Posted:November 20, 2018

Announcing My New Knowledge Representation Book

A Knowledge Representation PractionaryPractical Guidance on How to Leverage Knowledge Graphs, Semantic Technologies, and KBpedia

As readers of this blog well know, I am passionate on topics related to semantic technologies, knowledge graphs (ontologies), data structs, and artificial intelligence. Readers also probably know that I have found Charles S. Peirce, the 19th century American logician, scientist, and philosopher, to have remarkable insights on all aspects of knowledge representation. I’m proud to now announce my new book, A Knowledge Representation Practionary: Guidelines Based on Charles Sanders Peirce (Springer), that combines these viewpoints into a comprehensive whole. The 464 pp book is available for pre-order from Springer or from Amazon (and others, I’m sure). Formal release is due the second week of December.

Peirce’s practical guidelines and universal categories provide 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, this Peircean approach is also well-suited to machine learning and knowledge-based artificial intelligence (KBAI). Peirce is a founder of pragmatism, the uniquely American philosophy. We have already used this viewpoint to produce the KBpedia knowledge base and artifact, which we just released as open source. My book combines that viewpoint with the experience that Fred Giasson and I gained over the past decade with commercial clients in semantic and AI technologies. While KBpedia and the book stand on their own and do not depend on each other, they do reference one another, and those with serious interest may find it useful to keep KBpedia open as they progress through the book’s chapters.

I use the term practionary for the book — a decidedly new term — because the Peircean scholar Kelly Parker first coined that term to capture Charles Perice’s uniquely pragmatic way to fully explicate a particular domain of inquiry. In our case, of course, that domain is knowledge representation, which 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 practionary approach is a soup-to-nuts way to fully apprehend a given topic. To my knowledge, the book is the first attempt to put this Peircean method and framework into action.

I structure the book into five parts, following Peirce’s own approach. The first and last parts are bookends. The first bookend sets the context and background. The concluding bookend presents practical applications from following the guidelines. In between, the three main parts mirror Peirce’s three universal categories, the meat of his approach. The first of these three addresses the terminologies and grammar of knowledge representation. The next discusses the actual components or building blocks for KR systems. And the third provides what generalities we may derive about how to design, build, test, and follow 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. Actual practitioners may find KBpedia, built from the ground up on these Peircean principles, a ready baseline to build their own domain knowledge graph and applications.

Here are the parts and chapters of the book:

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

My intent is to produce a book of enduring, practical guidelines for how to think about KR and to design knowledge management (KM) systems. I emphasize how-to guidance and ways to think about KR problems. The audience in my mind are enterprise information and knowledge managers who are contemplating a new knowledge initiative. However, early reviewers have told me the basics are useful to students and researchers at all levels.

I am not even-handed in this book. My explicit purpose is to offer a fresh viewpoint on KR as informed by Peirce and our experience in building systems. For more balanced treatments, I recommend the excellent reference texts by van Harmelan et al. or Brachman and Levesque. Still, for those looking at the practical side of things, I hope this book may become an essential addition to theory and practice for KR and semantic technology. Peirce has a profound understanding of meaning and context that I believe is of benefit to knowledge management practitioners and AI researchers alike.

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 me directly for possible alternatives.

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headline:
Announcing My New Knowledge Representation Book

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Practical Guidance on How to Leverage Knowledge Graphs, Semantic Technologies, and KBpedia

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Michael K. Bergman announces his new book, A Knowledge Representation Practionary: Guidance from Charles Sanders Peirce. The book applies this guidance to the question of how to best represent human knowledge to computers. The book's practical guidelines should be of interest to any enterprise KM manager, AI researcher interested in knowledge, or Peirce scholar.

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