Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges (Studies on the Semantic Web)

The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field. Title Page Preface Contents Part 1. Foundations of Knowledge-Based eXplainable Systems Knowledge Graphs on the Web - An Overview Foundations of Explainable Knowledge-Enabled Systems Knowledge Graph Embeddings and Explainable AI Benchmarking the Lifecycle of Knowledge Graphs Part 2. Applications Knowledge-Aware Interpretable Recommender Systems Differentiable Reasoning on Large Knowledge Bases and Natural Language Neuro-Symbolic Architectures for Context Understanding Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning Knowledge-Based Explanations for Transfer Learning Explanations in Predictive Analytics: Case Studies Generating Explanations in Natural Language from Knowledge Graphs Part 3. Challenges for Knowledge-Based eXplainable Systems Directions for Explainable Knowledge-Enabled Systems The Data Ethics Challenges of Explainable AI and Their Knowledge-Based Solutions Who Is This Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI Managing Identity in Knowledge-Based Explainable Systems Subject Index Author Index