A Study of the Quality of Wikidata

TL;DR

This paper presents a framework for detecting low-quality data in Wikidata by analyzing community consensus, deprecated statements, and constraint violations, revealing key challenges in data quality.

Contribution

It introduces a novel framework combining multiple indicators to identify low-quality statements in Wikidata, aiding quality improvement efforts.

Findings

Identified challenges with duplicate entities and missing triples.

Detected violations of type rules and taxonomic distinctions.

Provided insights to support community-driven data quality improvements.

Abstract

Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results. In this paper, we develop a framework to detect and analyze low-quality statements in Wikidata by shedding light on the current practices exercised by the community. We explore three indicators of data quality in Wikidata, based on: 1) community consensus on the currently recorded knowledge, assuming that statements that have been removed and not added back are implicitly agreed to be of low quality; 2) statements that have been deprecated; and 3) constraint violations in the data. We combine these indicators to detect low-quality statements, revealing challenges with duplicate entities, missing triples, violated type rules, and taxonomic distinctions. Our findings complement ongoing efforts by the Wikidata community to improve data quality, aiming to make it easier for users and editors to find and correct mistakes.