Graph-based Knowledge Representation Computational Foundations of Conceptual Graphs /

This 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 comput...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Chein, Michel (Συγγραφέας), Mugnier, Marie-Laure (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : Springer London, 2009.
Σειρά:Advanced Information and Knowledge Processing
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Chein, Michel.  |e author. 
245 1 0 |a Graph-based Knowledge Representation  |h [electronic resource] :  |b Computational Foundations of Conceptual Graphs /  |c by Michel Chein, Marie-Laure Mugnier. 
264 1 |a London :  |b Springer London,  |c 2009. 
300 |a XIV, 428 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Advanced Information and Knowledge Processing 
505 0 |a Foundations: 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 |a This 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?''. 
650 0 |a Mathematics. 
650 0 |a Data mining. 
650 0 |a Information storage and retrieval. 
650 0 |a Artificial intelligence. 
650 0 |a Discrete mathematics. 
650 1 4 |a Mathematics. 
650 2 4 |a Discrete Mathematics. 
650 2 4 |a Information Storage and Retrieval. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
700 1 |a Mugnier, Marie-Laure.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781848002852 
830 0 |a Advanced Information and Knowledge Processing 
856 4 0 |u http://dx.doi.org/10.1007/978-1-84800-286-9  |z Full Text via HEAL-Link 
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950 |a Computer Science (Springer-11645)