Singapore-based urban knowledge graph with site selection application

Abstract

In recent years, knowledge graphs (KGs) have seen a rise in popularity, with major organisations jumping on the trend to create their own KGs. This is no surprise given the advantages that KGs have over other data models and the huge role they play in knowledge-driven machine learning paradigms. An interesting subset of KGs is urban KGs (UrbanKG), which are KGs developed from multi-source urban data. These UrbanKGs can be used to tackle urban machine learning tasks, an example of this being site selection within an urban city.

This project aims to bring KGs and its applications to a Singapore context. Through the course of this project, a Singapore-based UrbanKG, centred around businesses in Singapore, was constructed. Additionally, to demonstrate a possible application of the UrbanKG, a site selection machine learning model and web application was also developed.