The audiobook version of "Knowledge Graphs and LLMs in Action" is now available
🎧 Exciting news! The audiobook version of "Knowledge Graphs and LLMs in Action" is now available!
Are you busy but would love to learn how to build powerful and explainable AI solutions? No problem! Manning has just released the audio version of our book.
Now you can listen while you're:
- Running and training for your next marathon 🏃
- Commuting to the office 🚗
- Sitting in the parking lot waiting for your kids to finish their violin lesson 🎻
Your schedule is packed, but that shouldn't stop you from mastering these powerful AI techniques.
Get your copy here: https://hubs.la/Q03MVhhk0
And don't forget to use discount code: lagraphs40 for 40% off!
Clever solutions for smart people.
The audiobook version of "Knowledge Graphs and LLMs in Action" is now available
Help !! I just let the pre-order live! 😬
All of you who signed up for more information shall have received an e-mail with pre-order option now.
This is the most scariest sh*t I've done in a long time. Seeing pre-orders ticking in to something I've created---together with the most amazing guest authors---is a super weird feeling. THANK YOU! ❤️
It's been quite a process. From an idea planted in 2022, to now seeing the light at the end of the tunnel. I've spend hours and hours inside TeXworks, nerding around with LaTeX and Ti𝘬Z. Numerous moments at pubs, while waiting for someone, to edit, edit and edit. Taking vacation off work to isolate myself to write (so effective, btw!). Having a gold team of proofreaders, providing with super valuable feedback. Working with awesome SHACL practitioners to tell great SHACL stories to you! IT HAS BEEN GREAT FUN!
This week, I have been focusing on final touches (thank you Data Treehouse for letting me do this!!). Indexing like a hero.
Soon the words, bits and blobs will hit the printing press, and the first copies will ship on 𝐍𝐨𝐯𝐞𝐦𝐛𝐞𝐫 3𝐫𝐝.
If you want to pre-order, tag along to https://lnkd.in/dER72USX for more information. All pre-orders will get a tiny SHACL surprise inside their book. 😇
Btw: the final product will probably not look like this, I got good help from our mutual friend ChatGPT, but I know it will be yellow at least. 💛
Knowledge Graphs and LLMs in Action - Alessandro Negro with Vlastimil Kus, Giuseppe Futia and Fabio Montagna
Knowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights.
In Knowledge Graphs and LLMs in Action you will learn how to:
Model knowledge graphs with an iterative top-down approach based in business needs
Create a knowledge graph starting from ontologies, taxonomies, and structured data
Use machine learning algorithms to hone and complete your graphs
Build knowledge graphs from unstructured text data sources
Reason on the knowledge graph and apply machine learning algorithms
Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs and LLMs in Action, you’ll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You’ll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance.
This is the title of my upcoming book. And it’s all about the Shapes Constraint Language (SHACL). Expected release before November 1st 2025. The book is written and illustrated by Veronika He…
❓ Why I Wrote This Book?
In the past two to three years, we've witnessed a revolution. First with ChatGPT, and now with autonomous AI agents. This is only the beginning. In the years ahead, AI will transform not only how we work but how we live. At the core of this transformation lies a single breakthrough technology: large language models (LLMs). That’s why I decided to write this book.
This book explores what an LLM is, how it works, and how it develops its remarkable capabilities. It also shows how to put these capabilities into practice, like turning an LLM into the beating heart of an AI agent. Dissatisfied with the overly simplified or fragmented treatments found in many current books, I’ve aimed to provide both solid theoretical foundations and hands-on demonstrations. You'll learn how to build agents using LLMs, integrate technologies like retrieval-augmented generation (RAG) and knowledge graphs, and explore one of today’s most fascinating frontiers: multi-agent systems. Finally, I’ve included a section on open research questions (areas where today’s models still fall short, ethical issues, doubts, and so on), and where tomorrow’s breakthroughs may lie.
🧠 Who is this book for?
Anyone curious about LLMs, how they work, and how to use them effectively. Whether you're just starting out or already have experience, this book offers both accessible explanations and practical guidance. It's for those who want to understand the theory and apply it in the real world.
🛑 Who is this book not for?
Those who dismiss AI as a passing fad or have no interest in what lies ahead. But for everyone else this book is for you. Because AI agents are no longer speculative. They’re real, and they’re here.
A huge thanks to my co-author Gabriele Iuculano, and the Packt's team: Gebin George, Sanjana Gupta, Ali A., Sonia Chauhan, Vignesh Raju., Malhar Deshpande
#AI #LLMs #KnowledgeGraphs #AIagents #RAG #GenerativeAI #MachineLearning #NLP #Agents #DeepLearning
| 22 comments on LinkedIn
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
𝐁𝐨𝐨𝐤 𝐩𝐫𝐨𝐦𝐨𝐭𝐢𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐢𝐬 𝐨𝐧𝐞 𝐢𝐬 𝐰𝐨𝐫𝐭𝐡 𝐢𝐭.. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐚𝐭 𝐢𝐭𝐬 𝐛𝐞𝐬𝐭..
This masterpiece was published by Salvatore Raieli and Gabriele Iuculano, and it is available for orders from today, and it's already a 𝐁𝐞𝐬𝐭𝐬𝐞𝐥𝐥𝐞𝐫!
While many resources focus on LLMs or basic agentic workflows, what makes this book stand out is its deep dive into grounding LLMs with real-world data and action through the powerful combination of 𝘙𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭-𝘈𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 (𝘙𝘈𝘎) 𝘢𝘯𝘥 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘎𝘳𝘢𝘱𝘩𝘴.
This isn't just about building Agents; it's about building AI that reasons, retrieves accurate information, and acts autonomously by leveraging structured knowledge alongside advanced LLMs.
The book offers a practical roadmap, packed with concrete Python examples and real-world case studies, guiding you from concept to deployment of intelligent, robust, and hallucination-minimized AI solutions, even orchestrating multi-agent systems.
Order your copy here - https://packt.link/RpzGM
#AI #LLMs #KnowledgeGraphs #AIAgents #RAG #GenerativeAI #MachineLearning
Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.
You’ve built a RAG system and grounded it in your own data. Then you ask a complex question that needs to draw from multiple sources. Your heart sinks when the answers you get are vague or plain wrong.
How could this happen?
Traditional vector-only RAG bases its outputs on just the words you use in your prompt. It misses out on valuable context because it pulls from different documents and data structures. Basically, it misses out on the bigger, more connected picture.
Your AI needs a mental model of your data with all its context and nuances. A knowledge graph provides just that by mapping your data as connected entities and relationships. Pair it with RAG to create a GraphRAG architecture to feed your LLM information about dependencies, sequences, hierarchies, and deeper meaning.
Check out The Developer’s Guide to GraphRAG. You’ll learn how to:
Prepare a knowledge graph for GraphRAG
Combine a knowledge graph with native vector search
Implement three GraphRAG retrieval patterns
New workbook for the Ontology Engineering textbook
The first part of my textbook revisions is complete and there’s now a first version of the accompanying workbook (also available here)! It’s designed partially to not substantially increase the pri…
"Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
🎉🎉 🎉 "Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
Four years ago, we embarked on writing "Knowledge Graphs Applied" with a clear mission: to guide practitioners in implementing production-ready knowledge graph solutions. Drawing from our extensive field experience across multiple domains, we aimed to share battle-tested best practices that transcend basic use cases.
Like fine wine, ideas, and concepts need time to mature. During these four years of careful development, we witnessed a seismic shift in the technological landscape. Large Language Models (LLMs) emerged not just as a buzzword, but as a transformative force that naturally converged with knowledge graphs.
This synergy unlocked new possibilities, particularly in simplifying complex tasks like unstructured data ingestion and knowledge graph-based question-answering.
We couldn't ignore this technological disruption. Instead, we embraced it, incorporating our hands-on experience in combining LLMs with graph technologies. The result is "Knowledge Graphs and LLMs in Action" – a thoroughly revised work with new chapters and an expanded scope.
Yet our fundamental goal remains unchanged: to empower you to harness the full potential of knowledge graphs, now enhanced by their increasingly natural companion, LLMs. This book represents the culmination of a journey that evolved alongside the technology itself. It delivers practical, production-focused guidance for the modern era, in which knowledge graphs and LLMs work in concert.
Now available in MEAP, with new LLMs-focused chapters ready to be published.
#llms #knowledgegraph #graphdatascience
"Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"