A Knowledge Representation Practionary (eBook)

Guidelines Based on Charles Sanders Peirce

Michael K. Bergman (Autor)

eBook Download: PDF

2018 | 1st ed. 2018
XVII, 462 Seiten
Springer International Publishing (Verlag)
978-3-319-98092-8 (ISBN)

Infos

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.

Michael K. Bergman is a senior principal for Cognonto Corporation, and lead editor for the open-source KBpedia knowledge structure. For more than a decade, his AI3:::Adaptive Information blog has been a leading go-to resource on topics in semantic technologies, large-scale knowledge bases for machine learning, data interoperability, knowledge graphs and mapping, and fact and entity extraction and tagging. For the past twenty years Mike has been an entrepreneur, Web scientist, and independent consultant. For the decade up to 2018, Mike was the CEO of Structured Dynamics LLC, which he co-founded with Fred Giasson.  Mike has held C-class positions and was a founder of the prior companies Zitgist LLC, BrightPlanet Corporation, VisualMetrics Corporation, and TheWebTools Company. These companies provided notable market advances in semantic technologies, data warehousing, the deep Web, large-scale Internet databases, meta-search tools, and bioinformatics. Bergman began his professional career in the mid-1970s as a project director for the U.S. EPA for a major energy study called the Coal Technology Assessment. He later taught in the Graduate School of Engineering at the University of Virginia, where he was a fellow in the Energy Policies Study Center. He then joined the American Public Power Association in 1982, where he rose to director of energy research. APPA's pioneering work with small computers sparked Bergman's transition to information technologies. Before entering industry, Mike was a doctoral candidate at Duke University in population genetics.

Inhalt

Dedication 5
Preface 6
In-line Citations 12
Contents 13
Chapter 1: Introduction 16
Structure of the Book 17
Overview of Contents 18
Key Themes 25
References 28
Chapter 2: Information, Knowledge, Representation 29
What Is Information? 30
Some Basics of Information 30
The Structure of Information 33
Forms of Structure 33
Some Structures Are More Efficient 34
Evolution Favors Efficient Structures 35
The Meaning of Information 37
What Is Knowledge? 41
The Nature of Knowledge 41
Knowledge as Belief 44
Doubt as the Impetus of Knowledge 46
What Is Representation? 47
The Shadowy Object 48
Three Modes of Representation 50
Peirce’s Semiosis and Triadomany 52
References 55
Part I: Knowledge Representation in Context 57
Chapter 3: The Situation 58
Information and Economic Wealth 59
Historical Breakpoints 59
The X Factor of Information 63
Knowledge and Innovation 64
Untapped Information Assets 67
Valuing Information as an Asset 68
Lost Value in Information 70
The Information Enterprise 72
Impediments to Information Sharing 74
Cultural Factors 74
Tooling and Technology 75
Perspectives and Priorities 76
References 76
Chapter 4: The Opportunity 78
KM and a Spectrum of Applications 79
Some Premises 79
Potential Applications 80
A Minimal Scaffolding 81
Data Interoperability 82
The Data Federation Pyramid 82
Benefits from Interoperability 84
A Design for Interoperating 85
Knowledge-Based Artificial Intelligence 87
Machine Learning 92
Knowledge Supervision 94
Feature Engineering 96
References 97
Chapter 5: The Precepts 98
Equal Class Data Citizens 99
The Structural View 100
The Formats View 101
The Content View 102
Addressing Semantic Heterogeneity 104
Sources of Semantic Heterogeneity 104
Role of Semantic Technologies 108
Semantics and Graph Structures 110
Carving Nature at the Joints 110
Forming ‘Natural’ Classes 111
A Mindset for Categorization 115
Connections Create Graphs 116
References 117
Part II: A Grammar for Knowledge Representation 118
Chapter 6: The Universal Categories 119
A Foundational Mindset 120
A Common Grounding in Peirce 120
Truth Is Testable and Fallible 121
Upper Ontologies, Context, and Perspective 122
Being Attuned to Nature 123
Firstness, Secondness, Thirdness 124
Constant Themes of Three 124
Summary of the Universal Categories 125
The Irreducible Triad 127
The Lens of the Universal Categories 128
An Aha! Moment 129
Grokking the Universal Categories 130
Applying the Universal Categories 135
The Categories and Categorization 136
References 138
Chapter 7: A KR Terminology 140
Things of the World 142
Entities, Attributes, and Concepts 142
What Is an Event? 144
Hierarchies in Knowledge Representation 146
Types of Hierarchical Relationships 147
Structures Arising from Hierarchies 152
A Three-Relation Model 154
Attributes, the Firstness of Relations 156
External Relations, the Secondness of Relations 157
Representations, the Thirdness of Relations 157
The Basic Statement 158
References 159
Chapter 8: KR Vocabulary and Languages 161
Logical Considerations 163
First-Order Logic and Inferencing 164
Deductive Logic 166
Inductive Logic 167
Abductive Logic 168
Redux: The Nature of Knowledge 170
Particulars, Generals, and Description Logics 172
Pragmatic Model and Language Choices 173
RDF: A Universal Solvent 173
OWL 2: The Knowledge Graph Language 175
W3C: Source for Other Standards 176
The KBpedia Vocabulary 177
Structured on the Universal Categories 177
Three Main Hierarchies 178
The Instances Vocabulary 178
The Relations Vocabulary 180
Attributes Relations (1ns) 184
External Relations (2ns) 184
Representation Relations (3ns) 185
The Generals (KR Domain) Vocabulary 186
Other Vocabulary Considerations 187
References 190
Part III: Components of Knowledge Representation 191
Chapter 9: Keeping the Design Open 192
The Context of Openness 193
An Era of Openness 194
The Open-World Assumption 198
Open Standards 201
Information Management Concepts 202
Things, Not Strings 203
The Idea and Role of Reference Concepts 204
Punning for Instances and Classes 208
Taming a Bestiary of Data Structs 209
Rationale for a Canonical Model 210
The RDF Canonical Data Model 211
Other Benefits from a Canonical Model 213
References 213
Chapter 10: Modular, Expandable Typologies 215
Types as Organizing Constructs 216
The Type-Token Distinction 216
Types and Natural Classes 218
Very-Fine-Grained Entity Types 220
A Flexible Typology Design 223
Construction of the Hierarchical Typologies 223
Typologies Are Modular 224
Typologies Are Expandable 226
KBpedia’s Typologies 227
Full Listing of Typologies 227
‘Core’ Typologies 230
Tailoring Your Own Typologies 234
References 234
Chapter 11: Knowledge Graphs and Bases 235
Graphs and Connectivity 236
Graph Theory 237
The Value of Connecting Information 239
Graphs as Knowledge Representations 244
Upper, Domain, and Administrative Ontologies 245
A Lay Introduction to Ontologies 246
Ontologies Are a Family of Graphs 247
Incipient Potentials 248
Good Ontology Design and Construction 249
KBpedia’s Knowledge Bases 250
KBpedia KBs 251
Primary KBs 251
Secondary KBs 253
Candidate KBs for Expansion 254
References 254
Part IV: Building KR Systems 256
Chapter 12: Platforms and Knowledge Management 257
Uses and Work Splits 258
The State of Tooling 258
TBox, ABox, and Work Splits 260
Content Workflows 265
Platform Considerations 268
Supporting Multiple Purposes 269
Search 269
Knowledge Management 270
An Ontology-Based Design 271
Enterprise Considerations 272
A Web-Oriented Architecture 274
Web Orientation and Standards 275
A Modular Web Service Design 275
An Interoperability Architecture 277
References 278
Chapter 13: Building Out the System 279
Tailoring for Domain Uses 280
A Ten-Point Checklist for Domain Use 280
An Inventory of Assets 281
Phased Implementation Tasks and Plan 282
Domain Knowledge Graph 283
Instance Data Population 284
Analysis and Content Processing 284
Use and Maintenance 285
Testing and Mapping 285
Documentation 286
Mapping Schema and Knowledge Bases 286
Mapping Methods and Tools 286
Building Out the Schema 287
Overview of Approaches 288
Some Design Guidelines 290
Be Lightweight and Modular 290
Use Reference Structures 291
Reuse Existing Structure 292
Build Incrementally 292
Use Simple Predicates 293
Test for Logic and Consistency 293
Map to External Ontologies 294
Building Out the Instances (Knowledge Bases) 294
Update Changing Knowledge 295
Process the Input KBs 295
Install, Run, and Update the System 295
Test and Vet Placements 295
Test and Vet Mappings 296
Test and Vet Assertions 296
Ensure Completeness 296
Test and Vet Coherence 296
Generate Training Sets 296
Test and Vet Learners 297
Rinse and Repeat 297
Pay as You Benefit 297
Placing the First Stake 298
Incremental Build-Outs Follow Benefits 298
Learn to Quantify and Document Benefits 299
References 299
Chapter 14: Testing and Best Practices 301
A Primer on Knowledge Statistics 302
Two Essential Metrics, Four Possible Values 302
Many Useful Statistics 305
Working Toward ‘Gold Standards’ 307
Builds and Testing 310
Build Scripts 311
Testing Scripts 312
Literate Programming 313
Some Best Practices 315
Data and Dataset Practices 316
Dataset Best Practices 316
Linked Data 317
Knowledge Structures and Management Practices 318
Organizational and Collaborative Best Practices 318
Naming and Vocabulary Best Practices 318
Best Ontology Practices 319
Testing, Analysis, and Documentation Practices 320
Testing Best Practices 320
Analytical Best Practices 320
Documentation Best Practices 321
References 322
Part V: Practical Potentials and Outcomes 323
Chapter 15: Potential Uses in Breadth 324
Near-Term Potentials 325
Word Sense Disambiguation 325
Relation Extraction 327
Reciprocal Mapping 328
Extreme Knowledge Supervision 330
Logic and Representation 332
Automatic Hypothesis Generation 332
Encapsulating KBpedia for Deep Learning 334
Measuring Classifier Performance 335
Thermodynamics of Representation 336
Potential Methods and Applications 337
Self-Service Business Intelligence 337
Semantic Learning 338
Nature as an Information Processor 340
Gaia Hypothesis Test 342
References 344
Chapter 16: Potential Uses in Depth 347
Workflows and BPM 347
Concepts and Definitions 349
The BPM Process 350
Optimal Approaches and Outcomes 351
Semantic Parsing 353
A Taxonomy of Grammars 354
Computational Semantics 357
Three Possible Contributions Based on Peirce 358
Peircean POS Tagging 359
Machine Learning Understanding Based on Peirce 362
Peirce Grammar 363
Cognitive Robotics and Agents 365
Lights, Camera, Action! 366
Grounding Robots in Reality 369
Robot as Pragmatist 370
References 371
Chapter 17: Conclusion 374
The Sign and Information Theoretics 375
Peirce: The Philosopher of KR 376
Knowledge and Peirce 377
Time to Move from Theory to Practice 378
Reasons to Question Premises 380
AI Is a Field of KR 380
Hurdles to Be Overcome 381
Of Crystals and Robots 382
References 383
Appendix A: Perspectives on Peirce 384
Peirce, the Person 385
Peirce, the Philosopher 388
Peirce’s Architectonic 388
Chance, Existents, and Continuity: Real 390
Chance 391
Existents 392
Continuity 393
What Is Real 395
Leaning into Pragmatism 395
Peirce, the Polymath 396
Mathematics 397
Cenoscopy 398
Idioscopy 398
Scientist 399
Inventor 399
Humanist, as Person 400
An Obsession with Terminology 401
Peirce, the Polestar 402
Resources About Peirce 404
References 409
Appendix B: The KBpedia Resource 411
Components 412
The KBpedia Knowledge Ontology (KKO) 413
The KBpedia Knowledge Bases 413
The KBpedia Typologies 415
Structure 416
Capabilities and Uses 420
Appendix C: KBpedia Feature Possibilities 422
What Is a Feature? 423
A (Partial) Inventory of Natural Language and KB Features 424
Feature Engineering for Practical Limits 432
Considerations for a Feature Science 433
Role of a Platform 434
Glossary 436
Index 451

Produkt-Details

• Erscheint lt. Verlag: 12.12.2018
• Zusatzinfo: XVII, 462 p. 28 illus., 16 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Wirtschaftsinformatik
• Schlagworte: Artificial Intelligence • Charles Sanders Peirce • data interoperability • KBpedia • knowledge base • knowledge-based artificial intelligence • Knowledge graph • knowledge management • Knowledge Representation • Ontology • OWL • RDF • semantic technologies • semantic web
• ISBN-10: 3-319-98092-0 / 3319980920
• ISBN-13: 978-3-319-98092-8 / 9783319980928