  Data verbalisation is a task of great importance in the current field of
natural language processing, as there is great benefit in the transformation of
our abundant structured and semi-structured data into human-readable formats.
Verbalising Knowledge Graph (KG) data focuses on converting interconnected
triple-based claims, formed of subject, predicate, and object, into text.
Although KG verbalisation datasets exist for some KGs, there are still gaps in
their fitness for use in many scenarios. This is especially true for Wikidata,
where available datasets either loosely couple claim sets with textual
information or heavily focus on predicates around biographies, cities, and
countries. To address these gaps, we propose WDV, a large KG claim
verbalisation dataset built from Wikidata, with a tight coupling between
triples and text, covering a wide variety of entities and predicates. We also
evaluate the quality of our verbalisations through a reusable workflow for
measuring human-centred fluency and adequacy scores. Our data and code are
openly available in the hopes of furthering research towards KG verbalisation.
