A Knowledge Representation Practionary
Practical 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 |
The book is intended to provide durable, 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.
Besides the free PDF pre-release version of the full book , individuals with a Springer subscription may get a softcover copy of the e-book for $24.99 under Springer’s MyCopy program. The standard (non-subscriber) e-book is available for $129 and hardcover copies are available for $169; see the standard Springer order site.
Enjoy, and I hope to hear your reactions!