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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2022
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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 |
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Engineering::Computer science and engineering Hong, Glenda Zixuan Singapore-based urban knowledge graph with site selection application |
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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. |
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Long Cheng |
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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 |
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Singapore-based urban knowledge graph with site selection application |
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Singapore-based urban knowledge graph with site selection application |
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singapore-based urban knowledge graph with site selection application |
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Nanyang Technological University |
publishDate |
2022 |
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https://hdl.handle.net/10356/162918 |
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1751548545009188864 |