A Knowledge Representation Practionary

Guidelines Based on Charles Sanders Peirce

  • Michael K. Bergman

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Michael K. Bergman
    Pages 1-13
  3. Michael K. Bergman
    Pages 15-42
  4. Knowledge Representation in Context

    1. Front Matter
      Pages 43-43
    2. Michael K. Bergman
      Pages 45-64
    3. Michael K. Bergman
      Pages 65-84
    4. Michael K. Bergman
      Pages 85-104
  5. A Grammar for Knowledge Representation

    1. Front Matter
      Pages 105-105
    2. Michael K. Bergman
      Pages 107-127
    3. Michael K. Bergman
      Pages 129-149
    4. Michael K. Bergman
      Pages 151-180
  6. Components of Knowledge Representation

    1. Front Matter
      Pages 181-181
    2. Michael K. Bergman
      Pages 183-205
    3. Michael K. Bergman
      Pages 207-226
    4. Michael K. Bergman
      Pages 227-247
  7. Building KR Systems

    1. Front Matter
      Pages 249-249
    2. Michael K. Bergman
      Pages 251-272
    3. Michael K. Bergman
      Pages 273-294
    4. Michael K. Bergman
      Pages 295-316
  8. Practical Potentials and Outcomes

    1. Front Matter
      Pages 317-317
    2. Michael K. Bergman
      Pages 319-341
    3. Michael K. Bergman
      Pages 343-369
    4. Michael K. Bergman
      Pages 371-380
  9. Back Matter
    Pages 381-462

About this book


This major work on knowledge representation is based on the writings of Charles S. Peirce, a logician, scientist, and philosopher of the first rank at the beginning of the 20th century. This book follows Peirce's practical guidelines and universal categories in a structured approach to knowledge representation that captures differences in events, entities, relations, attributes, types, and concepts. Besides the ability to capture meaning and context, the Peircean approach is also well-suited to machine learning and knowledge-based artificial intelligence. Peirce is a founder of pragmatism, the uniquely American philosophy.

Knowledge representation is shorthand for how to represent human symbolic information and knowledge to computers to solve complex questions. KR applications range from semantic technologies and knowledge management and machine learning to information integration, data interoperability, and natural language understanding. Knowledge representation is an essential foundation for knowledge-based AI.

This book is structured into five parts. The first and last parts are bookends that first set the context and background and conclude with practical applications. The three main parts that are the meat of the approach first address the terminologies and grammar of knowledge representation, then building blocks for KR systems, and then design, build, test, and best practices in putting a system together. Throughout, the book refers to and leverages the open source KBpedia knowledge graph and its public knowledge bases, including Wikipedia and Wikidata. KBpedia is a ready baseline for users to bridge from and expand for their own domain needs and applications. It is built from the ground up to reflect Peircean principles.

This book is one of timeless, practical guidelines for how to think about KR and to design knowledge management (KM) systems. The book is grounded bedrock for enterprise information and knowledge managers who are contemplating a new knowledge initiative.

This book is an essential addition to theory and practice for KR and semantic technology and AI researchers and practitioners, who will benefit from Peirce's profound understanding of meaning and context.


semantic technologies semantic web artificial intelligence knowledge graph knowledge base knowledge representation knowledge-based artificial intelligence knowledge management ontology data interoperability RDF OWL KBpedia Charles Sanders Peirce

Authors and affiliations

  • Michael K. Bergman
    • 1
  1. 1.Cognonto CorporationCoralvilleUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-98091-1
  • Online ISBN 978-3-319-98092-8
  • Buy this book on publisher's site
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