Book Review - Semantic Modeling for Data - Avoiding Pitfalls and Breaking Dilemmas (Panos Alexopoulos)

"Ontology is the branch of philosophy that studies concepts such as existence, being, becoming, and reality. It includes the questions of how entities are grouped into basic categories and which of these entities exist on the most fundamental level." (Wikipedia)

Ontology from a professional stand-point, is a topic that requires a deep understanding of domains and clear objectives of purpose. Understanding the requirement and basis which the development require a solid approach and orientation.

For those part of this journey on ontology / knowledge engineering projects, the book "Semantic Modeling for Data (Panos Alexopoulos)" is a high quality reference to consider. In this review share my perspectives on the same.

About the Author: Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, he currently works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. (link)

Book Overview (by Publisher - O'Reilly)

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 .

Book Review and Perspectives

1. In today's world of AI capability builds and capability, it is important to realize domain knowledge
2. Understanding the intrinsic data patterns play a critical role for effective model building
3. Data models and its effective usage in building models are required for domain specific models and its effectiveness
4. The architecture and design of robust AI/ML solutions require the intricate data plus domain understanding coupled with model, hence the play for semantic modeling.
5. The design level approaches will require a balanced and rigor based approach.
6. Most people would have figured out during the data engineering/ processing the critical need to categorize / structure data. Hence what are the key aspects to keep in mind (hence -> quality, avoid pitfalls and breaking dilemmas !)
7. Ontology perfection is as much as the beholder of the vision requires it (assuming there are high levels of alignment across stake holders). This sensitivity must be kept in mind. Certain decisions will impact this and will need to be taken for domain coverage and consistency.
8. Guiding principles for effective ontology models are covered well in the book with good examples. These perspectives will help you take learnings to your specific projects.
9. One of the important areas to address is ambiguous or inaccurate scenarios during knowledge acquisition. There needs to be guidelines to be considered for this to be minimized.
10. While many a times we push for a consensus in domain representation, there needs to be a mechanism to accept disagreement and how to address this in the design (yes there will be trade-offs)
11. Approach for tasks such as extraction need to be considered along with putting into appropriate and usable knowledge representation
12. Models need to be tested constantly and refactored/optimized as required. This is a constant process and not a one-time approach
13. The book also provides good examples of approaches and capability builds shared by the author. These provide very clear situational case studies and solutions adopted
14. The coverage of the book is also well structured and detailed with the key topics such as semantic modeling elements, semantic linguistic phenomena (ambiguity, uncertainty, vagueness, etc.), model quality, model development, pitfalls and dilemmas.

Conclusion: This book is a worthy reference book both from a guidance as well as a practitioner perspective for those interested in Ontology / Knowledge Engineering related areas.