Incoming links are a revealing way to explore a Linked Data dataset — but they vanish silently when the linking subject is a blank node.
LDView now walks up the graph (up to 2 hops) to find the real subject.
Example: https://data.digitopia.nl/uri:http://data.bibliotheken.nl/id/thes/p075547279
Before: 0 incoming links. After: the full picture of where an entity appears in the dataset.
There are a couple of projects that explore #ActivityPub in its intended #LinkedData format. Plain JSON is allowed, but the AP's primary notation is using #JSONLD. I think there is a slight uptick in interest for this approach again, coming along with the opportunity to implement the Social API (client-to-server) as intended.
What I really like is that ecosystem tools which aim to ease #fediverse solution development, are gaining an interest. Most notably here is #Fedify imho, who recently received #NLnet funding to build Fedify Studio development platform..
LDView - the linked data viewer / dereferencer for humans - now follows owl:sameAs and skos:exactMatch links to fetch images from linked datasets like Wikidata.
OWL semantics at work: if two URIs identify the same thing, their properties are interchangeable.
Example at https://data.digitopia.nl/uri:http://vici.org/vici/49
En un proyecto de datos geográficos debemos responder a esta pregunta:
"¿Estamos hablando del mismo lugar?"
Madrid puede aparecer como:
una entidad en Wikidata
otra en DBpedia
otra en un tesauro
otra en una base GIS
Usé varias estrategias de Entity Matching:
✓ coincidencia estructural
✓ equivalencias Wikipedia ↔ Wikidata ↔ DBpedia
✓ reconciliación textual
✓ normalización lingüística
Demo [https://javiermurcia.tech/lab/geografico-espana/]
#EntityMatching #KnowledgeGraph #LinkedData #DataIntegration
blog post amended to focus less on #aislop and more on #linkeddata / #indieweb
https://blog.lvmbdv.dev/posts/rebuilding-my-blog-with-machines-for-machines/
I rebuilt this blog recently. Not a redesign. The old posts are still here, same URLs, same markup. What changed is that every page now carries a full structured data graph and every element is annotated with microformats. Most of the new posts were co-written with an AI that has a checklist of things it is not allowed to sound like, plus a set of voice rules. The AI drafts, I edit. The structured data handles the other side: helping machines understand the content.
Which is the canonical wikidata URI for Fectio?
https:// wikidata.org/ entity/ Q2311719
http:// wikidata.org/ entity/ Q2311719
https:// wikidata.org/ wiki/ Q2311719
http:// wikidata.org/ wiki/ Q2311719
http:// www. wikidata.org/ entity/ Q2311719
https:// www. wikidata.org/ entity/ Q2311719
http:// www. wikidata.org/ wiki/ Q2311719
https:// www. wikidata.org/ wiki/ Q2311719
Answer at https://data.digitopia.nl/uri:http://vici.org/vici/49
Links to wikidata have returned to the Vici.org rdf:
For example: https://data.digitopia.nl/uri:http://vici.org/vici/100
What makes data trustworthy enough for people or their agents to act on it?
I wrote about Linked Data based “trust triggers” that can give systems good reasons to act.
https://pietercolpaert.be/interoperability/2026/06/11/trust-triggers
Do I know someone (or can someone connect me to someone) at @heiseonline or @golem ? I would love to discuss the idea of embedding #LinkedData in articles to better identify organizations and products discussed in news articles.
Broadly relates to this metadata proposal https://github.com/lsmith77/open-source-ecosystem and more concretely our simplistic integration of the Heise newsfeed in https://www.ossdirectory.com
Proposal to extend publiccode.yml with faceted classification, supply chain references, and credit/usage/funding registry discovery. Companion specs for decentralized usage declarations, funding tr...
René Voorburg
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