4 New Knowledge Representation Books Reshaping AI in 2025

Par Lolatchi's exploration of knowledge representation takes a direct approach to defining what knowledge truly is and how you actively engage with it. You’ll find discussions that clarify the nature and function of knowledge itself, moving beyond abstract theory to practical understanding. The book sharpens your grasp of how knowledge operates in daily use, which can be particularly insightful if you're involved in fields like AI or information systems where knowledge structuring is crucial. With concise chapters spanning just under 90 pages, it suits those seeking a focused introduction rather than an exhaustive treatise.

When Hua Shi first explored the limitations of traditional Petri nets, he recognized the need for models that better handle uncertainty in knowledge systems. This book introduces enhanced fuzzy Petri net models that deepen your understanding of knowledge representation and reasoning under ambiguity, particularly in organizational contexts. You'll find detailed chapters on improved FPN structures and practical examples that demonstrate their application in knowledge management and decision-making. If you're involved in knowledge-intensive industries or academic research, this work offers insights to refine your approach to modeling complex information flows and sustaining competitive advantages.

This tailored book explores the latest breakthroughs and challenges in knowledge representation as they stand in 2025, focusing specifically on emerging AI-driven approaches. It examines cutting-edge developments such as advanced graph-based models, dynamic uncertainty management, and innovative reasoning techniques. By matching your background and interests, the content delves deeper into the aspects of knowledge representation that matter most to you, helping you stay ahead in this rapidly evolving field. The book reveals how new theories and tools are transforming how machines process and understand complex information, all tailored to address your specific goals and knowledge level.

Chandra M addresses the growing complexity of access control in cybersecurity by focusing on how knowledge representation can streamline policy management. You’ll get a clear understanding of how access control lists (ACLs) and role-based access control (RBAC) models can be organized using ontologies and semantic networks to capture relationships among users, resources, and permissions. For example, the book breaks down how RBAC simplifies large-scale permission assignments by structuring roles systematically, making it easier to update and enforce security policies. If you’re involved in designing or managing secure systems, this book offers practical frameworks to help you automate and integrate access control effectively, though it’s best suited for those with some background in security concepts.

After extensive work with semantic technologies, Jesus Barrasa and Jim Webber crafted this guide to demystify knowledge graphs and make them accessible for data professionals. You learn to build knowledge graphs from the ground up, with clear examples covering integration, search, pattern detection, and dependency graphs, plus how to enrich them with machine learning. The book dives into practical uses across domains like medical research and cybersecurity, showing how graph databases underpin knowledge representation. If you're a data scientist or engineer eager to move beyond theory and deploy knowledge graphs effectively, this book offers a solid foundation without unnecessary jargon or fluff.