Neural Symbolic Knowledge Graph Reasoning: A Pathway Towards Neural Symbolic AI
暫譯: 神經符號知識圖譜推理：通往神經符號人工智慧的途徑

Liu, Lihui, Tong, Hanghang

Neural Symbolic Knowledge Graph Reasoning: A Pathway Towards Neural Symbolic AI
  • 出版商: Springer
  • 出版日期: 2026-02-03
  • 售價: $2,040
  • 貴賓價: 9.8 折 $1,999
  • 語言: 英文
  • 頁數: 151
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3032158575
  • ISBN-13: 9783032158574
  • 相關分類: 推薦系統

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商品描述

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-symbolic hybrid approaches to knowledge graph reasoning. The authors focus on the model and algorithm design aspect and study knowledge graphs from two perspectives: background knowledge graph and input query. Knowledge graph reasoning, which aims to infer and discover new knowledge from existing information in the knowledge graph, has played an important role in many real-world applications, such as question answering and recommender systems. A new trend in knowledge graph reasoning is the combination of neural models with symbolic knowledge graphs, allowing for the design of models that are not only efficient and accurate, but also interpretable. In this book, the authors study the application of neural-symbolic knowledge reasoning to different tasks from two perspectives: the input query and the background knowledge graph.

商品描述(中文翻譯)

本書探討知識圖譜推理的各個方面，以解決不同的任務，包括首先，傳統的符號方法用於知識圖譜推理；其次，基於神經網絡的知識圖譜推理技術的最新發展；第三，知識圖譜推理的神經-符號混合方法的前沿進展。作者專注於模型和算法設計的方面，並從兩個角度研究知識圖譜：背景知識圖譜和輸入查詢。知識圖譜推理旨在從知識圖譜中現有的信息推斷和發現新知識，在許多現實應用中扮演了重要角色，例如問答系統和推薦系統。知識圖譜推理的一個新趨勢是將神經模型與符號知識圖譜相結合，這使得設計出既高效又準確且可解釋的模型成為可能。在本書中，作者從兩個角度研究神經-符號知識推理在不同任務中的應用：輸入查詢和背景知識圖譜。

作者簡介

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).

作者簡介(中文翻譯)

Lihui Liu 博士是韋恩州立大學計算機科學系的助理教授。他在伊利諾伊大學香檳分校的計算機科學系獲得博士學位。他的研究專注於大規模數據挖掘和機器學習，特別是在圖形方面，並強調知識圖譜推理。劉博士的研究已在多個主要會議和數據挖掘及人工智慧期刊上發表。他還擔任過多個頂尖數據挖掘和人工智慧會議及期刊的審稿人和程序委員會成員，包括 KDD、WWW、AAAI、IJCAI 和 BigData。

Hanghang Tong 博士是伊利諾伊大學香檳分校 Siebel 計算與數據科學學院的教授及大學學者。他於 2008 年和 2009 年在卡內基梅隆大學獲得碩士和博士學位，專攻機器學習。他的研究興趣包括圖形和多媒體的大規模數據挖掘。唐博士已發表超過 300 篇論文，並獲得多項獎項，包括 SDM/IBM 2018 年早期職業數據挖掘研究獎、兩項「時間考驗」獎（ICDM 2015 和 2022 年 10 年最高影響力論文獎）、ICDM Tao Li 獎（2019 年）、NSF CAREER 獎以及多項最佳論文獎。他曾擔任 ACM SIGKDD Explorations 的主編（2018 - 2022）。他也是 ACM 的傑出會員（2021 年）和 IEEE 的院士（2022 年）。

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