The Semantic Web as an extension of the World Wide Web (WWW) represents an effective means of data representation and enables users and computers to retrieve and share information efficiently. The Resource Description Framework (RDF) is the foundational data model for Semantic Web. Unlike traditional databases, such as relational ones, where data has to adhere to a fixed schema, RDF documents are not prescribed by a schema and can be described without additional information making RDF data model self-describing. To learn more about RDF, you can read one of my previous blog posts on data modelling with RDF.
Linked Data lies at the heart of what Semantic Web is all about: large scale integration of, and reasoning on, data on the Web. Almost all applications listed in, say collection of Semantic Web Case Studies and Use Cases are essentially based on the accessibility of, and integration of Linked Data at various level of complexities.
Moreover, the unique potential which the Semantic Web and Linked Data offer to electronic lexicography enables interoperability across lexical resources by leveraging printed or unstructured linguistic data to machine-readable semantic formats.
Semantic web and linked data facilitate retrieving information from huge resources such as printed dictionaries (Photo taken at DSL in Copenhagen)
We present 10 essential queries in SPARQL, an RDF query language, for lexicographical purposes to retrieve information. To this end, we use the SPARQL endpoint of Wikidata which comes with a few lexeme queries as example, too.
Unfortunately, not all languages are equally represented on Wikipedia. In this tutorial, we focus on some of the richly represented ones, e.g. English and French. So, if you modify the queries to work on another language, make sure that your language is sufficiently represented on Wikidata before double-checking the soundness of the syntax of your queries.
1- Retrieve lexemes describing book(L536) in different languages