Singapore-based urban knowledge graph with site selection application

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...

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Main Author: Hong, Glenda Zixuan
Other Authors: Long Cheng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/162918
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1629182022-11-14T08:44:50Z Singapore-based urban knowledge graph with site selection application Hong, Glenda Zixuan Long Cheng School of Computer Science and Engineering c.long@ntu.edu.sg Engineering::Computer science and engineering 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. Bachelor of Engineering (Computer Science) 2022-11-14T03:26:54Z 2022-11-14T03:26:54Z 2022 Final Year Project (FYP) Hong, G. Z. (2022). Singapore-based urban knowledge graph with site selection application. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162918 https://hdl.handle.net/10356/162918 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Hong, Glenda Zixuan
Singapore-based urban knowledge graph with site selection application
description 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.
author2 Long Cheng
author_facet Long Cheng
Hong, Glenda Zixuan
format Final Year Project
author Hong, Glenda Zixuan
author_sort Hong, Glenda Zixuan
title Singapore-based urban knowledge graph with site selection application
title_short Singapore-based urban knowledge graph with site selection application
title_full Singapore-based urban knowledge graph with site selection application
title_fullStr Singapore-based urban knowledge graph with site selection application
title_full_unstemmed Singapore-based urban knowledge graph with site selection application
title_sort singapore-based urban knowledge graph with site selection application
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/162918
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