rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Neural symbolic knowledge graph reasoning
    sub title: a pathway towards neural symbolic AI
  name:
    name part: Liu, Lihui
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Tong, Hanghang
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xvi, 151 pages) : illustrations (some color).
  abstract: 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. In addition, this book: Presents existing practices at the methodology level by building index structure for saving query time Discusses how to iteratively find answers when acknowledge graph is incomplete and the query is dynamic Focuses on designing effective models that can answer conversational questions.
  table of contents: Introduction: Background and Challenges -- Knowledge Graph Reasoning for Accurate Query and Complete Graph -- Knowledge Graph Reasoning for Accurate Query and Incomplete Graph -- Knowledge Graph Reasoning for Ambiguous Query and Incomplete Graph -- Knowledge Graph Reasoning for Dynamic Query and Incomplete Graph -- Knowledge Graph Reasoning with Large Language Models -- Conclusion, Open Challenges, and Future Directions.
  note: Lihui Liu, Hanghang Tong.
  note: Includes bibliographical references.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Semantic networks (Information theory)
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Information theory
  subject:
    topic: Information retrieval
  subject:
    topic: Computer architecture
  subject:
    topic: Intelligence artificielle
  subject:
    topic: Structures de données (Informatique)
  subject:
    topic: Théorie de l'information
  subject:
    topic: Recherche de l'information
  subject:
    topic: Ordinateurs
    topic: Architecture
  subject:
    topic: Réseaux neuronaux (Informatique)
  subject:
    topic: artificial intelligence
  subject:
    topic: information retrieval
  classification: Q335 .L58 2026
  classification: 006.3
  related item:
    title info:
      title: Neural Symbolic Knowledge Graph Reasoning
    name:
      name part: Liu, Lihui
    origin info:
      publisher: Cham : Springer,c2026
  related item:
    title info:
      title: Synthesis lectures on computer science (Springer (Firm))
  identifier: 9783032158581 (electronic bk.)
  identifier: 3032158583
  identifier: 9783032158574
  location:
    physical location: POD
  record info:
    record content source: GW5XE
    record creation date: 260304
    record change date: 20260406152358.6
    record identifier: in00005777698
    language of cataloging:
      language term: eng