Graph-based Knowledge Representation - Softcover

Review

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)

From the Back Cover

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?''