Natural Language Processing for the Semantic Web

Where Language Meets the Semantic Web 🧠

Natural language processing (NLP) and Semantic Web technologies play different but complementary roles in managing data. This book shows how combining them allows structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data into meaningful representations, and NLP provides the tools to extract that meaning from text. The result: applications that connect text to Linked Open Data, link texts to each other, enable semantic searching, and model user behavior in online networks.

Building Blocks of NLP

Written for non-experts, the first half of the book introduces the fundamental components of NLP: tokenization, part-of-speech tagging, and morphological analysis. It then builds on these with the main tools required for information extraction systems, such as named entity recognition and relation extraction. Each concept is presented as a building block, helping readers understand not just what the technology does, but why it is needed and how it fits into larger applications.

Integrating NLP and Semantic Web 🔗

The second half explores how Semantic Web and NLP technologies can enhance each other. Topics include semantic annotation, ontology linking and population, and sentiment analysis—a key component for making sense of textual data. The authors also address the difficulties of performing NLP on social media text and propose some solutions. The book concludes with applications such as semantic search, visualization, and modeling user behavior, along with an outlook on future developments.

For Developers, Researchers, and Students

This book is designed for those who want to understand how to apply NLP within Semantic Web applications. It assumes no prior expertise in either area, making it accessible to newcomers while still offering depth for practitioners. A comprehensive bibliography and clear explanations make it a useful reference for anyone working with text and structured data.

About the Authors

Diana Maynard and Kalina Bontcheva are researchers at the University of Sheffield, and Isabelle Augenstein is at University College London. Their combined expertise ensures the material is both current and authoritative.