Knowledge Graph Reasoning: A Neuro-Symbolic Perspective (知識圖譜推理：神經符號觀點)

Cheng, Kewei, Sun, Yizhou

Knowledge Graph Reasoning: A Neuro-Symbolic Perspective
  • 出版商: Springer
  • 出版日期: 2024-11-22
  • 售價: $1,870
  • 貴賓價: 9.5 折 $1,777
  • 語言: 英文
  • 頁數: 196
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031720075
  • ISBN-13: 9783031720079

    • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

本書提供了一個連貫且統一的視角，將邏輯與表示學習結合，以促進知識圖譜（KG）推理並產生更好的計算工具來整合這兩個領域。為此，邏輯與深度神經網絡模型被作為計算的整合模型共同研究。本書適合對KG推理及神經符號整合的新視角感興趣的讀者，並且具備神經網絡和深度學習的先前知識。作者首先提供了邏輯及與所調查技術密切相關的背景知識的初步介紹，例如知識圖譜和本體架構的介紹，以及一階邏輯學習的技術基礎。知識圖譜補全的推理技術從三個角度進行介紹，包括：基於表示學習的、邏輯的，以及神經符號整合的。接著，本書探討了在KG上進行問題回答，特別關注多跳和複雜邏輯查詢的回答，然後概述了解決規則學習問題的相關工作。最後幾章強調了本體架構的基礎，並介紹了其在KG中的應用，最後以開放的研究問題和對該領域未來潛在方向的討論作結。

作者簡介

Kewei 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. Her principal research interest is on mining graphs/networks and more generally in data mining and machine learning with a recent focus on deep learning on graphs and neuro-symbolic reasoning. Dr. Sun is a recipient of multiple Best Paper Awards, two Test of Time Awards, among many other awards. She has also served as organizers of top conferences in the field, such as KDD'23, ICLR'24, and KDD'25.

作者簡介(中文翻譯)

Kewei Cheng, Ph.D.，是亞馬遜的一名應用科學家。她於2024年在加州大學洛杉磯分校（UCLA）獲得計算機科學博士學位。她的主要研究領域包括圖形和網絡挖掘，以及對數據挖掘和機器學習的更廣泛興趣。Cheng博士的研究成果已在多個領域的各大知名會議上發表，如KDD、VLDB、WSDM、CIKM、AAAI、ICLR、EMNLP和ACL。

Yizhou Sun, Ph.D.，是加州大學洛杉磯分校（UCLA）計算機科學系的教授。她的主要研究興趣在於圖形/網絡挖掘，以及更一般的數據挖掘和機器學習，最近專注於圖形上的深度學習和神經符號推理。Sun博士曾獲得多項最佳論文獎、兩項持久影響獎等多個獎項。她還擔任過該領域頂級會議的組織者，如KDD'23、ICLR'24和KDD'25。

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