Neural Symbolic Knowledge Graph Reasoning

A Pathway Towards Neural Symbolic AI

,

Éditeur :

Springer

Paru le : 2026-02-02

This book explores various aspects of knowledge graph reasoning to solve different tasks, encompassing first, traditional symbolic methods for knowledge graph reasoning; second, recent developments in neural-based knowledge graph reasoning techniques; and third, cutting-edge advancements in neural-s...
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Éditeur

Collection
n.c

Parution
2026-02-02

Pages
151 pages

EAN papier
9783032158574

Auteur(s) du livre


Lihui Liu, Ph.D., is an Assistant Professor in the Department of Computer Science at Wayne State University. He received his Ph.D. from the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on large-scale data mining and machine learning, particularly on graphs, with an emphasis on knowledge graph reasoning. Dr. Liu’s research has been published at several major conferences and in journals on data mining and artificial intelligence.  He has also served as a reviewer and program committee member for top-tier data mining and artificial intelligence conferences and journals, including KDD, WWW, AAAI, IJCAI, and BigData. Hanghang Tong, Ph.D, is a Professor and University Scholar at Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign.  He received his M.Sc. and Ph.D. degrees from Carnegie Mellon University in 2008 and 2009, both in Machine Learning. His research interests include large scale data mining for graphs and multimedia.  Dr. Tong has published 300+ papers, and his research has received several awards, including SDM/IBM 2018 early career data mining research award, two ‘test of time’ awards (ICDM 2015 & 2022 10-Year Highest Impact Paper award), ICDM Tao Li award (2019), NSF CAREER award, and several best paper awards. He was Editor-in-Chief of ACM SIGKDD Explorations (2018 - 2022). He is also a distinguished member of ACM (2021) and a Fellow of IEEE (2022).

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EAN PDF
9783032158581
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42,19 €
Nombre pages copiables
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Nombre pages imprimables
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Taille du fichier
9021 Ko
EAN EPUB
9783032158581
Prix
42,19 €
Nombre pages copiables
1
Nombre pages imprimables
15
Taille du fichier
15790 Ko

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