# A Study of the Quality of Wikidata

arXiv: https://arxiv.org/abs/2107.00156 · HTML: https://mlsanity.com/paper/2107.00156/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Message Passing for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2009.10847/) (2020)
- [KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis](https://mlsanity.com/paper/2006.00088/) (2020)
- [Wikidata on MARS](https://mlsanity.com/paper/2008.06599/) (2020)
- [Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out](https://mlsanity.com/paper/2102.02307/) (2021)

## Built on by (most cited first, 30 of 35)

- [KGGen: Extracting Knowledge Graphs from Plain Text with Language Models](https://mlsanity.com/paper/2502.09956/) (2025)
- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions](https://mlsanity.com/paper/2407.11417/) (2024)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning](https://mlsanity.com/paper/2306.16296/) (2023)
- [WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs](https://mlsanity.com/paper/2209.13101/) (2022)
- [ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources](https://mlsanity.com/paper/2210.14846/) (2022)
- [Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidata](https://mlsanity.com/paper/2309.08491/) (2023)
- [Uncertainty Management in the Construction of Knowledge Graphs: a Survey](https://mlsanity.com/paper/2405.16929/) (2024)
- [Refining Wikidata Taxonomy using Large Language Models](https://mlsanity.com/paper/2409.04056/) (2024)
- [UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions](https://mlsanity.com/paper/2505.23461/) (2025)
- [Schema Generation for Large Knowledge Graphs Using Large Language Models](https://mlsanity.com/paper/2506.04512/) (2025)
- [LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data](https://mlsanity.com/paper/2510.23341/) (2025)
- [Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History](https://mlsanity.com/paper/2112.05003/) (2021)
- [Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs](https://mlsanity.com/paper/2206.08709/) (2022)
- [Enriching Wikidata with Linked Open Data](https://mlsanity.com/paper/2207.00143/) (2022)
- [Diagnosing and Mitigating Semantic Inconsistencies in Wikidata's Classification Hierarchy](https://mlsanity.com/paper/2511.04926/) (2025)
- [User-friendly Comparison of Similarity Algorithms on Wikidata](https://mlsanity.com/paper/2108.05410/) (2021)
- [Language Models As or For Knowledge Bases](https://mlsanity.com/paper/2110.04888/) (2021)
- [Creating Knowledge Graphs Subsets using Shape Expressions](https://mlsanity.com/paper/2110.11709/) (2021)
- [Does Wikidata Support Analogical Reasoning?](https://mlsanity.com/paper/2210.00620/) (2022)
- [Leveraging Wikidata's edit history in knowledge graph refinement tasks](https://mlsanity.com/paper/2210.15495/) (2022)
- [The X Types -- Mapping the Semantics of the Twitter Sphere](https://mlsanity.com/paper/2409.14584/) (2024)
- [Disjointness Violations in Wikidata](https://mlsanity.com/paper/2410.13707/) (2024)
- [Class Order Disorder in Wikidata and First Fixes](https://mlsanity.com/paper/2411.15550/) (2024)
- [A topic-aware graph neural network model for knowledge base updating](https://mlsanity.com/paper/2208.14601/) (2022)
- [A Knowledge Engineering Primer](https://mlsanity.com/paper/2305.17196/) (2023)
- [Standardizing Knowledge Engineering Practices with a Reference Architecture](https://mlsanity.com/paper/2404.03624/) (2024)
- [Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models](https://mlsanity.com/paper/2404.17000/) (2024)
- [Improving Knowledge Graph Embeddings through Contrastive Learning with Negative Statements](https://mlsanity.com/paper/2510.11868/) (2025)

## Abstract (arXiv)

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.