Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges
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Knowledge Graphs For Explainable Artificial Intelligence
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Author : Ilaria Tiddi
language : en
Publisher:
Release Date : 2020
Knowledge Graphs For Explainable Artificial Intelligence written by Ilaria Tiddi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Knowledge Graphs And Llms
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Author : HAWKE. NEXON
language : en
Publisher: Independently Published
Release Date : 2025-05-21
Knowledge Graphs And Llms written by HAWKE. NEXON and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-21 with Computers categories.
Knowledge Graphs and LLMs: Building Intelligent, Explainable, and Context-Aware AI Systems Unlock the full potential of artificial intelligence by seamlessly integrating Knowledge Graphs with Large Language Models (LLMs) to build smarter, explainable, and context-aware AI systems. This comprehensive guide empowers data scientists, AI engineers, and researchers to harness the synergy of structured knowledge and advanced natural language understanding, creating AI applications that reason, explain, and adapt like never before. What You'll Learn: - Foundations of Knowledge Graphs, LLMs, and their hybrid architectures - Techniques to enhance AI explainability and trustworthiness through transparent reasoning - Scalable system designs for deploying robust Knowledge Graph + LLM solutions - Advanced graph indexing, vector databases, and retrieval optimization for performance at scale - Real-world applications in healthcare, finance, legal, and research domains - Ethical considerations, bias mitigation, and AI governance best practices Why This Book? Unlike other AI texts, this book uniquely focuses on the intersection of Knowledge Graphs and LLMs, providing practical insights into how these technologies combine to overcome challenges in explainability, context awareness, and scalability. Featuring hands-on projects, real-world case studies, and clear code examples, it guides you from foundational concepts to cutting-edge implementations. Who This Book Is For: - AI/ML Engineers and Developers seeking to build intelligent, explainable systems - Data Scientists and Knowledge Engineers working with semantic web and graph data - Researchers and Graduate Students specializing in AI, NLP, and graph technologies - Technical leaders aiming to deploy scalable and trustworthy AI solutions Master the art of building AI that truly understands and explains its decisions - revolutionize your approach with Knowledge Graphs and LLMs.
Explainable Ai Foundations Methodologies And Applications
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Author : Mayuri Mehta
language : en
Publisher: Springer
Release Date : 2022-12-05
Explainable Ai Foundations Methodologies And Applications written by Mayuri Mehta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-05 with Technology & Engineering categories.
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Artificial Intelligence Foundations And Applications
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Author : Siva Sankar Namani
language : en
Publisher: Archers & Elevators Publishing House
Release Date :
Artificial Intelligence Foundations And Applications written by Siva Sankar Namani and has been published by Archers & Elevators Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on with Antiques & Collectibles categories.
Artificial Intelligence Foundations Applications And Future Directions
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Author : Ahmet Gürkan YÜKSEK•
language : en
Publisher: Livre de Lyon
Release Date : 2025-03-23
Artificial Intelligence Foundations Applications And Future Directions written by Ahmet Gürkan YÜKSEK• and has been published by Livre de Lyon this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-23 with Computers categories.
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.
Explainable Ai Interpreting Explaining And Visualizing Deep Learning
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Author : Wojciech Samek
language : en
Publisher: Springer Nature
Release Date : 2019-09-10
Explainable Ai Interpreting Explaining And Visualizing Deep Learning written by Wojciech Samek and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with Computers categories.
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Explainable Artificial Intelligence A Practical Guide
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Author : Parikshit Narendra Mahalle
language : en
Publisher: CRC Press
Release Date : 2024-12-02
Explainable Artificial Intelligence A Practical Guide written by Parikshit Narendra Mahalle and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-02 with Computers categories.
This book explores the growing focus on artificial intelligence (AI) systems in both industry and academia. It evaluates and justifies AI applications while enhancing trust in AI outcomes and aiding comprehension of AI feature development. Key topics include an overview of explainable AI, black box model understanding, interpretability techniques, practical XAI applications, and future trends and challenges in XAI. Technical topics discussed in the book include: Explainable AI overview Understanding black box models Techniques for model interpretability Practical applications of XAI Future trends and challenges in XAI
The Artificial Intelligence And Machine Learning Blueprint Foundations Frameworks And Real World Applications
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Author : Priyambada Swain
language : en
Publisher: Deep Science Publishing
Release Date : 2025-08-06
The Artificial Intelligence And Machine Learning Blueprint Foundations Frameworks And Real World Applications written by Priyambada Swain and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-06 with Computers categories.
In the current era of data-centric transformation, Artificial Intelligence (AI) and Machine Learning (ML) are influencing organizational strategies and operations. The AI and Machine Learning Blueprint serves as a guide connecting academic concepts with industry applications. It is intended for both students seeking basic knowledge and professionals interested in deploying scalable AI systems. The book covers core mathematical principles relevant to AI, including linear algebra, probability, statistics, and optimization, and provides an overview of classical machine learning algorithms, neural networks, and reinforcement learning. Concepts are illustrated with practical examples, Python code, and case studies from sectors such as healthcare, finance, cybersecurity, natural language processing, and computer vision. Operational considerations are also addressed, with chapters on MLOps, model deployment, explainable AI (XAI), and ethics. The text concludes with information on emerging topics including generative AI, federated learning, and artificial general intelligence (AGI). With a blend of theoretical depth and practical relevance, this book is an essential blueprint for mastering AI and ML in today’s intelligent systems landscape.
Knowledge Graphs And Llms In Action
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Author : Alessandro Negro
language : en
Publisher: Simon and Schuster
Release Date : 2025-11-18
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 2025-11-18 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 Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.