The book is about the broader topic of Semantic Data Modeling which is actually the task and problem of creating representations and structures of data in a way that the meaning of the data is explicit and commonly shared and understood by both systems and humans. That’s a general challenge that information technology has, and especially now with AI technology in place, it’s important that meaning is understood in an explicit way by humans and machines. The book actually fills a gap in the literature and the market, especially when it comes to book about practitioners and professionals. There are several academic books describing how to build an ontology, what is the underlying theory behind data semantics etcetera, but the problem is usually this information is sparse, all around the place, either in papers or in presentations, so it’s never gathered together. What is lacking is the industry perspective, the perspective from the side of a practitioner - what it means to build, use and maintain these kinds of models in the real world, in organizations in the industry. My work here at Textkernel has been one of the key inspirations of the book, so many of the things I’ve seen here both positive and negative have contributed to me being a better modeler and professional, and I wanted to share these experiences with the rest of the community. That’s how the book was born.
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Panos Alexopoulos - Semantic Modeling for Data - What the book is about
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