Ontology Engineering

This book serves as a foundational guide to ontology engineering, addressing the challenges posed by vast data volumes and the need for structured frameworks.

In today's rapidly evolving digital landscape, ontologies play a crucial role in managing the vast amounts of data generated daily. With the rise of knowledge graphs, machine learning, and natural language processing (NLP), the need for structured and semantically rich frameworks has never been more pressing. As of 2014, an astounding 90% of the data in the digital universe was created in just the previous two years, with projections of growth from 3.2 zettabytes to 40 zettabytes within six years. This overwhelming volume of information poses significant challenges for organizations aiming to make informed decisions.

Ontology Engineering addresses these challenges by emphasizing the importance of unambiguous semantics in data integration and manipulation. The book highlights how ontologies can serve as a foundational schema for knowledge graphs, enhancing the effectiveness of NLP and learning algorithms. However, it also acknowledges the common pitfalls in ontology projects, often stemming from a lack of discipline in the development process. By providing insights into best practices, the book aims to improve the success rate of ontology initiatives.

This work is inspired by years of teaching the Ontology 101 tutorial at the Semantic Technology Conference and a university course on the subject. It is designed to be both a comprehensive textbook for students and a valuable resource for professionals interested in ontology engineering. Readers will gain a solid grounding in the principles and practices necessary for effective ontology development and implementation.