Appel à candidatures #SemWebPro !

La conférence https://semweb.pro est LA journée annuelle de présentations et de rencontres dédiées au #webSémantique dans le monde professionnel

Elle a lieu chaque année à Paris

Le comité de programme souhaite intégrer de nouveaux membres ! Vous nous rejoignez ?

Candidature avant le 30 avril à contact@semweb.pro

#semweb #websem #linkeddata #linkedopendata #knowledgegraph #thesaurus #ontology #RDF #SPARQL #SHACL #OWL #JSONLD

@semwebpro @logilab

Die Kirchenkreise der #Nordkirche auf einer #Karte auf Basis der Daten in @wikidata
Hier ist die #SPARQL-Abfrage dazu: https://w.wiki/Dm8f

For MS Word, working with its own content remains a challenge. Luckily, there is #SPARQL Anything

Le laboratoire Géographie-cités propose une journée de présentation/formation sur #Wikipédia et #wikidata avec un volet pratique #Python #R #SPARQL.

> 17 juin à Paris et en visio

Inscriptions gratuites mais obligatoires :
https://site-fef6fe.gitpages.huma-num.fr/journee/wikipedia.html

New blog post! I've started taking regular snapshots of Library of Congress linked open data (as part of a much broader data rescue and monitoring project) and took the opportunity to finally get to grips with #SPARQL, #RDF and all that comes along with it. I've started small though...

On fooling around with triples
https://erambler.co.uk/blog/on-fooling-around-with-triples/

This week’s edition of my @linkedin newsletter shines a light on the impact of the Model Context Protocol (MCP) across multiple computing eras—leading right up to today.

We’ve arrived at a moment where loose coupling principles are cool (and super useful) again.

Naturally, there are some live demo nuggets in the post to help bring it all to life:

https://www.linkedin.com/pulse/model-context-protocol-bridging-generational-computing-idehen-qdtce/

The Model Context Protocol acts as a universal translator between LLMs and data sources, eliminating complex platform-specific requirements. Our new open source MCP server for ODBC (mcp-odbc-server) enables seamless integration of any ODBC-accessible data into RAG pipelines.

Read more in my latest newsletter!

https://www.linkedin.com/pulse/whats-model-context-protocol-why-important-kingsley-uyi-idehen-zy8ue

TIL: Eine #SPARQL Abfrage in #Wikidata nach aufsteigender #QID zu sortieren ist etwas mehr, als in https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples beschrieben. Statt bloß "ORDER BY ?horse" muss man schon zu "ORDER BY (xsd:long(STRAFTER(STR(?horse),"Q")))" greifen.

We’re excited to introduce the latest Virtuoso Maintenance Release—Virtuoso 08.03.3333—packed with feature enhancements and bug fixes across the platform.

https://community.openlinksw.com/t/virtuoso-08-03-3333-release-notes/4872

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

#OSM #QLever #SPARQL #Wikidata

https://katharinabrunner.de/2025/03/openstreetmap-get-the-building-way-relation-that-encloses-a-node-using-the-qlever-sparql-endpoint/

Voilà mes interventions d'ici la fin (et un peu au delà) de ma dernière résidence #Wikimédia au sein d'une #URFIST. https://fr.wikipedia.org/wiki/Projet:Wikifier_la_science/Nice #Wikipedia #SPARQL #OpenRefine #Wikidata #WikiCommons #LicenceLibre

Looking for some #SPARQL help, as I'm a newbie to this and my mental model of how triple stores work is incomplete.

I have two versions of the same set of terms in two Turtle files, and I want to load them up into a triple store and then compare them to see what changed: terms updated, added or deleted.

I just figured out that combing markerclusters with layers in #SPARQL queries visualising results from #Wikidata on maps can give you a nice coloured classification of periodicals by publishing language.

Unfortunately the query is too long for posting it here ...

Update: created a gist for this query at https://gist.github.com/tillgrallert/3999b5da1616107a483221bc04a420c2

https://query.wikidata.org/

Wow.
I highly recommend this to /anyone/ dealing with #annotation, #metadata and large, mixed collections (#cataloging, anyone?)

I finally get the chance to learn and dive into #sparql and large #rdf datasets...

Thanks #wikimedia!
(paid my yearly "thx" just yesterday)

Wrapping my brains around RDF-triples in practice, with real-world #DigiPres data - learning #sparql and #OmekaS at the same time.

So #Wikidata confirms my assumption:
**you /do/ use a distinct property for /each/ identifier you'd like as reference**

See: https://www.wikidata.org/wiki/Property:P12083

Feels noisy, but maybe that's oldschool-flashbacks...

I would like to write a query to find, given a parent taxon, the closest child taxa with a specific identifier, such that the resulting set of identifiers is the smallest to cover all species of the parent taxon. I tried the following but it times out (and OOMs on QLever). Any ideas?

At @swat4hcls there was a presentation by @albdrg that I really like: https://www.youtube.com/watch?v=F4Nl-nmLZA4 This uses #SPARQL to combine data in a privacy aware #beacons-api and combine it with public #knowledgegraph like #uniprot by @SIB and #wikidata @wikidata to reduce cost of the data integration challenge required to answer clinical question.

Ongoing discussions on the #dblp #SPARQL Query Service at Github Discussions:
"Unable To Get Titles For Some Entities"
https://github.com/dblp/kg/discussions/6