Toward Predicting Impact of Changes in Evolving Knowledge Graphs

Abstract

The updates on knowledge graphs (KGs) affect the services built on top of them. However, changes are not all the same: some updates drastically change the result of operations based on knowledge graph content; others do not lead to any variation. Estimating the impact of a change ex-ante is highly important, as it might make KG engineers aware of the consequences of their action during KG editing or may be used to highlight the importance of a new fragment of knowledge to be added to the KG for some application.
The main goal of this contribution is to offer a formalization of the problem. Additionally, it presents some preliminary experiments on three different datasets considering embeddings as operation.Results show that the estimation can reach AUCs of 0.85, suggesting the feasibility of this research.