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GPBuilding knowledge Graph using #AI
#chatgpt #knowledge #KnowledgeGraph #GPT
@gpt4ar
#Knowledge Graph Ai
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INTry the new InfraNodus extension that visualizes your content as a knowledge graph. Link in bio.
#infranodus #knowledgegraphs #llms #ai #obsidian #browserextension #chromeextension
@infranodus
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YEA knowledge graph isn’t just a data hub — it’s the backbone of AI search. 📁🧠
By structuring brand info (hours, services, FAQs), you give AI the context it needs to boost discoverability, accuracy, and competitiveness.
@yextinc
39.5K
DA🧠 Graph RAG: The Next Evolution in AI Knowledge Retrieval
Traditional RAG systems are like searching through a library by keywords. Graph RAG? It's like having a librarian who understands the connections between every concept.
🔄 How Graph RAG Works:
Phase 1: Knowledge Construction
Documents are processed and chunked into manageable pieces
AI extracts entities (people, places, concepts) and their relationships
Two parallel storage systems are built:
📊 Knowledge Graph: Structured web of connected entities
🔍 Vector Database: Semantic embeddings for similarity search
Phase 2: Intelligent Retrieval When you ask a question, Graph RAG doesn't just find similar text—it thinks:
🎯 Entity Recognition: "What key concepts is the user asking about?"
🕸️ Graph Traversal: "What's connected to these concepts?"
🔎 Semantic Search: "What text chunks are contextually relevant?"
Phase 3: Context Integration
Combines structured relationships from the knowledge graph
Merges semantically similar content from vector search
LLM generates responses with both factual connections AND contextual understanding
💡 Why This Matters:
Traditional RAG: "Find documents about 'climate change'" Graph RAG: "Find documents about climate change, connect them to economic impacts, policy decisions, and related environmental factors"
The result? AI that doesn't just retrieve information—it understands the web of knowledge and delivers insights with context and connections intact.
Perfect for complex domains where relationships matter: legal research, scientific literature, business intelligence, and technical documentation.
#AI #RAG #KnowledgeGraphs #MachineLearning #DataScience
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#datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist
@datasciencebrain
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THComment 'Graph' to get the full document!
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In RAG (Retrieval-Augmented Generation), a knowledge graph organizes data as interconnected entities and relationships, enabling better context understanding and retrieval. It helps LLMs fetch precise, structured insights from linked information, improving reasoning, accuracy, and relevance of generated responses beyond traditional text-based document retrieval.
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#rag #knowledgegraph #ai
@the_ai_dude
23.5K
EDThere are multiple possible RAG architectures so don’t just assume you are using Vector Databases.
@edhonour
210.0K
ARKnowledge Graph vs Vector Database
Both of these tools can be used by LLM to pull in information to answer a users question (RAG). Using a knowledge graph is best for discovering relations between things, and using a vector database is best for finding similar things. #coding #programming #rag
@arjay_the_dev
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ONDoes anyone know the answer already? COMMENT IT BELOW!
tutorials on how to do solve it coming soon 🔥🔥
Kim and Tay introduce us to integration🫡
Disclaimer: This is not real audio/video of Kim Kardashian or Taylor Swift, it is generated by AI to help people learn about maths, physics, and engineering.
You might be thinking why are they even explaining this if we don’t work it out this way on paper?🤨
It is insanely useful to know this method because this is how computers would do integration - we'll go through this in our later engineering videos later on.
@onlocklearning
946
GRThe Hypergraph Developer Preview is live!
Build privacy-preserving, composable apps with a local-first framework.
🔐 E2E encryption
🖇️ GRC-20 composability
ℹ️ Access to a decentralized knowledge graph
➕ TypeSync & Geo Connect for effortless schemas & private-space access
@graphprotocol
84.8K
NIComment "REPORT" to get this Free AI Deep Research tool for writing interactive reports.
You don’t have to pay for ChatGPT just to get deep research for reports anymore. There’s a new tool in town. It’s called Kimi AI Researcher. And it’s free. Unlimited use. No hidden fees.
Here’s how it works. You go to their website. Click “researcher mode.” Type in your topic. That’s it. The AI does the rest. It scans hundreds of web pages. Fact-checks them. Then creates a detailed report. With citations. With graphs. With images. All in one place.
I tried it last week. Needed a report on climate change. Usually, I’d spend hours. Reading articles. Copying links. Making charts. This time? Kimi did it all in minutes. The report looked great. Everything was sourced. Even the graphs made sense.
Think of it like having a super-smart assistant. One that reads fast. Double-checks facts. And never gets tired. You just tell it what you need. It delivers. No more digging through endless Google results. No more copying and pasting into Word docs.
Here’s what you get:
1. Detailed, interactive reports
2. Proper citations for every fact
3. Clean, readable design
4. Custom graphs and images
The best part? It’s totally free. No limits. Use it for school. Work. Personal projects. Whatever you want.
Kimi isn’t just fast. It’s smart. It’s ranked high on tests like HLE and SEAL-0. That means it’s not just another AI chatbot. It actually understands your questions. And gives real answers.
Remember when you had to pay for every little feature? Those days are over. Now, you can get quality research. For nothing.
So, next time you need a report? Try Kimi AI Researcher. It’s like having a research team in your pocket. And you don’t pay a cent.
#deepresearch #chatgptdeepresearch #reportwriting #airesearch #kimiai #aitools #ainews #aiindia #aicommmunity #nicksaraev
@nick_saraev
22.4K
RAGraph databases accelerate multi-hop traversals, but most production queries are shallow (1–2 hops) that SQL or embeddings handle efficiently. Knowledge graphs are valuable for integration but costly to build and govern, and few organizations have the resources to maintain them. Most organizations find the marginal gains of graph RAG rarely justify the operational overhead.
Stonebraker, M., & Pavlo, A. (2024). What Goes Around Comes Around... And Around. SIGMOD Record.
@rajistics
506.6K
CHComment “agent” to get all ten agents listed here
@chase.h.ai
✨ #Knowledge Graph Ai Discovery Guide
Instagram hosts thousands of posts under #Knowledge Graph Ai, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Knowledge Graph Ai is one of the most engaging trends on Instagram right now. With over thousands of posts in this category, creators like @onlocklearning, @chase.h.ai and @arjay_the_dev are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
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Analysis of 12 reels
💡 Top performing posts average 4.3M views (3.0x above average). Moderate competition - consistent posting builds momentum.
Post consistently 3-5 times/week at times when your audience is most active
Content Creation Tips & Strategy
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📹 High-quality vertical videos (9:16) perform best for #Knowledge Graph Ai - use good lighting and clear audio
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