The Semantic Owl

YouTube video by The Semantic Owl 5 Signs you Need a Graph #knowledgegraphs #semanticweb #ontology

www.youtube.com/shorts/gE5g8...

We chase accuracy like gold, but context is the map. Without map, gold can be useless. Right answer in wrong place is wrong. I learned this the hard way. #ContextIsEverything #AI #DataScience

We tune models again and again, but meaning still matters more. Garbage meaning becomes fast garbage. Fix data and concepts first.
My mistakes usually start there.

#AIethics #DataQuality #KnowledgeGraphs

Good AI should advise experts, not replace them. Imagine a doctor with infinite memory and clear sources. The human still decides, and that is important.
I trust this more than magic answers.

#AIInHealthcare #DecisionSupport #KnowledgeGraphs

LLMs remember patterns; KGs remember facts. The future is both, not one. Dreams plus memory feels balanced. I bet teams who mix them win more.

#AI #HybridIntelligence #LLMera

YouTube video by The Semantic Owl Your AI just isn't there yet... here's why. #ai #knowledgegraphs #semanticweb

www.youtube.com/shorts/uNL7J...

Asking a model is easy; trusting the answer is hard. Graphs keep language close to facts. It feels like a safety rope.
I prefer safe rope when heights are big.

#TruthInAI #KnowledgeGraphs #RAG

Think your data is clean? Merge it with three other systems. Chaos will say hello fast. KGs are built exactly for this headache.
#DataIntegration #AI #KnowledgeGraphs

YouTube video by The Semantic Owl Visualize any RDF graph online in minutes!

www.youtube.com/watch?v=uyuS...

Follow this for easy visuals!

#SemanticWeb #KnowledgeGraphs

“Data-driven” was cool, but “meaning-driven” helps me more. Knowledge Graphs make data make sense.
Less noise, more links. My brain relax when I see the connections.

#DataToKnowledge #SemanticAI #AI

LLMs dream, KGs do logic. Between the dreamer and the logician maybe lives real intelligence.
We need both hands to build something stable. I don’t know all, but this feels right.

#AI #CognitiveComputing #HybridSystems

We taught machines to talk like poets, but thinking is harder. With Knowledge Graphs, models learn to think more like scientists. Language gets a backbone, not just vibes. I like when pretty words meet solid facts.

#HybridAI #LLMs #KnowledgeGraphs

YouTube video by The Semantic Owl Why I left SQL for Semantic Graphs and you might too!🤯

www.youtube.com/watch?v=gV8T...

Are you mostly living in Excel tables and SQL rows, but a bit curious about KG world?
If you are new to semantic technologies or you just want to finally see how tables become a graph, this is for you!

#RDF #KnowledgeGraphs #SemanticWeb #AI #SQL #Excel

Explainability is not only feature, it is trust. If AI can’t say “why” I feel nervous to use it.
Even a simple reason is better than nothing. We humans need story and reason together.

#ExplainableAI #TrustInTech #KnowledgeGraphs

We don’t always need smarter models, we need smarter connections. Context beats size many days. A small smart link can fix big confusion. I still learn this every project.

#ContextIsKing #AI #DataScience

LLMs hallucinate a lot sometimes.
I think maybe they are alone with no grounding. When we add a Knowledge Graph, they get a friend who remembers facts.
Not perfect, but much better for truth. #AI #LLMs #KnowledgeGraphs #GroundedIntelligence

We keep saying “AI understands”... but does it?
Sometimes I think it just connects patterns faster than we can.
Maybe real understanding comes when we connect those patterns to meaning — which is what Knowledge Graphs quietly do behind the scenes.

3️⃣
Curious what people think about next steps: would you keep it ontology-light, or try to add ontology-aware reasoning (subclasses, property chains, constraints) on top of these hubs before/after retrieval?

2️⃣
This “hub” idea also seems like a smarter alternative to random text chunks in vanilla RAG. Precomputing small, meaningful subgraphs around papers/contributions lets embeddings see multi-hop structure, not just isolated sentences.

1️⃣
What I really like here is that it’s RDF-based but schema-agnostic – no NL→SPARQL pipeline, no heavy TBox logic. Just using triples as graph-shaped context and for provenance, which feels very pragmatic for scholarly QA.

Just read “HubLink: A Novel QA Retrieval over Knowledge Graphs”, Best Paper at #RAGEKG 2025 🧠

Instead of NL→SPARQL, it slices KG into hubs, embeds those subgraphs, does GraphRAG, and returns the triples used for provenance.

ceur-ws.org/Vol-4079

#KnowledgeGraphs #RDF

Is the Semantic Web BACK?🦉 www.youtube.com/watch?v=czfo...
Let's finally give semantics some love!