Knowledge Graph Completion and Its Applications - Zhao, Yu
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Knowledge Graphs (KGs) attract increasing interest in research in various KG-driven AI-related fields, such as question answering, information recommendation, dialogue, etc. KGs are effective well-structural relational databases for knowledge acquisition. This book presents our research and developments on knowledge graph learning and its applications. Specifically, we mainly focus on: 1) knowledge graph completion, including entity and relation prediction, and entity typing, and 2) the KG applications, including its utilization in fine-grained entity typing, and stock movement prediction.

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Produktbeschreibung
Knowledge Graphs (KGs) attract increasing interest in research in various KG-driven AI-related fields, such as question answering, information recommendation, dialogue, etc. KGs are effective well-structural relational databases for knowledge acquisition. This book presents our research and developments on knowledge graph learning and its applications. Specifically, we mainly focus on: 1) knowledge graph completion, including entity and relation prediction, and entity typing, and 2) the KG applications, including its utilization in fine-grained entity typing, and stock movement prediction.
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Autorenporträt
Yu Zhao received M.S. and Ph.D. degrees from the Beijing University of Posts and Telecommunications in 2011 and 2017, respectively. He is currently a Professor at Southwestern University of Finance and Economics. He has authored more than 30 papers in top journals and conferences including IEEE TKDE, IEEE TNNLS, IEEE TMC, ACL, ICME, etc.