Textkernel takes great pride in our strong ties to the academic community, particularly since we started from humble beginnings in 2001 as a private, commercial R&D spin-off with a focus on research into Natural Language Processing and Machine Learning at the Universities of Tilburg, Antwerp and Amsterdam.
And so with great excitement, our Head of Ontology, Panos Alexopoulos, has announced the release of his book: “Semantic Modeling for Data – Avoiding Pitfalls and Breaking Dilemmas”. Published by O’Reilly, the book serves as a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world.
The book is available for purchase global from all good online and physical book retailers. For a complete list of where to buy, visit Panos’ site.
We sat down with Panos to discuss a bit about how the concept of the book came about, and what readers can expect:
Avoiding Pitfalls and Breaking Dilemmas
“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,” Panos explains.
“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 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.”
The role of early feedback
“It’s always important when you write a book to get early feedback, and what O’Reiley, my publisher, allows you to do is provide the book online, provide some raw and unedited content on he platform so that any users can see the book and are able to share their opinion, give their feedback, find mistakes, find things that may be wrong or may want more information. That’s extremely useful feedback because in the end it’s all about removing ambiguity. And because this book is not addressed to only one community, it’s actually a wider community and there are different sub-communities in the data world that don’t necessarily use the same terminology or necessarily have the same experiences it’s important that all these sub communities have a an opportunity to say something about the book.”
“Semantic Modeling for Data” is expected to be published in November, and is currently available as an early release version.