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Conceptual Graphs and Fuzzy Logic

A Fusion for Representing and Reasoning with Linguistic Information

  • Tru Hoang Cao

Part of the Studies in Computational Intelligence book series (SCI, volume 306)

Table of contents

  1. Front Matter
  2. Tru Hoang Cao
    Pages 1-4
  3. Tru Hoang Cao
    Pages 5-45
  4. Tru Hoang Cao
    Pages 47-78
  5. Tru Hoang Cao
    Pages 79-103
  6. Tru Hoang Cao
    Pages 127-144
  7. Tru Hoang Cao
    Pages 145-166
  8. Back Matter

About this book

Introduction

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.

Keywords

Natur automated reasoning fuzzy fuzzy logic knowledge logic modeling programming

Authors and affiliations

  • Tru Hoang Cao
    • 1
  1. 1.Faculty of Computer Science & EngineeringHo Chi Minh City University of TechnologyHo Chi Minh CityVietnam

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-14087-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-14086-0
  • Online ISBN 978-3-642-14087-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site