This is the home page for the PLDN Linked Data ThesaurusDe PLDN Linked Data Thesaurus (PLDN-LDT) is een SKOS-thesaurus die de Linked Data-termen beschrijft die we in onze PLDN-community gebruiken Working Group.
This thesaurus will become part of the PLDN website (and possibly also of BegrippenXL).
Basically, it is a next version of the Expertise list on this PLDN-website, but then with Dutch and English labels, additional explanation (when needed), a link (or links) to additional sources where you can find more details (like the W3C Recommendations or the list of open standards from Forum StandaardisatieForum en College Standaardisatie bevorderen interoperabiliteit en de toepassing van open standaarden binnen de Nederlandse overheid. Digitale systemen zijn interoperabel als gegevens onderling kunnen worden uitgewisseld. Het College beheert de lijst met aanbevolen en verplichte open standaarden die gelden voor de gehele publieke sector.), some additional SKOS relationships when appropriate (broader, narrower and related to relationships) and optionally an image and/or video that might give more clarity for a term in the thesaurus.
The ambition is to have a first version of this thesaurus in April 2020 and we are looking for contributors and reviewers that would like to partcipate in this working group. Please contact Pieter van Everdingen via firstname.lastname@example.org when you would like to join this working group.
More content will be added on the short term on this working group page. But, for a first impression of this thesaurus, navigate to the key term (sleutelterm in Dutch) Linked Data to get an idea of the scope and the structure of this thesaurus. Working from this key term we will develop this thesaurus 'inside out', working from the core Linked Data terms to broader related terms, but only when those terms are related to discussions and activities in our PLDN community and/or our network (e.g. AR/VR-related activities that make use of Linked Data sources).
The aim is that thesaurus users can easily find related information from the alfabetical Expertise list or via a specific term, where each term might provide additional information in such a way that you can easily find your way to the right resources and details (more expected and possible also unexpected data discovery via the follow-your-nose principle and hopefully resulting in increased levels of Linked Data literacy).