Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas

Perhaps you're an information architect on a mission to make your organization's data more understandable and usable across applications. Or a knowledge engineer working to infuse domain knowledge into the next Alexa or Siri. Or a machine learning expert having difficulty obtaining the right data for your models. If you pursue these or similar tasks, this is your book.

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 and increase the usability and value of your data and applications. With this practical and comprehensive field guide, you'll understand the pitfalls to avoid and dilemmas to overcome to build high-quality and valuable semantic representations of data.

• Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage 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

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