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020 _a9781848002869
_99781848002869
024 7 _a10.1007/9781848002869
_2doi
035 _avtls000344254
039 9 _a201509030354
_bVLOAD
_c201405050304
_dVLOAD
_y201402061252
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA75.5-76.95
100 1 _aChein, Michel.
_eautor
_9322179
245 1 0 _aGraph-based Knowledge Representation :
_bComputational Foundations of Conceptual Graphs /
_cby Michel Chein, Marie-Laure Mugnier.
264 1 _aLondon :
_bSpringer London,
_c2008.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aAdvanced Information and Knowledge Processing
500 _aSpringer eBooks
505 0 _aFoundations: Basic and Simple Conceptual Graphs -- Basic Conceptual Graphs -- Simple Conceptual Graphs -- Formal Semantics of SGs -- BG Homomorphism and Equivalent Notions -- Computational Aspects of Basic Conceptual Graphs -- Basic Algorithms for BG Homomorphism -- Tractable Cases -- Other Specialization/Generalization Operations -- Extensions -- Nested Conceptual Graphs -- Rules -- The BG Family: Facts, Rules and Constraints -- Conceptual Graphs with Negation -- An Application of Nested Typed Graphs: Semantic Annotation Bases.
520 _aThis book studies a graph-based knowledge representation and reasoning formalism stemming from conceptual graphs, with a substantial focus on the computational properties. Knowledge can be symbolically represented in many ways, and the authors have chosen labeled graphs for their modeling and computational qualities. Key features of the formalism presented can be summarized as follows: • all kinds of knowledge (ontology, facts, rules, constraints) are labeled graphs, which provide an intuitive and easily understandable means to represent knowledge, • reasoning mechanisms are based on graph-theoretic operations and this allows, in particular, for linking the basic problem to other fundamental problems in computer science (e.g. constraint networks, conjunctive queries in databases), • it is logically founded, i.e. it has a logical semantics and the graph inference mechanisms are sound and complete, • there are efficient reasoning algorithms, thus knowledge-based systems can be built to solve real problems. In a nutshell, the authors have attempted to answer, the following question: ``how far is it possible to go in knowledge representation and reasoning by representing knowledge with graphs and reasoning with graph operations?''
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aMugnier, Marie-Laure.
_eautor
_9322180
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781848002852
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84800-286-9
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c291074
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