Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs: Advanced Information and Knowledge Processing
Autor Michel Chein, Marie-Laure Mugnieren Limba Engleză Paperback – 22 oct 2010
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 1114.83 lei 6-8 săpt. | |
SPRINGER LONDON – 22 oct 2010 | 1114.83 lei 6-8 săpt. | |
Hardback (1) | 1120.99 lei 6-8 săpt. | |
SPRINGER LONDON – 21 oct 2008 | 1120.99 lei 6-8 săpt. |
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Specificații
ISBN-13: 9781849967693
ISBN-10: 1849967695
Pagini: 444
Ilustrații: XIV, 428 p.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.62 kg
Ediția:Softcover reprint of hardcover 1st ed. 2009
Editura: SPRINGER LONDON
Colecția Springer
Seria Advanced Information and Knowledge Processing
Locul publicării:London, United Kingdom
ISBN-10: 1849967695
Pagini: 444
Ilustrații: XIV, 428 p.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.62 kg
Ediția:Softcover reprint of hardcover 1st ed. 2009
Editura: SPRINGER LONDON
Colecția Springer
Seria Advanced Information and Knowledge Processing
Locul publicării:London, United Kingdom
Public țintă
ResearchCuprins
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.
Recenzii
From the reviews:
"This well-written book is a wonderful text for researchers working on theoretical artificial intelligence (AI). Fundamentally, AI represents knowledge with mathematical objects and then designs computational rules to manipulate these objects. … In summary, this is a theoretical book for a graph-based approach to knowledge representation. … A number of detailed algorithms presented in the book may serve as good references for designing a variety of AI systems, such as database mining and logic reasoning." (Hsun-Hsien Chang, ACM Computing Reviews, April, 2009)
"This well-written book is a wonderful text for researchers working on theoretical artificial intelligence (AI). Fundamentally, AI represents knowledge with mathematical objects and then designs computational rules to manipulate these objects. … In summary, this is a theoretical book for a graph-based approach to knowledge representation. … A number of detailed algorithms presented in the book may serve as good references for designing a variety of AI systems, such as database mining and logic reasoning." (Hsun-Hsien Chang, ACM Computing Reviews, April, 2009)
Textul de pe ultima copertă
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?''
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?''
Caracteristici
The first book to provide a comprehensive overview on the computational facets of conceptual graphs Intuitively introduces the main notions of graph theory from a knowledge representation viewpoint, and give precise definitions of these notions together with complete proofs of the theorems In its provision of strong theoretical bases the book links up fundamental problems in conceptual graphhs with important problems in computer science, and contains algorithms for reasoning Includes supplementary material: sn.pub/extras