Leveraging Wikidata's edit history in knowledge graph refinement tasks
- URL: http://arxiv.org/abs/2210.15495v1
- Date: Thu, 27 Oct 2022 14:32:45 GMT
- Title: Leveraging Wikidata's edit history in knowledge graph refinement tasks
- Authors: Alejandro Gonzalez-Hevia, Daniel Gayo-Avello
- Abstract summary: edit history represents the process in which the community reaches some kind of fuzzy and distributed consensus.
We build a dataset containing the edit history of every instance from the 100 most important classes in Wikidata.
We propose and evaluate two new methods to leverage this edit history information in knowledge graph embedding models for type prediction tasks.
- Score: 77.34726150561087
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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