A Study of the Quality of Wikidata

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 lowquality 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.

User-friendly Comparison of Similarity Algorithms on Wikidata

“…Namely, when an editor introduces a new Qnode, it is useful to have metrics which can detect very similar existing entities and ask the editor to confirm that the new entity is different from the most similar existing ones [1]. This procedure would help to avoid introducing duplicates in Wikidata, which is a key challenge today, considering that millions of redirects have been introduced in Wikidata since its inception [9]. At the same time, similarity methods could be run over the current set of entities in Wikidata to detect potentially existing duplicates, which can be validated by an editor before their merging.…”