Neurosymbolic integration has emerged as a very active subfield of artificial intelligence research. Historically, neural approaches have excelled at perceptual tasks—such as pattern recognition in images and natural language—while symbolic frameworks have offered powerful methods for knowledge representation, logical inference, and interpretability. Today’s AI landscape is increasingly defined by hybrid systems that blend these complementary paradigms, enabling both sophisticated pattern recognition and robust reasoning grounded in explicit knowledge. This confluence is particularly relevant in the context of knowledge graphs (KGs), which provide a structured, machine-readable representation of relational data and domain ontologies. KGs serve as a bridge between symbolic logic and the subsymbolic world of deep learning, bringing forward a new era of data-driven discovery and explainable AI.

This book was conceived to capture and organize state-of-the-art neurosymbolic and knowledge graph-based AI. It reflects an ecosystem in which large language models, deep neural networks, and symbolic representations converge. The chapters collected here illustrate the progress that has been made in bridging data-driven machine learning with the semantic richness of KGs, while also uncovering emerging challenges in trustworthiness, interpretability, and scalability. The primary aim is to provide researchers, practitioners, and students with a comprehensive overview of these intersecting fields—unifying theoretical foundations with practical insights and real-world applications.

We have intentionally designed this volume’s structure to move from Foundations and Representation to Construction, Reasoning, Interpretability, and, ultimately, Applications. After setting the conceptual stage in Foundations of Neural and Symbolic AI, the book explores the nuances of Knowledge Graph Representation and Embeddings, where readers gain insight into the cutting-edge methods of turning symbolic knowledge into continuous vector spaces. We then move on to Knowledge Graph Construction, Integration, and Quality, covering key challenges such as entity alignment, canonicalization, fusion, and the critical aspect of uncertainty management. Next, in Neurosymbolic Reasoning and Hybrid Architectures, readers will find an anthology of solutions that seamlessly combine symbolic logic with deep learning pipelines—spanning question answering, program synthesis, dynamic KG methods, and beyond. The subsequent section, Explainable and Interpretable AI, addresses one of the most pressing needs in modern AI: ensuring transparency, accountability, and trust in systems that operate on increasingly complex data. Finally, Interdisciplinary Perspectives and Real-World Applications showcase how these methods tackle domain-specific problems across news analytics, literary studies, life sciences, food computing, social media, and more—demonstrating the far-reaching impact of knowledge-infused neural AI.

Our hope is that this book not only advances the conversation in neurosymbolic integration and knowledge graphs but also serves as a practical guide for building AI systems that are robust, transparent, and ethically grounded. We see this volume as a starting point for researchers and developers who wish to create applications that meaningfully combine data-driven models with explicit symbolic reasoning. By tying together fundamental concepts, advanced methods, and real-world scenarios, we strive to offer a well-rounded perspective suitable for graduate students, academic researchers, and industrial practitioners alike.

We extend our sincere gratitude to the authors and contributors whose expertise, vision, and dedication made this volume possible. Their chapters reflect a remarkable diversity of approaches, each reinforcing the notion that the synergy between symbolic and neural methodologies is indispensable for the future of AI. We also thank our readers for embarking on this journey. May these pages spark your curiosity, inform your practice, and inspire breakthroughs at the vibrant intersection of knowledge graphs, deep learning, and symbolic reasoning.

Pascal Hitzler

Abhilekha Dalal

Mohammad Saeid Mahdavinejad

Sanaz Saki Norouzi

Manhattan, Kansas, December 2024