Neural approaches have traditionally excelled at perceptual tasks like pattern recognition, whereas symbolic frameworks have offered powerful methods for knowledge representation, logical inference, and interpretability, but the current AI landscape is increasingly defined by hybrid systems that blend these complementary paradigms. This is particularly relevant in the context of knowledge graphs (KGs), which serve as a bridge between symbolic logic and the subsymbolic world of deep learning. The Handbook on Neurosymbolic AI and Knowledge Graphs deals with state-of-the-art neurosymbolic and KG-based AI, reflecting an ecosystem in which large language models, deep neural networks, and symbolic representations converge. It illustrates the progress that has been made, while also revealing emerging challenges in trustworthiness, interpretability, and scalability. The first four chapters are on the foundations of neural and symbolic AI. In the following chapters the authors explore the nuances of KG representation and embeddings, moving on to KG construction, integration, and quality, and covering challenges such as entity alignment, canonicalization, fusion, and the critical aspect of uncertainty management. Offering solutions that seamlessly combine symbolic logic with deep learning pipelines, the handbook deals with question answering, program synthesis, and dynamic KG methods, before moving on to the need to ensure transparency, accountability, and trust in systems operating on increasingly complex data. The final chapters demonstrate problem solving across news analytics, literary studies, life sciences, food computing, social media, and more. This work offers a comprehensive overview of these intersecting fields and will be of interest to researchers and developers looking for a practical guide to building AI systems that are robust, transparent, and ethically grounded.

Neural approaches have traditionally excelled at perceptual tasks like pattern recognition, whereas symbolic frameworks have offered powerful methods for knowledge representation, logical inference, and interpretability, but the current AI landscape is increasingly defined by hybrid systems that blend these complementary paradigms. This is particularly relevant in the context of knowledge graphs (KGs), which serve as a bridge between symbolic logic and the subsymbolic world of deep learning. The Handbook on Neurosymbolic AI and Knowledge Graphs deals with state-of-the-art neurosymbolic and KG-based AI, reflecting an ecosystem in which large language models, deep neural networks, and symbolic representations converge. It illustrates the progress that has been made, while also revealing emerging challenges in trustworthiness, interpretability, and scalability. The first four chapters are on the foundations of neural and symbolic AI. In the following chapters the authors explore the nuances of KG representation and embeddings, moving on to KG construction, integration, and quality, and covering challenges such as entity alignment, canonicalization, fusion, and the critical aspect of uncertainty management. Offering solutions that seamlessly combine symbolic logic with deep learning pipelines, the handbook deals with question answering, program synthesis, and dynamic KG methods, before moving on to the need to ensure transparency, accountability, and trust in systems operating on increasingly complex data. The final chapters demonstrate problem solving across news analytics, literary studies, life sciences, food computing, social media, and more. This work offers a comprehensive overview of these intersecting fields and will be of interest to researchers and developers looking for a practical guide to building AI systems that are robust, transparent, and ethically grounded.

Neural approaches have traditionally excelled at perceptual tasks like pattern recognition, whereas symbolic frameworks have offered powerful methods for knowledge representation, logical inference, and interpretability, but the current AI landscape is increasingly defined by hybrid systems that blend these complementary paradigms. This is particularly relevant in the context of knowledge graphs (KGs), which serve as a bridge between symbolic logic and the subsymbolic world of deep learning. The Handbook on Neurosymbolic AI and Knowledge Graphs deals with state-of-the-art neurosymbolic and KG-based AI, reflecting an ecosystem in which large language models, deep neural networks, and symbolic representations converge. It illustrates the progress that has been made, while also revealing emerging challenges in trustworthiness, interpretability, and scalability. The first four chapters are on the foundations of neural and symbolic AI. In the following chapters the authors explore the nuances of KG representation and embeddings, moving on to KG construction, integration, and quality, and covering challenges such as entity alignment, canonicalization, fusion, and the critical aspect of uncertainty management. Offering solutions that seamlessly combine symbolic logic with deep learning pipelines, the handbook deals with question answering, program synthesis, and dynamic KG methods, before moving on to the need to ensure transparency, accountability, and trust in systems operating on increasingly complex data. The final chapters demonstrate problem solving across news analytics, literary studies, life sciences, food computing, social media, and more. This work offers a comprehensive overview of these intersecting fields and will be of interest to researchers and developers looking for a practical guide to building AI systems that are robust, transparent, and ethically grounded.