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.
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.