Machine Learning With Knowledge Graphs For Explainable Artificial Intelligence

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Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges

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Author : Freddy Lécué
language : en
Publisher: SAGE Publications Limited
Release Date : 2020-05-06

Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges written by Freddy Lécué and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-06 with Computers categories.

The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.

Machine Learning With Knowledge Graphs For Explainable Artificial Intelligence

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Author : Yushan Liu
language : en
Publisher:
Release Date : 2024

Machine Learning With Knowledge Graphs For Explainable Artificial Intelligence written by Yushan Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.

Towards Explainable Artificial Intelligence Xai Based On Contextualizing Data With Knowledge Graphs

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Author : Md Kamruzzaman Sarker
language : en
Publisher:
Release Date : 2020

Towards Explainable Artificial Intelligence Xai Based On Contextualizing Data With Knowledge Graphs written by Md Kamruzzaman Sarker 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.

Artificial intelligence (AI)---including the sub-fields of machine learning and deep learning has advanced considerably in recent years. In tandem with these performance improvements, understanding how AI systems make decisions has become increasingly difficult due to many nonlinear transformations of input data and the complex nature of the algorithms involved. Explainable AI (XAI) are the techniques to examine these decision processes. A main desideratum of XAI is user understandability, while explanations should take into account the context and domain knowledge of the problem. Humans understand and reason mostly in terms of concepts and combinations thereof. A knowledge graph (KG) embodies such understanding in links between concepts; such a natural conceptual network creates a pathway to use knowledge graphs in XAI applications to improve overall understandability of complex AI algorithms. Over the course of this dissertation, we outline a number of contributions towards explaining the AI decision in a human friendly way. We show a proof-of-concept on how domain knowledge can be used to analyze the input and output data of AI algorithms. We materialize the domain knowledge into knowledge graph (more technically ontology) and by using concept induction algorithm find the pattern between input and output. After demonstrating this, we start to experiment on a large scale, as we found that the current state of the art concept induction algorithm does not scale well with large amounts of data. To solve this runtime issue, we develop a new algorithm efficient concept induction (ECII), which improves the runtime significantly. During this process, we also find that current tools are not adequate to create and edit the knowledge graphs, as well as that there is scarcity to quality knowledge graph. We make the creation and editing process easier, by creating OWLAx and ROWLTab plugin for the industry-standard ontology editor, Protégé. We also develop a large knowledge graph from the Wikipedia category hierarchy. Overall, these research contributions improved the software support to create knowledge graph, developed a better knowledge graph, and showed a new direction on how AI decision making can be explained by using a contextual knowledge graph.

Semantic Ai In Knowledge Graphs

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Author : Sanju Tiwari
language : en
Publisher: CRC Press
Release Date : 2023-08-21

Semantic Ai In Knowledge Graphs written by Sanju Tiwari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-21 with Computers categories.

Recent combinations of semantic technology and artificial intelligence (AI) present new techniques to build intelligent systems that identify more precise results. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to extend the knowledge graphs by graph mapping or corpus-based ontology learning. Securing efficient results via the combination of symbolic AI and statistical AI such as entity extraction based on machine learning, text mining methods, semantic knowledge graphs, and related reasoning power, this book is the first of its kind to explore semantic AI and knowledge graphs. A range of topics are covered, from neuro-symbolic AI, explainable AI and deep learning to knowledge discovery and mining, and knowledge representation and reasoning. A trailblazing exploration of semantic AI in knowledge graphs, this book is a significant contribution to both researchers in the field of AI and data mining as well as beginner academicians.

Semantic Computing And Ai Transforming Knowledge Into Intelligence

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Author : Florian Schimanke
language : en
Publisher: World Scientific
Release Date : 2025-09-26

Semantic Computing And Ai Transforming Knowledge Into Intelligence written by Florian Schimanke and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-26 with Computers categories.

This collection reflects key milestones in research, addressing fundamental theories, emerging trends, and real-world applications of two closely connected disciplines of computer science: semantic computing and artificial intelligence (AI).The rapid evolution of computing has led to transformative advancements in semantic computing and AI simultaneously. Both fields have driven innovation in data processing, knowledge representation, and intelligent decision-making. This book presents a carefully curated selection of articles from the International Journal of Semantic Computing (IJSC), spanning from 2007 to 2025, capturing the journey in which the two disciplines became more and more connected, while expanding our understanding of machine intelligence.The intersection of semantic computing and AI has become increasingly significant, offering sophisticated solutions to complex computational problems and enhancing the way machines interpret and interact with human knowledge. This book serves as a valuable resource for researchers, practitioners, and students seeking to understand the evolution and impact of semantic computing and AI.

Knowledge Graph Mastery

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Author : Gilbert Huie
language : en
Publisher: Independently Published
Release Date : 2025-03-24

Knowledge Graph Mastery written by Gilbert Huie and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-24 with Computers categories.

Artificial intelligence is evolving-and so should the way we build it. In a world driven by data, intelligence comes not just from the volume of information, but from the connections we can make between concepts. Knowledge graphs are at the forefront of this evolution, powering smarter AI systems that understand, reason, and adapt. From enterprise search to recommendation engines, from fraud detection to AI assistants, knowledge graphs enable machines to move from pattern recognition to true contextual understanding. Knowledge Graph Mastery is the definitive guide for building intelligent, scalable AI systems grounded in structured, semantic knowledge. Whether you're a software developer, AI engineer, data scientist, architect, or researcher, this book equips you with the tools, concepts, and real-world examples you need to master the design and deployment of modern knowledge-driven applications. This comprehensive, practical book takes you from foundational principles to advanced graph reasoning techniques. You'll explore how to structure and interlink information, model domain-specific ontologies, query complex graphs efficiently, and integrate them with machine learning workflows. Inside, you'll learn how to: Understand graph theory and graph thinking as a foundation for AI Design semantic models using RDF, OWL, SHACL, and linked data principles Build and query scalable knowledge graphs using SPARQL, Cypher, and Gremlin Implement entity resolution, data enrichment, and graph-based ETL pipelines Apply graph reasoning, inference, and logic to build explainable AI systems Leverage knowledge graphs in real-world AI solutions-from chatbots and digital twins to recommendation systems, fraud detection, and multimodal reasoning Choose the right graph database technology for your use case (Neo4j, Amazon Neptune, ArangoDB, etc.) Integrate knowledge graphs with machine learning models using graph embeddings and hybrid AI techniques This book doesn't just teach you what knowledge graphs are-it shows you how to make them work in production environments, across sectors, at scale. Whether you're architecting intelligent search, powering enterprise knowledge hubs, or enabling human-like reasoning in machines, Knowledge Graph Mastery is your blueprint for designing AI that knows what it's doing. Don't just build AI that reacts-build AI that understands. Master knowledge graphs. Design smarter systems. Start now.

Building Intelligent Systems With Knowledge Graphs

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Author : RICARDO. HOLMES
language : en
Publisher: Independently Published
Release Date : 2025-01-26

Building Intelligent Systems With Knowledge Graphs written by RICARDO. HOLMES and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-26 with Computers categories.

Building Intelligent Systems with Knowledge Graphs: Tools, Techniques, and Use Cases Overview: Building Intelligent Systems with Knowledge Graphs is your comprehensive guide to understanding, building, and applying knowledge graphs for modern AI-driven systems. This book demystifies the core concepts behind knowledge graphs, explores the tools and techniques needed for implementation, and provides practical examples for real-world use cases. Whether you're a data scientist, AI engineer, or business professional seeking to leverage connected data for smarter decision-making, this book equips you with the essential skills to design, develop, and deploy intelligent systems powered by knowledge graphs. By the end of this book, you'll have a deep understanding of how knowledge graphs enhance AI models, support explainable AI (XAI), and enable data-driven insights across industries like healthcare, finance, and smart cities. This book takes a hands-on approach to teaching knowledge graph concepts, combining theoretical insights with practical tutorials. You'll learn how to build a knowledge graph from scratch, integrate diverse data sources, query data using SPARQL and Cypher, and visualize insights for effective decision-making. Advanced topics like reasoning, entity resolution, and graph-based AI applications are also covered, ensuring you're prepared for both foundational and cutting-edge implementations. Key Features of This Book: Step-by-Step Guidance: Clear, structured tutorials for building and querying knowledge graphs. Practical Code Examples: Hands-on coding with SPARQL, Cypher, and graph tools like Neo4j and Amazon Neptune. Real-World Use Cases: Explore how knowledge graphs power AI in industries such as healthcare, finance, and smart cities. Advanced Topics: Learn about entity resolution, reasoning, and graph-based explainable AI (XAI). Scalable Solutions: Understand how to design and deploy large-scale knowledge graphs in distributed environments. Target Audience: AI and Data Professionals: Data scientists, machine learning engineers, and AI researchers seeking to enhance model performance with structured data. Business and Domain Experts: Decision-makers and industry professionals aiming to leverage graph technology for smarter insights. Software Engineers and Developers: Engineers looking to implement knowledge graphs in modern applications. Students and Researchers: Academics exploring the role of graphs in data science and artificial intelligence. Unlock the power of connected data! Start your journey with Building Intelligent Systems with Knowledge Graphs today and discover how to transform raw information into actionable insights for your AI-driven projects. Whether you're just starting or expanding your expertise, this book is your essential companion for mastering the world of knowledge graphs.

Reasoning Web Explainable Artificial Intelligence

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Author : Markus Krötzsch
language : en
Publisher: Springer Nature
Release Date : 2019-09-17

Reasoning Web Explainable Artificial Intelligence written by Markus Krötzsch and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-17 with Computers categories.

This volume contains lecture notes of the 15th Reasoning Web Summer School (RW 2019), held in Bolzano, Italy, in September 2019. The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.

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

Scala Machine Learning Projects

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Author : Md. Rezaul Karim
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-31

Scala Machine Learning Projects written by Md. Rezaul Karim and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-31 with Computers categories.

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. Key Features Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework. Book Description Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment. What you will learn Apply advanced regression techniques to boost the performance of predictive models Use different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniques Train Deep Neural Networks (DNN) using H2O and Spark ML Utilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application Learn how to use autoencoders to develop a fraud detection application Implement LSTM and CNN models using DeepLearning4j and MXNet Who this book is for If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.