A Knowledge Representation Practionary

A Knowledge Representation PractionaryPractical Guidance on KR, Knowledge Graphs, Semantic Technologies, and KBpedia


A Knowledge Representation Practionary:
Guidelines Based on Charles Sanders Peirce

Michael K. Bergman

Springer International Publishing, 464 pp., December 2018
ISBN 978-3-319-98091-1

This major work on knowledge representation is based on the insights of Charles S. Peirce, the 19th century founder of American pragmatism, who was also a logician, scientist, mathematician, and philosopher of the first rank. The 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.

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.

The 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.

Preface vii
 1. Introduction 1
Structure of the Book 2
Overview of Contents 3
Key Themes 10
 2. Information, Knowledge, Representation 15
What is Information? 16
What is Knowledge? 27
What is Representation? 33
Part I: Knowledge Representation in Context
 3. The Situation 45
Information and Economic Wealth 46
Untapped Information Assets 54
Impediments to Information Sharing 61
 4. The Opportunity 65
KM and A Spectrum of Applications 66
Data Interoperability 69
Knowledge-based Artificial Intelligence 74
 5. The Precepts 85
Equal Class Data Citizens 86
Addressing Semantic Heterogeneity 91
Carving Nature at the Joints 97
Part II: A Grammar for Knowledge Representation
 6. The Universal Categories 107
A Foundational Mindset 108
Firstness, Secondness, Thirdness 112
The Lens of the Universal Categories 116
 7. A KR Terminology 129
Things of the World 131
Hierarchies in Knowledge Representation 135
A Three-Relations Model 143
 8. KR Vocabulary and Languages 151
Logical Considerations 153
Pragmatic Model and Language Choices 163
The KBpedia Vocabulary 167
Part III: Components of Knowledge Representation
 9. Keeping the Design Open 183
The Context of Openness 184
Information Management Concepts 193
Taming a Bestiary of Data Structs 200
10. Modular, Expandable Typologies 207
Types as Organizing Constructs 208
A Flexible Typology Design 215
KBpedia’s Typologies 219
11. Knowledge Graphs and Bases 227
Graphs and Connectivity 228
Upper, Domain and Administrative Ontologies 237
KBpedia’s Knowledge Bases 242
Part IV: Building KR Systems
12. Platforms and Knowledge Management 251
Uses and Work Splits 252
Platform Considerations 262
A Web-oriented Architecture 268
13. Building Out The System 273
Tailoring for Domain Uses 274
Mapping Schema and Knowledge Bases 280
‘Pay as You Benefit’ 291
14. Testing and Best Practices 295
A Primer on Knowledge Statistics 296
Builds and Testing 304
Some Best Practices 309
Part V: Practical Potentials and Outcomes
15. Potential Uses in Breadth 319
Near-term Potentials 320
Logic and Representation 327
Potential Methods and Applications 332
16. Potential Uses in Depth 343
Workflows and BPM 343
Semantic Parsing 349
Cognitive Robotics and Agents 361
17. Conclusion 371
The Sign and Information Theoretics 372
Peirce: The Philosopher of KR 373
Reasons to Question Premises 377
Appendix A: Perspectives on Peirce 381
Appendix B: The KBpedia Resource 409
Appendix C: KBpedia Feature Possibilities 421
Glossary 435
Index 451

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.

The 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.

Individuals with a Springer subscription may get a softcover copy of the e-book for $24.99 under Springer’s MyCopy program. The standard e-book is available for $129 and hardcover copies are available for $169; see the standard Springer order site. Students or individuals without Springer subscriptions who can not afford these prices should contact Mike directly for possible alternatives.