Knowledge Graph Reasoning - Cheng, Kewei;Sun, Yizhou
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This book provides a coherent and unifying view for logic and representation learning to contribute to knowledge graph (KG) reasoning and produce better computational tools for integrating both worlds. To this end, logic and deep neural network models are studied together as integrated models of computation. This book is written for readers who are interested in KG reasoning and the new perspective of neuro-symbolic integration and have prior knowledge to neural networks and deep learning. The authors first provide a preliminary introduction to logic and background knowledge closely related to…mehr

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Produktbeschreibung
This book provides a coherent and unifying view for logic and representation learning to contribute to knowledge graph (KG) reasoning and produce better computational tools for integrating both worlds. To this end, logic and deep neural network models are studied together as integrated models of computation. This book is written for readers who are interested in KG reasoning and the new perspective of neuro-symbolic integration and have prior knowledge to neural networks and deep learning. The authors first provide a preliminary introduction to logic and background knowledge closely related to the surveyed techniques such as the introduction of knowledge graph and ontological schema and the technical foundations of first-order logic learning. Reasoning techniques for knowledge graph completion are presented from three perspectives, including: representation learning-based, logical, and neuro-symbolic integration. The book then explores question answering on KGs with specific focus on multi-hop and complex-logic query answering before outlining work that addresses the rule learning problem. The final chapters highlight foundations on ontological schema and introduce its usage in KG before closing with open research questions and a discussion on the potential directions in the future of the field.
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Autorenporträt
Vivian Cheng, Ph.D., is an applied scientist at Amazon. She earned her Ph.D. in Computer Science from UCLA in 2024. Her main research areas include graph and network mining as well as broader interests in data mining and machine learning. Dr. Cheng's work has been featured in various prestigious conferences across multiple domains such as KDD, VLDB, WSDM, CIKM, AAAI, ICLR, EMNLP, and ACL. Yizhou Sun, Ph.D., is a Professor in the Department of Computer Science at UCLA and Amazon Scholar. She received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. Her principal research interest is on mining graphs/networks and more generally in data mining, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications. She is a pioneer researcher in mining heterogeneous information network, with a recent focus on deep learning on graphs and neuro-symbolic reasonings. Dr. Sun has over 180 publications in books, journals, and major conferences. Tutorials of her research have been given in many premier conferences. She is a recipient of multiple Best Paper Awards, ACM SIGKDD Doctoral Dissertation Award, Yahoo ACE (Academic Career Enhancement) Award, NSF CAREER Award, CS@ILLINOIS Distinguished Educator Award, Amazon Research Awards, Okawa Foundation Research Award, VLDB Test of Time Award, WSDM Test of Time Award, ACM Distinguished Member, IEEE AI's 10 to Watch, and SDM/IBM faculty award. She was a general co-chair of SIGKDD 2023 and current PC co-chair of ICLR 2024 and SIGKDD 2025.