What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics.
In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data.
• Understand the fundamental concepts, phenomena, and processes related to semantic data modeling
• Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools
• Avoid mistakes and bad practices that can undermine your efforts to create good data models
• Learn about model development dilemmas, including representation, expressiveness and content, development, and governance
• Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges
Author(s): Panos Alexopoulos
Edition: 1
Publisher: O'Reilly Media
Year: 2020
Language: English
Commentary: Vector PDF
Pages: 330
City: Sebastopol, CA
Tags: Artificial Intelligence; Natural Language Processing; Data Science; Linguistics; Metric Analysis; Ontologies; Semantic Analysis; Model Evaluation; Data Modeling; Strategy; Semantic Web; Quality Management; Knowledge Acquisition
Cover
Copyright
Table of Contents
Preface
Who Should Read This Book
What to Expect in This Book
Book Outline
Conventions Used in This Book
O’Reilly Online Learning
How to Contact Us
Acknowledgments
Part I. The Basics
Chapter 1. Mind the Semantic Gap
What Is Semantic Data Modeling?
Why Develop and Use a Semantic Data Model?
Bad Semantic Modeling
Avoiding Pitfalls
Breaking Dilemmas
Chapter 2. Semantic Modeling Elements
General Elements
Entities
Relations
Classes and Individuals
Attributes
Complex Axioms, Constraints, and Rules
Terms
Common and Standardized Elements
Lexicalization and Synonymy
Instantiation
Meaning Inclusion and Class/Relation Subsumption
Part-Whole Relation
Semantic Relatedness
Mapping and Interlinking Relations
Documentation Elements
Summary
Chapter 3. Semantic and Linguistic Phenomena
Ambiguity
Uncertainty
Vagueness
Rigidity, Identity, Unity, and Dependence
Symmetry, Inversion, and Transitivity
Closed- and Open-World Assumptions
Semantic Change
Summary
Chapter 4. Semantic Model Quality
Semantic Accuracy
Completeness
Consistency
Conciseness
Timeliness
Relevancy
Understandability
Trustworthiness
Availability, Versatility, and Performance
Summary
Chapter 5. Semantic Model Development
Development Activities
Setting the Stage
Deciding What to Build
Building It
Ensuring It’s Good
Making It Useful
Making It Last
Vocabularies, Patterns, and Exemplary Models
Upper Ontologies
Design Patterns
Standard and Reference Models
Public Models and Datasets
Semantic Model Mining
Mining Tasks
Mining Methods and Techniques
Summary
Part II. The Pitfalls
Chapter 6. Bad Descriptions
Giving Bad Names
Setting a Bad Example
Why We Give Bad Names
Pushing for Clarity
Omitting Definitions or Giving Bad Ones
When You Need Definitions
Why We Omit Definitions
Good and Bad Definitions
How to Get Definitions
Ignoring Vagueness
Vagueness Is a Feature, Not a Bug
Detecting and Describing Vagueness
Not Documenting Biases and Assumptions
Keeping Your Enemies Close
Summary
Chapter 7. Bad Semantics
Bad Identity
Bad Synonymy
Bad Mapping and Interlinking
Bad Subclasses
Instantiation as Subclassing
Parts as Subclasses
Rigid Classes as Subclasses of Nonrigid Classes
Common Superclasses with Incompatible Identity Criteria
Bad Axioms and Rules
Defining Hierarchical Relations as Transitive
Defining Vague Relations as Transitive
Complementary Vague Classes
Mistaking Inference Rules for Constraints
Summary
Chapter 8. Bad Model Specification and Knowledge Acquisition
Building the Wrong Thing
Why We Get Bad Specifications
How to Get the Right Specifications
Bad Knowledge Acquisition
Wrong Knowledge Sources
Wrong Acquisition Methods and Tools
A Specification and Knowledge Acquisition Story
Model Specification and Design
Model Population
Summary
Chapter 9. Bad Quality Management
Not Treating Quality as a Set of Trade-Offs
Semantic Accuracy Versus Completeness
Conciseness Versus Completeness
Conciseness Versus Understandability
Relevancy to Context A Versus Relevancy to Context B
Not Linking Quality to Risks and Benefits
Not Using the Right Metrics
Using Metrics with Misleading Interpretations
Using Metrics with Little Comparative Value
Using Metrics with Arbitrary Value Thresholds
Using Metrics That Are Actually Quality Signals
Measuring Accuracy of Vague Assertions in a Crisp Way
Equating Model Quality with Information Extraction Quality
Summary
Chapter 10. Bad Application
Bad Entity Resolution
How Entity Resolution Systems Use Semantic Models
When Knowledge Can Hurt You
How to Select Disambiguation-Useful Knowledge
Two Entity Resolution Stories
Bad Semantic Relatedness
Why Semantic Relatedness Is Tricky
How to Get the Semantic Relatedness You Really Need
A Semantic Relatedness Story
Summary
Chapter 11. Bad Strategy and Organization
Bad Strategy
What Is a Semantic Model Strategy About?
Buying into Myths and Half-Truths
Underestimating Complexity and Cost
Not Knowing or Applying Your Context
Bad Organization
Not Building the Right Team
Underestimating the Need for Governance
Summary
Part III. The Dilemmas
Chapter 12. Representation Dilemmas
Class or Individual?
To Subclass or Not to Subclass?
Attribute or Relation?
To Fuzzify or Not to Fuzzify?
What Fuzzification Involves
When to Fuzzify
Two Fuzzification Stories
Summary
Chapter 13. Expressiveness and Content Dilemmas
What Lexicalizations to Have?
How Granular to Be?
How General to Be?
How Negative to Be?
How Many Truths to Handle?
How Interlinked to Be?
Summary
Chapter 14. Evolution and Governance Dilemmas
Model Evolution
Remember or Forget?
Run or Pace?
React or Prevent?
Knowing and Acting on Your Semantic Drift
Model Governance
Democracy, Oligarchy, or Dictatorship?
A Centralization Story
Summary
Chapter 15. Looking Ahead
The Map Is Not the Territory
Being an Optimist, but Not Naïve
Avoiding Tunnel Vision
Avoiding Distracting Debates
Semantic Versus Nonsemantic Frameworks
Symbolic Knowledge Representation Versus Machine Learning
Doing No Harm
Bridging the Semantic Gap
Bibliography
Glossary
Index
About the Author
Colophon
