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: Conceptual Graphs and Fuzzy Logic
    sub title: A Fusion for Representing and Reasoning with Linguistic Information
  name:
    name part: Cao, Tru Hoang.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: 240 p. 77 illus. online resource.
  abstract: The capacity for humans to communicate using language allows us to give, receive, and understand information expressed within a rich and flexible representational framework. Moreover, we can reason based on natural language expressions, and make decisions based on the information they convey, though this information usually involves imprecise terms and uncertain facts. In particular, conceptual graphs invented by John Sowa and fuzzy logic founded by Lofti Zadeh have the common target of representing and reasoning with linguistic information. At this juncture, conceptual graphs provide a syntactic structure for a smooth mapping to and from natural language, while fuzzy logic provides a semantic processor for approximate reasoning with words hav-ing vague meanings. This volume is the combined result of an interdisciplinary research programme focused on the integration of conceptual graphs and fuzzy logic for various knowledge and information processing tasks that involves natural language. First, it is about fuzzy conceptual graphs and their logic programming foundations, as a graph-based order-sorted fuzzy set logic programming language for automated reasoning with fuzzy object attributes and types. Second, it extends conceptual graphs with general quantifiers and develops direct reasoning operations on these extended conceptual graphs, which could be mapped to and from generally quantified natural language statements. Third, it defines similarity and subsumption measures between object types, names, and attributes and uses them for approximate retrieval of knowledge represented in graphs. Finally, it proposes a robust ontology-based method for understanding natural language queries using nested conceptual graphs.
  table of contents: Fuzzy Conceptual Graphs -- Annotated Fuzzy Logic Programming -- Fuzzy Conceptual Graph Programming -- Modelling and Computing with Generally Quantified Statements -- Approximate Knowledge Retrieval -- Natural Language Query Understanding.
  note: by Tru Hoang Cao.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 306
  identifier: 9783642140877
  identifier: http://dx.doi.org/10.1007/978-3-642-14087-7
  record info:
    record creation date: 100717
    record change date: 20151103123932.0
    record identifier: 978-3-642-14087-7