Graph-based Knowledge Representation [electronic resource] : Computational Foundations of Conceptual Graphs /
Chein, Michel.author.
Mugnier, Marie-Laure.author.
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?''.
Description based upon print version of record.
Includes bibliographical references and index.
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.
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?''.
Discrete mathematics.
Information storage and retrieval.
Data mining.
Artificial intelligence.
Discrete Mathematics.
Information Storage and Retrieval.
Data Mining and Knowledge Discovery.
Artificial Intelligence.
Advanced Information and Knowledge Processing