Top Tweets for #SparQL
Joint work with Vincent Emonet, Ruijie Wang, Ana Claudia Sima, and Tarcisio Mendes at SIB Swiss Institute of Bioinformatics.
#KnowledgeGraphs #SPARQL #Text2SPARQL #LLM #SemanticWeb #Bioinformatics #OpenSource [6/6]
Join us on 25 Aug for the #RDFox Semantic Reasoning #workshop! 👇
We'll be covering everything to get you started with:
✅ #KnowledgeGraphs
✅ Writing #SPARQL queries
✅ #Ontologies
✅ Datalog rules
✅ Best practice tips & tricks
📌 Sign up for free
https://t.co/CiHIrzLOME
It started as a system to help me deal with my health issues and became my OS: Retinue is a #sparql backed framework for AI agents.
Learning about #RAG I asked Fable 5:
https://t.co/V3emfYfJYv how is this different from RAG?
The core difference: RAG retrieves *text* for the LLM to read; this computes *answers* the LLM only interprets.
In RAG, you embed chunks, do similarity search, and stuff the top-k passages into context — the LLM then synthesizes something plausible from what happened to be retrieved. That fundamentally can't answer aggregate questions: no amount of chunk retrieval gets you "you slept longer than 78% of nights," [github](https://t.co/V3emfYfJYv) because that needs the full distribution at query time, and pasting a year of five-minute CGM readings into a prompt [github](https://t.co/V3emfYfJYv) isn't retrieval, it's a haystack. Here the agent writes SPARQL, and the store does exact joins, GROUP BY, and counts over years of observations — the store does the aggregation, the agent does the reasoning [github](https://t.co/V3emfYfJYv) . The result is deterministic and can't hallucinate.
The second difference is the semantic layer. RAG retrieval is fuzzy over unstructured text; here everything is normalized into shared vocabularies (SOSA, LOINC, SNOMED), so a glucose reading from a CGM, a sleep score from a ring, and a step count from a watch are all the same shape and join without negotiation. Plus provenance is structural: each file's triples land in a named graph derived from the file's path, rather than chunk metadata bolted on. The doc's own summary is apt: grep gets you text, SPARQL gets you joins — and RAG is essentially fuzzy grep.
They're complementary, though: this architecture deliberately leaves the prose bodies of notes unstructured, and answering questions *about prose* is exactly where RAG-style retrieval still fits. Structured facts → SPARQL; narrative text → retrieval.
LinkedDataHub is open source — build your own Knowledge Graph apps:
👉 https://t.co/4BfvqpGMVO
Built on RDF, SPARQL & declarative XSLT/SaxonJS.
#LinkedData #SemanticWeb #KnowledgeGraph #RDF #SPARQL
Join us on 7 July for the #RDFox Semantic Reasoning #workshop! 👇
We'll be covering everything to get you started with:
✅ #KnowledgeGraphs
✅ Writing #SPARQL queries
✅ #Ontologies
✅ Datalog rules
✅ Best practice tips & tricks
📌 Sign up for free
https://t.co/JCI0VzZkUc
i am going to publish an english version too :-) IT-Spektrum, https://t.co/yKXU06hIeP #LLMs #AgenticAI #DataPipelines #GQL #KnowledgeGraphs #Ontologies #GraphDBs #Reasoning #Microservices #DDD #NeuroSymbolic #Cypher #SPARQL
【Open Data Spaces (ODS) 本格始動】
組織のデータが、めざめる、つながる。
データ枯渇元年にカギとなる、現場のリアルデータ。
自社のデータを守りながら、信頼できる相手とだけつながる仕組み—
全容はWEBで👇
https://t.co/1g1uk6PpDY
#OpenDataSpaces #分散データマネジメント #AgenticAI
🚨 Federated SPARQL queries over public SPARQL endpoints are becoming increasingly difficult to execute, which brings the fundamental motivations behind #RDF, #SPARQL, and #KnowledgeGraphs into question.
In my new blog post, I explore this problem.
https://t.co/5anFOozXU6
Join us on 24 Feb for the #RDFox Semantic Reasoning #workshop! 👇
We'll be covering everything to get you started with:
✅ #KnowledgeGraphs
✅ Writing #SPARQL queries
✅ #Ontologies
✅ Datalog rules
✅ Best practice tips & tricks
📌 Sign up for free
https://t.co/0Wi3l1uLYn
In questo articolo mostriamo una pipeline controllata per tradurre domande in linguaggio naturale in query #SPARQL eseguibili su #wikidata
#fontistoriche
👉 https://t.co/yb2EVaMO1E
We're happy to announce the release of Comunica version 5.0, which has full SPARQL/RDF 1.2 support, more modular query parsing, improved HTTP caching, and more! https://t.co/qGEEFpaMdO #SPARQL #SPARQL12 #Query #JavaScript
#CIMon #chatbot (@Statnett and @ontotext /Graphwise): Democratizing #PowerSystemAnalytics #Talk2PowerSystem. #NLQ (#text2sparql) for #SPARQL over compex #semantic data: #electricalCIM of @ENTSO_E & @IECStandards.
🚀 #RDFox v7.5 is out now and it has a diverse array of incredible new additions!
✔️ #SPARQL Federation
✔️ Improved monitoring and #logging
✔️ Server-to-server #authentication
✔️ Semantic #similarity (experimental)
Full release notes:
https://t.co/0PtYljgmwK
Love #SPARQL and #RDF? I do. This info was super hard to find and handy.
Read this. Sparqloscope: A generic benchmark for the comprehensive and concise performance evaluation of SPARQL engines
Hannah Bast Johannes Kalmbach
Robin Textor-Falconi
Christoph Ullinger
University of Freiburg, Freiburg im Breisgau, Germany
https://t.co/aS0U2t9hcL
Super proud to share a new QLever milestone. We loaded one trillion triples on a single AMD Ryzen 9 machine with 16 cores and 128 GB RAM 🚀
Index size is 6.5TB and the dataset runs even with less memory.
Dataset: https://t.co/76eKC2LDgs
#RDF #SPARQL #KnowledgeGraphs
Just a few clicks away from your own DBpedia Knowledge Graph on AWS (2025-06 release)!
Perfect for LLM-powered AI agents needing richer context for smarter responses. 🚀
#DBpedia #KnowledgeGraphs #AI #Agentic #SPARQL #SQL #VirtuosoRDBMS
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