Intro -- Title Page -- Introduction -- Contents -- Chapter 1. The Roles of Symbols in Neural-Based AI: They Are Not What You Think! -- Chapter 2. Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges -- Chapter 3. Architectural Patterns for Neuro-Symbolic AI -- Chapter 4. Semantic Web Machine Learning Systems: An Analysis of System Patterns -- Chapter 5. Boolean Connectives and Deep Learning: Three Interpretations -- Chapter 6. Constructivism as a Paradigm for Neuro-Symbolic Artficial Intelligence -- Chapter 7. Neural-Symbolic Interaction and Co-Evolving -- Chapter 8. Neuro-Causal Models -- Chapter 9. Building Robust and Explainable AI with Commonsense Knowledge Graphs and Neural Models -- Chapter 10. Connectionist Neuroarchitectures in Cognition and Consciousness Theory Based on Integrative (Synchronization) Mechanisms -- Chapter 11. Autodidactic and Coachable Neural Architectures -- Chapter 12. The Neural Blackboard Theory of Neuro-Symbolic Processing: Logistics of Access, Connection Paths and Intrinsic Structures -- Chapter 13. Class Expression Learning with Multiple Representations -- Chapter 14. Embedding-Based First-Order Rule Learning in Large Knowledge Graphs -- Chapter 15. Lifted Relational Neural Networks: From Graphs to Deep Relational Learning -- Chapter 16. Discovering Visual Concepts and Rules in Convolutional Neural Networks -- Chapter 17. Approximate Answering of Graph Queries -- Chapter 18. Enhancing Case-Based Reasoning with Neural Networks -- Chapter 19. Neuro-Symbolic Spatio-Temporal Reasoning -- Chapter 20. Neuro-Symbolic Architectures for Combinatorial Problems in Structured Output Spaces -- Chapter 21. Neuro-Symbolic Semantic Learning for Chemistry -- Chapter 22. Semantic Loss Functions for Neuro-Symbolic Structured Prediction.

Chapter 23. Combining Symbolic and Deep Learning Approaches for Sentiment Analysis -- Chapter 24. Few-Shot Continual Learning Based on Vector Symbolic Architectures -- Chapter 25. Learning Logic Explanations by Neural Networks -- Chapter 26. Combining Sub-Symbolic and Symbolic Methods for Explainability -- Chapter 27. Explaining CNNs Using Knowledge Extraction and Graph Analysis -- Chapter 28. Effective Reasoning over Neural Networks Using Lukasiewicz Logic -- Chapter 29. Latent Trees for Compositional Generalization -- Chapter 30. Weakly Supervised Reasoning by Neuro-Symbolic Approaches -- Author Index.