7 Knowledge Representation Books That Separate Experts from Amateurs

Dean Allemang and James Hendler bring deep expertise to this exploration of Semantic Web modeling with RDFS and OWL. You gain a hands-on understanding of how to structure and query data using these languages, illustrated through inventive examples like Shakespeare's works to ground abstract concepts. The book systematically covers Semantic Web fundamentals, from RDF's role in distributing information to inferencing and vocabulary management with SKOS. If you’re working on domain modeling or looking to implement Semantic Web technologies, this book offers practical insights into turning chaotic data into interconnected knowledge frameworks.

The research was clear: traditional approaches to knowledge representation weren't capturing the complexity of real-world data effectively. Drawing on their deep expertise at the University of Southern California, Mayank Kejriwal, Craig Knoblock, and Pedro Szekely developed this authoritative text to map the evolving landscape of knowledge graphs within artificial intelligence. You’ll explore foundational theories alongside emerging techniques like deep learning applications, with detailed examples ranging from cyberattack prediction to COVID-19 research insights. This book is tailored for those aiming to grasp both the theoretical and practical aspects of knowledge graphs, particularly graduate students, AI practitioners, and data scientists looking to enhance their modeling skills.

This tailored book explores knowledge representation through the lens of your unique background and objectives, offering a deep dive into how AI systems conceptualize and organize information. It covers foundational theories, practical modeling techniques, and advanced reasoning approaches, all matched to your interests and expertise. By focusing on your specific goals, this personalized guide reveals how knowledge structures empower intelligent machines to mimic human understanding and decision-making. The book carefully unpacks complex ideas such as ontologies, semantic networks, and graph-based representations in ways that resonate with your learning style and aspirations. It also examines challenges and innovations in AI knowledge processing, aiming to deepen your mastery while connecting theory with application.

What happens when two leading experts in semantic technologies and graph databases join forces? Jesus Barrasa and Jim Webber draw on their extensive experience at Neo4j to guide you through building knowledge graphs that tackle real challenges like GDPR compliance and cybersecurity intelligence. You’ll learn how to structure interconnected data, import diverse datasets, and enhance your graphs with algorithms and machine learning techniques. Chapters explore practical patterns such as integration-and-search graphs and dependency graphs, making this a hands-on resource for data scientists and engineers aiming to transition from theory to production. If you need actionable insights on organizing and leveraging complex data relationships, this book delivers without unnecessary jargon.

Ronald J. Brachman and Hector J. Levesque bring decades of expertise in artificial intelligence to this curated collection that delves deeply into the foundations of knowledge representation. Instead of a typical textbook, this volume compiles seminal papers spanning 25 years, each framed with insightful introductions that clarify their impact and relevance. You'll explore key principles that underpin robust AI systems, from natural language processing to expert systems, gaining a nuanced understanding of how knowledge structures shape intelligent behavior. This book suits you especially well if you seek to grasp the theoretical underpinnings behind AI applications rather than quick practical fixes.

John F. Sowa challenges the conventional wisdom that knowledge representation is merely a technical exercise by weaving together logic, philosophy, linguistics, and computer science. You gain a deep understanding of how artificial intelligence techniques and database design converge to make knowledge explicit and computable. The first chapters ground you in logic and ontology, while later sections translate everyday language problems into formal models you can implement. This book suits advanced students and professionals who want a solid theoretical foundation alongside practical computational frameworks.

This tailored book leads you through a focused, step-by-step journey to build effective knowledge graphs within one month. It explores core concepts and practical techniques matched to your background, ensuring you grasp how to structure, connect, and apply graph data meaningfully. Covering topics from foundational graph theory to advanced coding practices, the content addresses your specific goals and interests with clarity and precision. By blending expert knowledge with your unique learning needs, the book reveals how knowledge graphs power data integration, reasoning, and AI applications. Its personalized pathway helps you synthesize complex ideas into actionable understanding, making the process of mastering knowledge graphs both engaging and achievable.

Drawing from his extensive experience leading the Cyc project, Douglas B. Lenat explores the complexities of building vast knowledge-based systems that can represent and reason with human-like understanding. You’ll gain insight into the architectural design and inference mechanisms that enable computers to process common sense knowledge, as detailed in chapters on representational frameworks and logic-based reasoning. This book suits those deeply involved in artificial intelligence research or software development focused on knowledge engineering, offering a rare, inside look at one of AI’s most ambitious efforts. While it demands a technical mindset, the depth of practical examples makes it a valuable reference for anyone tackling large-scale AI systems.

This collection, edited by Michael Cochez, Madalina Croitoru, Pierre Marquis, and Sebastian Rudolph, stems from the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning. It explores how graph-theoretic approaches bridge diverse knowledge representation communities, offering insights into complex reasoning frameworks. You'll find detailed examinations of graph structures applied to represent knowledge, alongside discussions on reasoning techniques that leverage these models. The selected papers serve those engaged in AI research or advanced applications of knowledge representation, providing a focused look at how graph theory underpins logical inference and semantic modeling.