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020 _a9783642140877
_9978-3-642-14087-7
040 _cMX-MeUAM
050 4 _aQ342
082 0 4 _a006.3
_223
100 1 _aCao, Tru Hoang.
_eauthor.
245 1 0 _aConceptual Graphs and Fuzzy Logic
_h[recurso electrónico] :
_bA Fusion for Representing and Reasoning with Linguistic Information /
_cby Tru Hoang Cao.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _a240p. 77 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v306
505 0 _aFuzzy Conceptual Graphs -- Annotated Fuzzy Logic Programming -- Fuzzy Conceptual Graph Programming -- Modelling and Computing with Generally Quantified Statements -- Approximate Knowledge Retrieval -- Natural Language Query Understanding.
520 _aThe 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642140860
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v306
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-14087-7
596 _a19
942 _cLIBRO_ELEC
999 _c202551
_d202551