Turn your data DataFrames into semantic Knowledge Graphs with only three lines of code.

Knowledge Graphs and GraphRAG have sorta taken over my life the last two months or so, so I thought I would share some very important books for learners and builders. Knowledge Graphs: I’m going to really enjoy this KG book a lot more, now. It’s simple reading, in my opinion. Text as Data: if you work in Data Science and AI, just buy this book right now and then read it. You need to know this. This is my favorite NLP book. Orange Book (Sorry, long title): that is the best builder book I have found so far. It shows how to build with GraphRAG, and you should check it out. I really enjoyed reading this book and use it all the time. Just wanted to make some recommendations as I am looking at a lot of my books for ideas, lately. These are diamonds. Find them where you like to shop for books! #100daysofnetworks | 11 comments on LinkedIn

Discover how Senzing enhances data clarity with AI entity resolution, partnering with NIEMOpen to resolve entities effectively.

From Siloed Data to Missed Opportunities Organizations today sit on massive troves of data – customer transactions, logs, metrics, documents – often scattered across departments and trapped in spreadsheets or relational tables. The data is diverse, dispersed, and growing at unfathomable rates, to th

Please read the previous article on Vanilla RAG

Learn what Entity Resolution is, how it works, and why it’s essential for data quality. Explore methods, tools, and use cases in MDM, customer 360, fraud detection, and more.

Everyone's talking about LLMs. I went a different direction 🧠 While everyone's building RAG systems with document chunking and vector search, I got curious about something else after Prof Alsayed Algergawy and his assistant Vishvapalsinhji Parmar's Knowledge Graphs seminar. What if the problem isn't just retrieval - but how we structure knowledge itself? 🤔 Traditional RAG's limitation: Chop documents into chunks, embed them, hope semantic search finds the right pieces. But what happens when you need to connect information across chunks? Or when relationships matter more than text similarity? 📄➡️❓ My approach: Instead of chunking, I built a structured knowledge graph from Yelp data (220K+ entities, 555K+ relationships) and trained Graph Neural Networks to reason through connections. 🕸️ The attached visualization shows exactly why this works - see how information naturally exists as interconnected webs, not isolated chunks. 👇🏻 The difference in action: ⚡ Traditional RAG: "Find similar text about Italian restaurants" 🔍 My system: "Traverse user→review→business→category→location→hours and explain why" 🗺️ Result: 94% AUC-ROC performance with explainable reasoning paths. Ask "Find family-friendly Italian restaurants in Philadelphia open Sunday" and get answers that show exactly how the AI connected reviews mentioning kids, atmosphere ratings, location data, and business hours. 🎯 Why this matters: While others optimize chunking strategies, maybe we should question whether chunking is the right approach at all. Sometimes the breakthrough isn't better embeddings - it's fundamentally rethinking how we represent knowledge. 💡 Check my script here 🔗: https://lnkd.in/dwNcS5uM The journey from that seminar to building this alternative has been incredibly rewarding. Excited to continue exploring how structured knowledge can transform AI systems beyond what traditional approaches achieve. ✨ #AI #MachineLearning #RAG #KnowledgeGraphs #GraphNeuralNetworks #NLP #DataScience  | 36 comments on LinkedIn

🎥 𝗧𝗵𝗲 𝗳𝗶𝗹𝗺 𝘁𝗵𝗮𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗼 𝘄𝗶𝗻 𝗩𝗲𝗻𝗶𝗰𝗲? 𝗟𝗲𝘁'𝘀 𝗺𝗮𝗽 𝗶𝘁 𝗶𝗻 𝗮 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵 Just asked 𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗱𝗟𝗶𝗳𝘁 to retrieve the 𝑮𝒐𝒐𝒈𝒍𝒆 𝑲𝒏𝒐𝒘𝒍𝒆𝒅𝒈𝒆 𝑮𝒓𝒂𝒑𝒉 details for "𝑵𝒐 𝑶𝒕𝒉𝒆𝒓 𝑪𝒉𝒐𝒊𝒄𝒆" — the dark comedy thriller by Park Chan-wook that’s leading the Venice buzz. 𝗛𝗲𝗿𝗲 𝘁𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁: ✅ Google KG entity ID: /g/11w2hjvh3j ✅ Wikidata: Q129906152 ✅ Cast, director, source material, and more — in under a second. ✅ Fallback-safe, deeply enriched with attributes from Wikidata. This is Enhanced Entity Research via MCP (Model Context Protocol). ✔️ Pulls data from Google’s Enterprise KG ✔️ Enriches with Wikidata (gender, occupation, relationships, etc.) ✔️ Builds 3–5× richer profiles — instantly ready for clustering, schema, content. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿? This is what AI-ready structured data looks like — live, real-time, and grounded. From the Venice Film Festival to your Knowledge Graph in milliseconds. 💥 Don’t let a cascade of incompetence pollute your AI workflows. 💡 Combine structured knowledge with agentic AI to drive precision, context, and trust across multi-step tasks. 👉 Enjoy the artifact (on Claude): https://lnkd.in/dYYMby26 👉 Full workflow: https://lnkd.in/dNHJ_DpP

Just released a new notebook exploring Semantic Entity Resolution & Extraction using DSPy (Community) and Google's new LangExtract library. Inspired by Russell Jurney’s excellent work on semantic entity resolution, this demo follows his approach of combining: ✅ embeddings, ✅ kNN blocking, ✅ and LLM matching with DSPy (Community). On top of that, I added a general extraction layer to test-drive LangExtract, a Gemini-powered, open-source Python library for reliable structured information extraction. The goal? Detect and merge mentions of the same real-world entities across text. It’s an end-to-end flow tackling one of the most persistent data challenges. Check it out, experiment with your own data, 𝐞𝐧𝐣𝐨𝐲 𝐭𝐡𝐞 𝐬𝐮𝐦𝐦𝐞𝐫 and let me know your thoughts! cc Paco Nathan you might like this 😉 https://wor.ai/8kQ2qa

This notebook converts CSV data into a Neo4j Graph Database. All you do is describe your data. Have you wanted to see what your data looked like as a graph…