Leveraging Wikidata's edit history in knowledge graph refinement tasks

TL;DR

This paper investigates how Wikidata's edit history can be utilized to enhance knowledge graph refinement, specifically improving type prediction accuracy through novel embedding methods that leverage community-driven edit information.

Contribution

It introduces a new dataset of Wikidata edit histories and proposes two methods that incorporate this data into knowledge graph embedding models for type prediction.

Findings

One method outperforms current approaches in type prediction.

Edit history information improves knowledge graph refinement.

Potential for new research directions in knowledge graph quality enhancement.

Abstract

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.