#sparql — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #sparql, aggregated by home.social.
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I might have told people to join @inventaire because it has a #SPARQL endpoint 😁 #wikidata #wikimania
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I might have told people to join @inventaire because it has a #SPARQL endpoint 😁 #wikidata #wikimania
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#Forschungssoftware auffindbar machen: Im Online-Editathon der NFDI4Objects-Cluster „Semantic Modelling & Linked Open Data“ und „Research Software Engineering“ arbeiten wir mit #Wikidata, #SPARQL und #Scholia an besseren Verknüpfungen zwischen #Software, #Publikationen, #Daten, #Personen und #Institutionen.
Mit Quarto-OER zum Nacharbeiten.
📅 18.08.2026, 14–16 Uhr
👥 max. 10 Teilnehmende
Vorkenntnisse in Wikidata/semantischer Modellierung sehr wünschenswert.ℹ️ Infos: https://www.nfdi4objects.net/events/n4o_activity_20260818-CM26-RS_Wikidata/
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#Forschungssoftware auffindbar machen: Im Online-Editathon der NFDI4Objects-Cluster „Semantic Modelling & Linked Open Data“ und „Research Software Engineering“ arbeiten wir mit #Wikidata, #SPARQL und #Scholia an besseren Verknüpfungen zwischen #Software, #Publikationen, #Daten, #Personen und #Institutionen.
Mit Quarto-OER zum Nacharbeiten.
📅 18.08.2026, 14–16 Uhr
👥 max. 10 Teilnehmende
Vorkenntnisse in Wikidata/semantischer Modellierung sehr wünschenswert.ℹ️ Infos: https://www.nfdi4objects.net/events/n4o_activity_20260818-CM26-RS_Wikidata/
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#Forschungssoftware auffindbar machen: Im Online-Editathon der NFDI4Objects-Cluster „Semantic Modelling & Linked Open Data“ und „Research Software Engineering“ arbeiten wir mit #Wikidata, #SPARQL und #Scholia an besseren Verknüpfungen zwischen #Software, #Publikationen, #Daten, #Personen und #Institutionen.
Mit Quarto-OER zum Nacharbeiten.
📅 18.08.2026, 14–16 Uhr
👥 max. 10 Teilnehmende
Vorkenntnisse in Wikidata/semantischer Modellierung sehr wünschenswert.ℹ️ Infos: https://www.nfdi4objects.net/events/n4o_activity_20260818-CM26-RS_Wikidata/
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#Forschungssoftware auffindbar machen: Im Online-Editathon der NFDI4Objects-Cluster „Semantic Modelling & Linked Open Data“ und „Research Software Engineering“ arbeiten wir mit #Wikidata, #SPARQL und #Scholia an besseren Verknüpfungen zwischen #Software, #Publikationen, #Daten, #Personen und #Institutionen.
Mit Quarto-OER zum Nacharbeiten.
📅 18.08.2026, 14–16 Uhr
👥 max. 10 Teilnehmende
Vorkenntnisse in Wikidata/semantischer Modellierung sehr wünschenswert.ℹ️ Infos: https://www.nfdi4objects.net/events/n4o_activity_20260818-CM26-RS_Wikidata/
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#Forschungssoftware auffindbar machen: Im Online-Editathon der NFDI4Objects-Cluster „Semantic Modelling & Linked Open Data“ und „Research Software Engineering“ arbeiten wir mit #Wikidata, #SPARQL und #Scholia an besseren Verknüpfungen zwischen #Software, #Publikationen, #Daten, #Personen und #Institutionen.
Mit Quarto-OER zum Nacharbeiten.
📅 18.08.2026, 14–16 Uhr
👥 max. 10 Teilnehmende
Vorkenntnisse in Wikidata/semantischer Modellierung sehr wünschenswert.ℹ️ Infos: https://www.nfdi4objects.net/events/n4o_activity_20260818-CM26-RS_Wikidata/
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#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.
Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)
Closing out with OWL: Description Logic SROIQ(D), etc.
See you space cowboy…
#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop
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#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.
Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)
Closing out with OWL: Description Logic SROIQ(D), etc.
See you space cowboy…
#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop
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#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.
Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)
Closing out with OWL: Description Logic SROIQ(D), etc.
See you space cowboy…
#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop
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#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.
Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)
Closing out with OWL: Description Logic SROIQ(D), etc.
See you space cowboy…
#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop
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#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.
Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)
Closing out with OWL: Description Logic SROIQ(D), etc.
See you space cowboy…
#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop
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17 Uhr geht der SPARQL Workshop mit Schwerpunkt GND und Wikidata los. Ich freue mich über die vielen Anmeldungen und bin gespannt.
https://meta.wikimedia.org/wiki/Event:GND_(Gemeinsame_Normdatei)_SPARQL_Workshop
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17 Uhr geht der SPARQL Workshop mit Schwerpunkt GND und Wikidata los. Ich freue mich über die vielen Anmeldungen und bin gespannt.
https://meta.wikimedia.org/wiki/Event:GND_(Gemeinsame_Normdatei)_SPARQL_Workshop
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17 Uhr geht der SPARQL Workshop mit Schwerpunkt GND und Wikidata los. Ich freue mich über die vielen Anmeldungen und bin gespannt.
https://meta.wikimedia.org/wiki/Event:GND_(Gemeinsame_Normdatei)_SPARQL_Workshop
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17 Uhr geht der SPARQL Workshop mit Schwerpunkt GND und Wikidata los. Ich freue mich über die vielen Anmeldungen und bin gespannt.
https://meta.wikimedia.org/wiki/Event:GND_(Gemeinsame_Normdatei)_SPARQL_Workshop
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17 Uhr geht der SPARQL Workshop mit Schwerpunkt GND und Wikidata los. Ich freue mich über die vielen Anmeldungen und bin gespannt.
https://meta.wikimedia.org/wiki/Event:GND_(Gemeinsame_Normdatei)_SPARQL_Workshop
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RE: https://digitalcourage.social/@tillgrallert/112354470001959255
AND it appears that Query Chest deletes queries after two years, thus breaking all published links … given that there is practically no documentation of this tool, I should have used more caution in adopting it.
Does anybody know of a way to share links to exceptionable #SPARQL queries on #Wikidata, which also works for complex queries or those not limited to #ASCII?
Edit: after checking more of my existing short URLs to SPARQL queries, it appears as if Query Chest has flushed its entire cache of queries in recent days. One can still submit new queries and the site works as expected, but all existing links are gone.
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RE: https://digitalcourage.social/@tillgrallert/112354470001959255
AND it appears that Query Chest deletes queries after two years, thus breaking all published links … given that there is practically no documentation of this tool, I should have used more caution in adopting it.
Does anybody know of a way to share links to exceptionable #SPARQL queries on #Wikidata, which also works for complex queries or those not limited to #ASCII?
Edit: after checking more of my existing short URLs to SPARQL queries, it appears as if Query Chest has flushed its entire cache of queries in recent days. One can still submit new queries and the site works as expected, but all existing links are gone.
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RE: https://digitalcourage.social/@tillgrallert/112354470001959255
AND it appears that Query Chest deletes queries after two years, thus breaking all published links … given that there is practically no documentation of this tool, I should have used more caution in adopting it.
Does anybody know of a way to share links to exceptionable #SPARQL queries on #Wikidata, which also works for complex queries or those not limited to #ASCII?
Edit: after checking more of my existing short URLs to SPARQL queries, it appears as if Query Chest has flushed its entire cache of queries in recent days. One can still submit new queries and the site works as expected, but all existing links are gone.
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RE: https://digitalcourage.social/@tillgrallert/112354470001959255
AND it appears that Query Chest deletes queries after two years, thus breaking all published links … given that there is practically no documentation of this tool, I should have used more caution in adopting it.
Does anybody know of a way to share links to exceptionable #SPARQL queries on #Wikidata, which also works for complex queries or those not limited to #ASCII?
Edit: after checking more of my existing short URLs to SPARQL queries, it appears as if Query Chest has flushed its entire cache of queries in recent days. One can still submit new queries and the site works as expected, but all existing links are gone.
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RE: https://digitalcourage.social/@tillgrallert/112354470001959255
AND it appears that Query Chest deletes queries after two years, thus breaking all published links … given that there is practically no documentation of this tool, I should have used more caution in adopting it.
Does anybody know of a way to share links to exceptionable #SPARQL queries on #Wikidata, which also works for complex queries or those not limited to #ASCII?
Edit: after checking more of my existing short URLs to SPARQL queries, it appears as if Query Chest has flushed its entire cache of queries in recent days. One can still submit new queries and the site works as expected, but all existing links are gone.
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From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first stepsThe triple is the message. 📡
@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql
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From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first stepsThe triple is the message. 📡
@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql
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From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first stepsThe triple is the message. 📡
@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql
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From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first stepsThe triple is the message. 📡
@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql
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From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first stepsThe triple is the message. 📡
@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql
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Ik zie dat de Nederlandse #overheid ( @Logius ) de #TOOI ontologieën en thesauri online te raadplegen heeft gemaakt via #ShowVoc. Heel cool!
https://standaarden.overheid.nl/showvoc/#/datasets
Al deze informatie kan ook via een SPARQL-endpoint benaderd worden. En #WikiData federeert sinds kort met het TOOI SPARQL-endpoint.
#LinkedData #thesauri #RDF #OWL #SKOS #RDFS #SPARQL #ontologie #overheidsinformatie #metadata
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Ik zie dat de Nederlandse #overheid ( @Logius ) de #TOOI ontologieën en thesauri online te raadplegen heeft gemaakt via #ShowVoc. Heel cool!
https://standaarden.overheid.nl/showvoc/#/datasets
Al deze informatie kan ook via een SPARQL-endpoint benaderd worden. En #WikiData federeert sinds kort met het TOOI SPARQL-endpoint.
#LinkedData #thesauri #RDF #OWL #SKOS #RDFS #SPARQL #ontologie #overheidsinformatie #metadata
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Ik zie dat de Nederlandse #overheid ( @Logius ) de #TOOI ontologieën en thesauri online te raadplegen heeft gemaakt via #ShowVoc. Heel cool!
https://standaarden.overheid.nl/showvoc/#/datasets
Al deze informatie kan ook via een SPARQL-endpoint benaderd worden. En #WikiData federeert sinds kort met het TOOI SPARQL-endpoint.
#LinkedData #thesauri #RDF #OWL #SKOS #RDFS #SPARQL #ontologie #overheidsinformatie #metadata
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Ik zie dat de Nederlandse #overheid ( @Logius ) de #TOOI ontologieën en thesauri online te raadplegen heeft gemaakt via #ShowVoc. Heel cool!
https://standaarden.overheid.nl/showvoc/#/datasets
Al deze informatie kan ook via een SPARQL-endpoint benaderd worden. En #WikiData federeert sinds kort met het TOOI SPARQL-endpoint.
#LinkedData #thesauri #RDF #OWL #SKOS #RDFS #SPARQL #ontologie #overheidsinformatie #metadata
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Ik zie dat de Nederlandse #overheid ( @Logius ) de #TOOI ontologieën en thesauri online te raadplegen heeft gemaakt via #ShowVoc. Heel cool!
https://standaarden.overheid.nl/showvoc/#/datasets
Al deze informatie kan ook via een SPARQL-endpoint benaderd worden. En #WikiData federeert sinds kort met het TOOI SPARQL-endpoint.
#LinkedData #thesauri #RDF #OWL #SKOS #RDFS #SPARQL #ontologie #overheidsinformatie #metadata
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oh, das ist mir neu in #SPARQL. Ich habe bislang immer nach 'LANG(?string) = "en"' gefiltert. Finde diese Syntax auch nicht in der SPARQL-Spezfikation 🤷
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oh, das ist mir neu in #SPARQL. Ich habe bislang immer nach 'LANG(?string) = "en"' gefiltert. Finde diese Syntax auch nicht in der SPARQL-Spezfikation 🤷
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oh, das ist mir neu in #SPARQL. Ich habe bislang immer nach 'LANG(?string) = "en"' gefiltert. Finde diese Syntax auch nicht in der SPARQL-Spezfikation 🤷
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oh, das ist mir neu in #SPARQL. Ich habe bislang immer nach 'LANG(?string) = "en"' gefiltert. Finde diese Syntax auch nicht in der SPARQL-Spezfikation 🤷
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oh, das ist mir neu in #SPARQL. Ich habe bislang immer nach 'LANG(?string) = "en"' gefiltert. Finde diese Syntax auch nicht in der SPARQL-Spezfikation 🤷
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The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?
The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.
This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy… -
The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?
The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.
This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy… -
The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?
The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.
This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy… -
The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?
The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.
This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy… -
The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?
The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.
This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy… -
this is amazing
https://github.com/andjar/wikitiddler
extracts data from multiple sources ( #Wikidata #SPARQL, JSON endpoints, RSS feeds ), converts the results to TiddlyWiki tiddlers with Python, builds a single-file #TiddlyWiki with Node.js, and deploys it to GitHub Pages — on a monthly schedule or on demand.
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this is amazing
https://github.com/andjar/wikitiddler
extracts data from multiple sources ( #Wikidata #SPARQL, JSON endpoints, RSS feeds ), converts the results to TiddlyWiki tiddlers with Python, builds a single-file #TiddlyWiki with Node.js, and deploys it to GitHub Pages — on a monthly schedule or on demand.
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this is amazing
https://github.com/andjar/wikitiddler
extracts data from multiple sources ( #Wikidata #SPARQL, JSON endpoints, RSS feeds ), converts the results to TiddlyWiki tiddlers with Python, builds a single-file #TiddlyWiki with Node.js, and deploys it to GitHub Pages — on a monthly schedule or on demand.
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this is amazing
https://github.com/andjar/wikitiddler
extracts data from multiple sources ( #Wikidata #SPARQL, JSON endpoints, RSS feeds ), converts the results to TiddlyWiki tiddlers with Python, builds a single-file #TiddlyWiki with Node.js, and deploys it to GitHub Pages — on a monthly schedule or on demand.
