# Leveraging Wikidata's edit history in knowledge graph refinement tasks

arXiv: https://arxiv.org/abs/2210.15495 · HTML: https://mlsanity.com/paper/2210.15495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Multi-relational Poincar'e Graph Embeddings](https://mlsanity.com/paper/1905.09791/) (2019)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework](https://mlsanity.com/paper/2006.13365/) (2020)
- [NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding](https://mlsanity.com/paper/1812.06410/) (2018)
- [A Study of the Quality of Wikidata](https://mlsanity.com/paper/2107.00156/) (2021)
- [Incorporating GAN for Negative Sampling in Knowledge Representation Learning](https://mlsanity.com/paper/1809.11017/) (2018)
- [Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1708.06816/) (2017)
- [pyRDF2Vec: A Python Implementation and Extension of RDF2Vec](https://mlsanity.com/paper/2205.02283/) (2022)
- [Supervised Typing of Big Graphs using Semantic Embeddings](https://mlsanity.com/paper/1703.07805/) (2017)
- [Learning to Recommend Items to Wikidata Editors](https://mlsanity.com/paper/2107.06423/) (2021)
- [Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding](https://mlsanity.com/paper/2010.14227/) (2020)
- [Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History](https://mlsanity.com/paper/2112.05003/) (2021)
- [Using Description Logics for RDF Constraint Checking and Closed-World Recognition](https://mlsanity.com/paper/1411.4156/) (2014)

## Built on by (most cited first, 1 of 1)

- [Blocks Architecture (BloArk): Efficient, Cost-Effective, and Incremental Dataset Architecture for Wikipedia Revision History](https://mlsanity.com/paper/2410.04410/) (2024)

## Abstract (arXiv)

Knowledge graphs have been adopted in many diverse fields for a variety of purposes. Most of those applications rely on valid and complete data to deliver their results, pressing the need to improve the quality of knowledge graphs. A number of solutions have been proposed to that end, ranging from rule-based approaches to the use of probabilistic methods, but there is an element that has not been considered yet: the edit history of the graph. In the case of collaborative knowledge graphs (e.g., Wikidata), those edits represent the process in which the community reaches some kind of fuzzy and distributed consensus over the information that best represents each entity, and can hold potentially interesting information to be used by knowledge graph refinement methods. In this paper, we explore the use of edit history information from Wikidata to improve the performance of type prediction methods. To do that, we have first built a JSON dataset containing the edit history of every instance from the 100 most important classes in Wikidata. This edit history information is then explored and analyzed, with a focus on its potential applicability in knowledge graph refinement tasks. Finally, we propose and evaluate two new methods to leverage this edit history information in knowledge graph embedding models for type prediction tasks. Our results show an improvement in one of the proposed methods against current approaches, showing the potential of using edit information in knowledge graph refinement tasks and opening new promising research lines within the field.