Overview
Recent years have seen an upsurge of interest in knowledge. Leading organisations now recognise the importance of identifying what they know, sharing what they know and using what they know for maximum benefit. Many organisations employ knowledge engineers to capture knowledge from experts using the principles and techniques of knowledge engineering. The emphasis is on a structured approach built on a sound understanding of the psychology of expertise and making use of knowledge modelling methods and the latest web technologies. Knowledge Acquisition Projects is the first book to provide a detailed step-by-step guide to the methods and practical aspects of acquiring, modelling, storing and sharing knowledge. The reader is led through 47 steps from the inception of a project to its successful conclusion. Each step is described in terms of the reasons for the step, the required resources, the activities to be undertaken, and the solutions to common problems. In addition, each step has a checklist which lists the key items that should be achieved during the step. Knowledge Acquisition Projects will be of value to knowledge engineers, knowledge workers, knowledge officers and ontological engineers. The book will also be of interest to students and researchers of AI, computer science and business studies.
Full Product Details
Author: Nicholas Ross Milton
Publisher: Springer London Ltd
Imprint: Springer London Ltd
Edition: Softcover reprint of hardcover 1st ed. 2007
Dimensions:
Width: 15.50cm
, Height: 1.00cm
, Length: 23.50cm
Weight: 0.454kg
ISBN: 9781849966610
ISBN 10: 1849966613
Pages: 176
Publication Date: 10 November 2010
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: In Print
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Reviews
From the reviews: ""Building on Nigel Shadbolt's pioneering work in the field of knowledge acquisition, Milton, prominent artificial intelligence researcher, here provides a clear guide for knowledge acquisition projects. ! Overall, this comprehensive work approaches knowledge bases holistically, by exploring both the process of creating knowledge bases and the strengths and weaknesses of competing knowledge base structures. Glossary, index, appendixes, bibliography. A highly technical work appropriate for academic libraries. Summing Up: Highly recommended. Upper-division undergraduates through professionals."" (K. J. Whitehair, CHOICE, Vol. 45 (5), January, 2008)
From the reviews: Building on Nigel Shadbolt's pioneering work in the field of knowledge acquisition, Milton, prominent artificial intelligence researcher, here provides a clear guide for knowledge acquisition projects. ! Overall, this comprehensive work approaches knowledge bases holistically, by exploring both the process of creating knowledge bases and the strengths and weaknesses of competing knowledge base structures. Glossary, index, appendixes, bibliography. A highly technical work appropriate for academic libraries. Summing Up: Highly recommended. Upper-division undergraduates through professionals. (K. J. Whitehair, CHOICE, Vol. 45 (5), January, 2008)
Author Information
Dr Nick Milton has worked in the area of knowledge engineering and knowledge acquisition for over 10 years, first as a researcher at the University of Nottingham, then as a consultant Knowledge Engineer, and now as Chief Knowledge Architect at Epistemics Ltd. As a researcher, he worked on the development of techniques taken from AI (for capturing expert knowledge) for their use in Knowledge Management. As a consultant Knowledge Engineer he has worked on multiple knowledge acquisition projects and helped develop and implement methodologies for a number of large organizations. He has provided training and support to many hundreds of knowledge engineers (e.g. in Airbus, Rolls-Royce and BAE Systems). He is an expert in the PCPACK software tool and part of its development team. A major thrust of his work has been to maximize the benefits of knowledge engineering to a company while also enabling novice knowledge engineers to learn and apply the approach.
