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.

本研究提出了一个基于 Wikidata 的大型知识图谱的口语化理解数据集 WDV，以填补当前 KG 口语化数据集在丰富性和紧密耦合性方面存在的空缺，同时通过衡量人类可读程度和足够性等可重复的工作流程评估了信息转化的质量。