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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
