Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas

Alexopoulos, Panos

ISBN 10: 1492054275 / ISBN 13: 9781492054276

Edité par O'Reilly Media, 2020

pp. 306. N° de réf. du vendeur 18383813717

Synopsis :

What value does semantic data modeling offer? As an information architect or data science professional, letâ€Â(TM)s say you have an abundance of the right data and the technology to extract business goldâ€Â"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. Youâ€Â(TM)ll learn how to master this craft to increase the usability and value of your data and applications. Youâ€Â(TM)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

À propos de l?auteur: 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.

Panos has published several papers at international conferences, journals and books, and he is a regular speaker and trainer in both academic and industry venues, striving to bridge the gap between academia and industry so that they can benefit from each other.

Détails bibliographiques

Titre : Semantic Modeling for Data: Avoiding ...
Éditeur : O'Reilly Media
Date d'édition : 2020
Reliure : Couverture souple
Etat : New