WDV: A Broad Data Verbalisation Dataset Built from Wikidata

TL;DR

This paper introduces WDV, a comprehensive dataset for verbalising Wikidata's knowledge graph triples into human-readable text, addressing existing gaps and supporting NLP research.

Contribution

We created WDV, a large, diverse Wikidata claim verbalisation dataset with high-quality text-triple coupling and an evaluation workflow for fluency and adequacy.

Findings

WDV covers a wide range of entities and predicates.

The dataset demonstrates high human-centred quality scores.

Open data and tools support further KG verbalisation research.

Abstract

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.