

#qlever 420ms, #wikidata #query service Timeout nach 60 Sek. , also mindestens Faktor 143. Keine Ahnung, was da die technischen Hintergründe sind; für die produktive Arbeit mit Wikidata ist #Blazegraph aber ein enorm limitierender Faktor.

🚍🏙️ Spatial analytics in QLever is crazy good! Here’s a query from the OpenStreetMap dataset showing all Jakarta buildings within 400m of a public transport stop. You can play with the examples on their endpoint:

qlever.dev/osm-planet/ #SpatialAnalytics #openstreetmap #RDF #SPARQL #QLever #Jakarta