Focusing on the development and application of ontologies, this book explores their significance in the semantic web and various fields such as knowledge management, information retrieval, text clustering, and classification. It delves into algorithms for ontology learning and population from text, offering evaluations and practical applications that enhance understanding and usability in computational contexts.
This monograph explores the emerging field of linguistic linked data, offering both foundational knowledge and practical implementation advice. It emphasizes the advantages of applying linked data (LD) principles to linguistic resources, advocating for a networked approach that enhances the utility of individual resources. The book begins by laying the groundwork for understanding LD, covering essential data models, ontologies, and query languages integral to the Semantic Web, along with an overview of the Linguistic Linked Data Cloud.
Subsequent sections delve into the modeling of language resources using LD principles, detailing the use of Ontolex-lemon for lexical resources and methods for annotating and addressing text elements in RDF. It also addresses how to model annotations and capture metadata, including a chapter on linguistic categories. The authors further illustrate the transformation of language resources into LD, exploring how to enhance connectivity among datasets and the application of LD in natural language processing.
The final section presents practical applications, including the representation and linking of multilingual wordnets, contributions to digital humanities, and the discovery of language resources. This comprehensive work is aimed at researchers and graduate students at the intersection of natural language processing and the Semantic Web, as well as Semantic Web experts and computational ling
