Semantic Modeling for dаta: Avoiding Pitfalls and Breaking Dilemmas

Semantic Modeling for dаta: Avoiding Pitfalls and Breaking Dilemmas

Book Description
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


I. The Basics
1. Mind the Semantic Gap
2. Semantic Modeling Elements
3. Semantic and Linguistic Phenomena
4. Semantic Model Quality
5. Semantic Model Development

II. The Pitfalls
6. Bad Descriptions
7. Bad Semantics
8. Bad Model Specification and Knowledge Acquisition
9. Bad Quality Management
10. Bad Application
11. Bad Strategy and Organization

III. The Dilemmas
12. Representation Dilemmas
13. Expressiveness and Content Dilemmas
14. Evolution and Governance Dilemmas
15. Looking Ahead

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