Knowledge graph-based entity importance learning for multi-stream regression on Australian fuel price forecasting

A knowledge graph (KG) represents a collection of interlinked descriptions of entities. It has become a key focus for organising and utilising this type of data for applications. Many graph embedding techniques have been proposed to simplify the manipulation while preserving the inherent structure of the KG. However, scant attention has been given to the investigation of the importance of the entities (the nodes of KGs). In this paper, we propose a novel entities importance learning framework that investigates how to weight the entities and use them as a prior knowledge for solving multi-stream regression problems. The framework consists of KG feature extraction, multi-stream correlation analysis, and entity importance learning. To evaluate the proposed method, we implemented the framework based on Wikidata and applied it to Australian retail fuel price forecasting. The experiment results indicate that the proposed method reduces prediction error, which supports the weighted knowledge graph information as a means for improving machine learning model accuracy.