Knowledge-Infused Learning
Neurosymbolic AI for Explainability, Interpretability, and Safety
Published on 7. May 2026
Book
Hardback
324 pages
978-1-009-51374-6 (ISBN)
Description
Knowledge-infused learning directly confronts the opacity of current 'black-box' AI models by combining data-driven machine learning techniques with the structured insights of symbolic AI. This guidebook introduces the pioneering techniques of neurosymbolic AI, which blends statistical models with symbolic knowledge to make AI safer and user-explainable. This is critical in high-stakes AI applications in healthcare, law, finance, and crisis management. The book brings readers up to speed on advancements in statistical AI, including transformer models such as BERT and GPT, and provides a comprehensive overview of weakly supervised, distantly supervised, and unsupervised learning methods alongside their knowledge-enhanced variants. Other topics include active learning, zero-shot learning, and model fusion. Beyond theory, the book presents practical considerations and applications of neurosymbolic AI in conversational systems, mental health, crisis management systems, and social and behavioral sciences, making it a pragmatic reference for AI system designers in academia and industry.
Reviews / Votes
'Professor Amit Sheth is a leading expert in knowledge-infused learning. The topics covered by this book are important to advance state-of-the-art AI. As our understanding of generative AI deepens, we ask what the next frontiers of AI are. This timely book offers a refreshing answer that explores AI research beyond large language models.' Huan Liu, Arizona State University 'This timely and insightful book by Manas Gaur and Amit Sheth combines data-driven AI with structured human knowledge, creating a practical pathway toward transparent and safe AI. Addressing critical gaps in AI's explainability and interpretability, especially in healthcare and crisis management, the authors introduce 'Knowledge-infused Learning'-an essential approach for human-centric AI. Their innovative frameworks, like CREST, are thoughtfully designed for real-world impact. For anyone deeply engaged in multimodal AI, digital health, or responsible technology use, this book is a must-read guide, offering robust technical foundations and thoughtful ethical considerations crucial for equitable AI solutions.' Ramesh Jain, University of California, Irvine 'Knowledge-Infused Learning is a timely and essential guide to building AI systems that are not only powerful, but also interpretable and trustworthy. Gaur and Sheth brilliantly show how integrating human knowledge with machine learning leads to more explainable, safer, and more responsible AI. A must-read for anyone shaping the future of intelligent systems.' Craig Knoblock, Information Sciences Institute, University of Southern California 'My 1997 book, Intelligent Systems for Engineering: A Knowledge-Based Approach, briefly discussed the need to integrate neural networks with knowledge-based reasoning. It is gratifying to see Manas carry this vision forward. His formulation of knowledge-infused learning resonates strongly with what DARPA later termed the 'third wave' of AI systems capable of contextual adaptation, reasoning, and explainability. I believe that knowledge-infused learning serves as the operational process for achieving neuro-symbolic integration, effectively catalyzing the transition into the third AI wave. In an era dominated by opaque models, this work is a timely reminder that grounding and trust remain central. I believe it will inspire students and researchers to build AI systems that are not only powerful but also truly understandable and socially responsible.' Ram Sriram, NISTMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Illustrations
Worked examples or Exercises
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 22 mm
Weight
622 gr
ISBN-13
978-1-009-51374-6 (9781009513746)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Manas Gaur is an assistant professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He earned his Ph.D. in 2022 from the University of South Carolina's Artificial Intelligence Institute, studying under Dr. Amit P. Sheth. A pioneer in knowledge-infused learning (2016-2022), Gaur's research has earned multiple best paper awards and recognition through USC Eminent Profiles and AAAI New Faculty Highlights. His cutting-edge work continues to attract major funding, including grants from NSF and EPSRC-UKRI in partnership with the Alan Turing Institute. Amit P. Sheth is the NCR Chair and Professor of Computer Science and Engineering at the University of South Carolina, where he founded the university-wide AI Institute in 2019 and grew it to nearly 50 AI researchers in four years. He is a fellow of IEEE, AAAI, AAAS, ACM, and AIAA. His awards include the IEEE CS Wallace McDowell Award and the IEEE TCSVC Research Innovation Award. He has co-founded four companies, run two of them, and advised or mentored over 45 Ph.D. candidates and postdocs to exceptional careers in academia, industry, and as entrepreneurs.
Content
1. Introduction; 2. Knowledge graphs for explainability and interpretability; 3. Knowledge-infused learning: the subsumer to neurosymbolic AI; 4. Shallow infusion of knowledge; 5. Semi-deep infusion learning; 6. Deep knowledge-infused learning; 7. Process knowledge-infused learning; 8. Knowledge-infused conversational NLP; 9. Neurosymbolic large language models; References; Index.