Mastering Knowledge Graphs For Llms
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Mastering Knowledge Graphs For Llms
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Author : Mathias Sandgreen
language : en
Publisher: Independently Published
Release Date : 2024-12-14
Mastering Knowledge Graphs For Llms written by Mathias Sandgreen and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-14 with Computers categories.
Mastering Knowledge Graphs for LLMs: Building Smarter RAG Pipelines What if your AI systems could think smarter, faster, and more contextually? This groundbreaking book explores the transformative power of knowledge graphs in shaping next-generation Retrieval-Augmented Generation (RAG) pipelines. Bridging the gap between structured reasoning and generative AI, it teaches how knowledge graphs elevate large language models (LLMs) by delivering precise, context-aware, and factually grounded responses. What you'll discover: How to design and build robust knowledge graphs tailored for AI applications. Cutting-edge techniques to integrate graphs with LLMs in RAG pipelines. Real-world applications in domains like healthcare, finance, and customer service. Solutions to address key challenges like scaling, privacy, and reducing LLM hallucinations. Emerging trends and research opportunities that will shape the future of AI and knowledge graphs. Packed with actionable insights, practical advice, and clear explanations, this book is your ultimate guide to mastering the synergy between knowledge graphs and LLMs. Ready to revolutionize your AI systems? Order this essential resource and start building smarter RAG pipelines today!
Mastering Knowledge Graphs And Llm Integration
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Author : Damian Zion
language : en
Publisher: Independently Published
Release Date : 2025-11-11
Mastering Knowledge Graphs And Llm Integration written by Damian Zion and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-11 with Computers categories.
This book is a comprehensive guide to the future of intelligent systems - where Knowledge Graphs meet Large Language Models (LLMs) to create AI that understands, reasons, and explains. It explores the complete journey from foundational concepts to advanced architectures, showing how structured knowledge and neural intelligence work together to power context-aware, trustworthy, and scalable AI applications. Written with the precision of an AI researcher and the clarity of a software engineer, Mastering Knowledge Graphs and LLM Integration bridges academic theory and real-world practice. Each chapter is backed by practical code examples, real industry use cases, and proven deployment templates used in enterprise AI environments. This book delivers not just knowledge - but implementation confidence, rooted in authentic, production-tested systems. About the Technology: Knowledge Graphs provide the structured backbone of reasoning - representing entities, relationships, and context. Large Language Models bring the semantic understanding that allows systems to communicate naturally. When fused, they form a new class of hybrid AI systems capable of contextual inference, explainability, and long-term memory. The book covers modern graph frameworks (Neo4j, GraphDB, RDFLib), hybrid reasoning paradigms (SPARQL + LLMs, GraphRAG), and integration strategies that transform traditional AI workflows into explainable, cognitive systems. What's Inside: Inside these pages, you'll learn to: Design and build semantic knowledge graphs for hybrid AI reasoning. Integrate LLMs with graph databases using Python, LangChain, and Neo4j. Engineer context-aware, explainable AI pipelines for real-world applications. Deploy scalable KG-LLM systems using Docker, Kubernetes, and Helm. Evaluate factual accuracy, consistency, and explainability using advanced metrics. Every chapter includes authentic, working examples - from building your first ontology to orchestrating graph-grounded RAG pipelines for cognitive assistants. Who This Book Is For: This book is written for AI engineers, data scientists, software architects, and researchers who want to move beyond pure neural networks and build structured, intelligent systems. Whether you're designing enterprise search engines, intelligent assistants, or autonomous reasoning agents, this book will help you architect the foundations of trustworthy, graph-integrated AI. AI is shifting faster than any technology before it. Companies and researchers that adopt hybrid intelligence early - systems that can reason, explain, and adapt - will define the next decade of innovation. Staying with black-box models is no longer enough; the future belongs to explainable, structured, and self-aware AI systems. This book gives you the roadmap to build them today. This is more than a technical manual - it's a professional accelerator. Every concept, tool, and workflow in this book is geared toward building production-ready systems that deliver real business and research impact. By mastering the integration of knowledge and language, you'll position yourself at the forefront of AI innovation - where understanding meets intelligence. If you're ready to go beyond black-box AI and start building intelligent, explainable, and self-evolving systems, this is the book for you. Get your copy of Mastering Knowledge Graphs and LLM Integration today - and start shaping the architecture of tomorrow's cognitive AI.
Knowledge Graphs And Llms In Action
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Author : Alessandro Negro
language : en
Publisher: Simon and Schuster
Release Date : 2025-11-11
Knowledge Graphs And Llms In Action written by Alessandro Negro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-11 with Computers categories.
Combine knowledge graphs with large language models to deliver powerful, reliable, and explainable AI solutions. Knowledge graphs model relationships between the objects, events, situations, and concepts in your domain so you can readily identify important patterns in your own data and make better decisions. Paired up with large language models, they promise huge potential for working with structured and unstructured enterprise data, building recommendation systems, developing fraud detection mechanisms, delivering customer service chatbots, or more. This book provides tools and techniques for efficiently organizing data, modeling a knowledge graph, and incorporating KGs into the functioning of LLMs—and vice versa. 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 • Build knowledge graphs from unstructured data sources using LLMs • Use machine learning algorithms to complete your graphs and derive insights from it • Reason on the knowledge graph and build KG-powered RAG systems for LLMs In Knowledge Graphs and LLMs in Action, you’ll discover the theory of knowledge graphs then put them into practice with LLMs to build working intelligence systems. You’ll learn to create KGs from first principles, go hands-on to develop advisor applications for real-world domains like healthcare and finance, build retrieval augmented generation for LLMs, and more. About the technology Using knowledge graphs with LLMs reduces hallucinations, enables explainable outputs, and supports better reasoning. By naturally encoding the relationships in your data, knowledge graphs help create AI systems that are more reliable and accurate, even for models that have limited domain knowledge. About the book Knowledge Graphs and LLMs in Action shows you how to introduce knowledge graphs constructed from structured and unstructured sources into LLM-powered applications and RAG pipelines. Real-world case studies for domain-specific applications—from healthcare to financial crime detection—illustrate how this powerful pairing works in practice. You’ll especially appreciate the expert insights on knowledge representation and reasoning strategies. What's inside • Design knowledge graphs for real-world needs • Build KGs from structured and unstructured data • Apply machine learning to enrich, complete, and analyze graphs • Pair knowledge graphs with RAG systems About the reader For ML and AI engineers, data scientists, and data engineers. Examples in Python. About the author Alessandro Negro is Chief Scientist at GraphAware and author of Graph-Powered Machine Learning. Vlastimil Kus, Giuseppe Futia, and Fabio Montagna are seasoned ML and AI professionals specializing in Knowledge Graphs, Large Language Models, and Graph Neural Networks. Table of Contents Part 1 1 Knowledge graphs and LLMs: A killer combination 2 Intelligent systems: A hybrid approach Part 2 3 Create your first knowledge graph from ontologies 4 From simple networks to multisource integration Part 3 5 Extracting domain-specific knowledge from unstructured data 6 Building knowledge graphs with large language models 7 Named entity disambiguation 8 NED with open LLMs and domain ontologies Part 4 9 Machine learning on knowledge graphs: A primer approach 10 Graph feature engineering: Manual and semiautomated approaches 11 Graph representation learning and graph neural networks 12 Node classification and link prediction with GNNs Part 5 13 Knowledge graph–powered retrieval-augmented generation 14 Asking a KG questions with natural language 15 Building a QA agent with LangGraph
Mastering Knowledge Graphs For Ai
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Author : Alex Zhen
language : en
Publisher: Independently Published
Release Date : 2025-07-25
Mastering Knowledge Graphs For Ai written by Alex Zhen and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.
Knowledge Graphs for AI: A Comprehensive Guide to Constructing and Utilizing Graph-Based Reasoning Systems is an indispensable resource for AI practitioners, data scientists, and engineers seeking to harness the power of knowledge graphs to build intelligent, data-driven AI systems. This book provides a step-by-step approach to constructing knowledge graphs from real-world datasets, enabling advanced reasoning and enhanced insights for AI applications. Covering core concepts such as entities, relationships, semantic models, and ontologies, it offers hands-on tutorials using industry-leading tools like Neo4j, SPARQL, RDF, and OWL. With real-world case studies in healthcare, finance, and e-commerce, this book demonstrates how knowledge graphs revolutionize natural language processing (NLP), recommendation systems, and fraud detection. Readers will master graph querying, inference techniques, graph embeddings, and machine learning on graphs, while learning strategies for scaling and deploying production-ready graph systems. Packed with practical examples and cutting-edge techniques, this book is a vital guide for creating scalable, intelligent AI solutions that leverage structured data and semantic reasoning. What's Inside Knowledge Graph Fundamentals: Explore entities, relationships, and properties to build robust graph structures. Graph Schema and Ontology Design: Learn to create effective schemas and ontologies for structured data representation. Data Integration: Master techniques for ingesting and integrating diverse data sources into knowledge graphs. Querying with SPARQL and Beyond: Dive into querying techniques to extract actionable insights from graphs. Graph Reasoning: Apply inference and deduction methods to derive new knowledge for AI applications. Semantic Models: Utilize RDF, OWL, and Schema.org to build semantic knowledge graphs. AI Integration: Enhance NLP, recommendation systems, and fraud detection with knowledge graph-driven insights. Real-World Case Studies: Analyze applications in healthcare, finance, and e-commerce for practical understanding. Advanced Techniques: Implement graph embeddings and machine learning for cutting-edge AI solutions. Production Deployment: Learn strategies for scaling and deploying knowledge graphs in production environments. Who This Book Is For This book is tailored for AI practitioners, data scientists, machine learning engineers, and technical leads working on data-driven AI solutions. Whether you're new to knowledge graphs or an experienced developer looking to integrate graph-based reasoning into NLP, recommendation systems, or fraud detection, this book provides a clear, structured path to mastering complex concepts. It's ideal for professionals in industries like healthcare, finance, and e-commerce who aim to leverage knowledge graphs for intelligent, scalable AI applications. Why You Should Buy This Book Knowledge graphs are at the forefront of AI innovation, enabling structured data representation and advanced reasoning for next-generation applications. Knowledge Graphs for AI offers a practical, hands-on guide to building and utilizing knowledge graphs with tools like Neo4j, SPARQL, and RDF, ensuring your AI systems deliver precise, context-aware insights. With detailed tutorials, real-world case studies, and advanced techniques like graph embeddings and production scaling, this book equips you to create intelligent systems that excel in healthcare, finance, e-commerce, and beyond. Stay ahead in the AI-driven world by mastering knowledge graphs and transforming your data into powerful, reasoning-driven solutions. Don't miss this opportunity to elevate your skills and build impactful, scalable AI
Proceedings Of The 2024 5th International Conference On Modern Education And Information Management Icmeim 2024
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Author : Donghui Hu
language : en
Publisher: Springer Nature
Release Date : 2024-11-26
Proceedings Of The 2024 5th International Conference On Modern Education And Information Management Icmeim 2024 written by Donghui Hu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-26 with Education categories.
This book is open access. Focusing on education and information management with modernization, ICMEIM 2024 provides a platform for scholars in related fields to exchange and share information, discuss how the two affect each other, and: · Promote the modernization of education by studying certain educational issues that exist. · Open up new perspectives, broaden horizons, and examine the issues under discussion by participants. · Create a forum for sharing, research and exchange at an international level, where participants will be informed of the latest research directions, results and content in different fields, thus inspiring them to come up with new research ideas. The organizing committee of conference is delighted to invite you to participate in this exciting event, which also paves way for young researchers in acquiring knowledge and information by meeting the experts.
Knowledge Science Engineering And Management
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Author : Tianqing Zhu
language : en
Publisher: Springer Nature
Release Date : 2025-11-16
Knowledge Science Engineering And Management written by Tianqing Zhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-16 with Computers categories.
The six-volume proceedings set LNAI 15919, 15920, 15921, 15922, 15923 and 15924 constitutes the refereed proceedings of the 18th International Conference on Knowledge Science, Engineering and Management, KSEM 2025, held in Macao, China during August 4–7, 2025. The 106 papers and 66 short papers are included in these proceedings were carefully reviewed and selected from 354 submissions. They focus on all aspects of the exchange of research in artificial intelligence, data science, knowledge engineering, AI safety, large language models, and related frontier areas.
Challenges And Algorithms For Knowledge Discovery From Data
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Author : Matthijs van Leeuwen
language : en
Publisher: Springer Nature
Release Date : 2025-09-22
Challenges And Algorithms For Knowledge Discovery From Data written by Matthijs van Leeuwen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-22 with Computers categories.
Arno Siebes graduated in Mathematics from Utrecht University in 1983. He joined CWI in Amsterdam in 1985, and obtained his Ph.D. in 1990 from Twente University. In 2000, he joined Utrecht University, where he took up the chair for Large Distributed Databases, which was later renamed to Algorithmic Data Analysis. He supervised 15 Ph.D. students, some of whom themselves became professors. His key research work has been on data mining and inductive databases. His most impactful contribution is using the minimum description length (MDL) principle for pattern mining, the algorithm known as Krimp led to an important subdomain in data mining. Arno has been a key member of the European data mining and machine learning community. In addition to his work on the Intelligent Data Analysis symposia, he was program co-chair of the first co-located edition of the European Conference on Machine Learning (ECML) and European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) and played a key role in the development of this thriving event. Throughout his research and teaching career, Arno has maintained the philosophy that theory should work in practice. The contributions in this Festschrift serve as a reminder of his successes as a researcher and mentor. The chapters are categorized into topical sections on pattern mining, learning and reasoning, and large language models.
Artificial Intelligence In Education
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Author : Alexandra I. Cristea
language : en
Publisher: Springer Nature
Release Date : 2025-08-21
Artificial Intelligence In Education written by Alexandra I. Cristea and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-21 with Computers categories.
This six-volume set LNAI 15877-15882 constitutes the refereed proceedings of the 26th International Conference on Artificial Intelligence in Education, AIED 2025, held in Palermo, Italy, during July 22–26, 2025. The 130 full papers and 129 short papers presented in this book were carefully reviewed and selected from 711 submissions. The conference program comprises seven thematic tracks: Track 1: AIED Architectures and Tools Track 2: Machine Learning and Generative AI: Emphasising datadriven Track 3: Learning, Teaching, and Pedagogy Track 4: Human-Centred Design and Design-Based Research Track 5: Teaching AI Track 6: Ethics, Equity, and AIED in Society Track 7: Theoretical Aspects of AIED and AI-Based Modelling for Education
Man Machine Environment System Engineering
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Author : Shengzhao Long
language : en
Publisher: Springer Nature
Release Date : 2025-10-22
Man Machine Environment System Engineering written by Shengzhao Long and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-22 with Technology & Engineering categories.
This book includes best papers selected from more than 500 papers submitted at 25th International Conference on Man-Machine-Environment System Engineering (MMESE) 2025. It covers the best research topics and the latest development trends in MMESE theory and application. MMESE is a scientific study of the design concepts and quantitative analysis of a complex giant system using physiology, psychology, system engineering, computer science, environment science, management theory, education, and other related disciplines methods. MMESE focuses mainly on the relationship and the optimum combination between man, machine, and environment. The three optimized goals of the MMESE study are safety, efficiency, and economy. Researchers and professionals who study a human-centered interdisciplinary subject crossing above disciplines will be mostly benefited from the proceedings. In 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian, Man-Machine-Environment System Engineering (MMESE), the integrated and advanced science research topic was established in China by Professor Shengzhao Long. In the letter to Shengzhao Long on October 22, 1993, Xuesen Qian wrote: “You have created a very important modern science subject and technology in China!”.
Mastering Graph Rag Foundations
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Author : Finn Cordex
language : en
Publisher: Independently Published
Release Date : 2025-11-24
Mastering Graph Rag Foundations written by Finn Cordex and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-24 with Computers categories.
Unlock the full power of Retrieval-Augmented Generation (RAG) with knowledge graphs, vector search, and large language models (LLMs) in this definitive guide for AI engineers, developers, and data scientists. Mastering Graph-RAG Foundations takes you from conceptual understanding to practical mastery, offering a structured, hands-on approach to designing AI systems that can intelligently retrieve, reason, and generate knowledge. Whether you're building advanced chatbots, knowledge-intensive agents, or production-grade AI workflows, this book equips you with the tools and frameworks you need to succeed. Inside, you'll discover: How knowledge graphs enhance RAG workflows for accurate and context-aware AI outputs. Step-by-step guidance on vector search, embeddings, and LLM integration. Hands-on Python and LangGraph examples to implement real-world RAG systems. Practical insights into designing scalable, maintainable AI architectures. Expert commentary, best practices, and caveats from a senior AI engineer's perspective. Designed for advanced learners and technical professionals, this book bridges the gap between theory and practice. Start your journey to mastering Graph-RAG today and unlock new levels of AI system intelligence and reliability.