This book constitutes the refereed proceedings of the 8th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence, CCKS 2023, held in Shenyang, China, during August 24–27, 2023. The 28 full papers included in this book were carefully reviewed and selected from 106 submissions. They were organized in topical sections as follows: ​knowledge representation and knowledge graph reasoning; knowledge acquisition and knowledge base construction; knowledge integration and knowledge graph management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources; and evaluations.

This book constitutes the refereed proceedings of the 6th China Conference on Knowledge Graph and Semantic Computing, CCKS 2021, held in Guangzhou, China, in November 2021. The 19 revised full papers and 9 short papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on ​knowledge extraction: knowledge graph representation and reasoning; knowledge acquisition and knowledge graph construction; linked data, knowledge integration, and knowledge graph storage management; natural language understanding and semantic computing; knowledge graph applications: semantic search, question answering, dialogue, decision support, and recommendation; knowledge graph open resources.

This book constitutes the refereed proceedings of the 7th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy, CCKS 2022, in Qinhuangdao, China, August 24–27, 2022. The 15 full papers and 2 short papers included in this book were carefully reviewed and selected from 100 submissions. They were organized in topical sections as follows: knowledge representation and reasoning; knowledge acquisition and knowledge base construction; linked data, knowledge integration, and knowledge graph storage managements; natural language understanding and semantic computing; knowledge graph applications; and knowledge graph open resources.

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

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.

ARTIFICIAL INTELLIGENCE IN PERFORMANCE-DRIVEN DESIGN A definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. The book examines relevant practices, case studies, and computational tools that harness AI’s capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments. This book also: Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design Presents data-driven methodologies and technologies that integrate into modeling and design platforms Shares valuable insights and tools for developing decarbonization pathways in urban buildings Includes contributions from expert researchers and educators across a range of related fields Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.