A knowledge graph based survey system for Ikigai
Discoveries on the positive health-related benefits and outcomes associated with ikigai, translated as “life worth living”, has sparked widespread interests in the Japanese concept. More than ever, individuals are seeking to identify and measure their ikigai while researchers are studying ikigai as...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/156437 |
| _version_ | 1826122568097071104 |
|---|---|
| author | Lua, Emily Jia Ning |
| author2 | Miao Chun Yan |
| author_facet | Miao Chun Yan Lua, Emily Jia Ning |
| author_sort | Lua, Emily Jia Ning |
| collection | NTU |
| description | Discoveries on the positive health-related benefits and outcomes associated with ikigai, translated as “life worth living”, has sparked widespread interests in the Japanese concept. More than ever, individuals are seeking to identify and measure their ikigai while researchers are studying ikigai as a psychological construct to better understand this Japanese concept surrounding the purpose of life. While there are a myriad of models and methodologies available to help people discover their life worthiness, they are often simplistic and structured in the form of traditional questionnaire that are known to be less interactive, dynamic and informative than open-ended interviews. At the same time, recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for researchers. Hence, this Final Year Project leveraged the synergies between the ever-growing artificial intelligence field and a complex, intricate social science concept like ikigai to create a knowledge graph based survey system for ikigai, to address the current gaps in existing ikigai measurement tools and models, while adopting best practices from intelligent survey systems found in past research work.
In particular, the knowledge graph based survey system for ikigai consist of 3 main features: (i) creation of equivalent questions to measure and improve on the quality of responses, (ii) responsive question selection to provide a dynamic survey experience and (iii) addition of new questions to account for possible new explorations and discoveries in the field of ikigai. In a time where more are invested in the concept of life worthiness, the knowledge graph based survey system for ikigai provide a better way to measure one’s ikigai and is one with exciting and far reaching use-cases. |
| first_indexed | 2024-10-01T05:50:35Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156437 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:50:35Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1564372022-04-16T13:38:40Z A knowledge graph based survey system for Ikigai Lua, Emily Jia Ning Miao Chun Yan School of Computer Science and Engineering ASCYMiao@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Discoveries on the positive health-related benefits and outcomes associated with ikigai, translated as “life worth living”, has sparked widespread interests in the Japanese concept. More than ever, individuals are seeking to identify and measure their ikigai while researchers are studying ikigai as a psychological construct to better understand this Japanese concept surrounding the purpose of life. While there are a myriad of models and methodologies available to help people discover their life worthiness, they are often simplistic and structured in the form of traditional questionnaire that are known to be less interactive, dynamic and informative than open-ended interviews. At the same time, recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for researchers. Hence, this Final Year Project leveraged the synergies between the ever-growing artificial intelligence field and a complex, intricate social science concept like ikigai to create a knowledge graph based survey system for ikigai, to address the current gaps in existing ikigai measurement tools and models, while adopting best practices from intelligent survey systems found in past research work. In particular, the knowledge graph based survey system for ikigai consist of 3 main features: (i) creation of equivalent questions to measure and improve on the quality of responses, (ii) responsive question selection to provide a dynamic survey experience and (iii) addition of new questions to account for possible new explorations and discoveries in the field of ikigai. In a time where more are invested in the concept of life worthiness, the knowledge graph based survey system for ikigai provide a better way to measure one’s ikigai and is one with exciting and far reaching use-cases. Bachelor of Engineering (Computer Science) 2022-04-16T13:38:40Z 2022-04-16T13:38:40Z 2022 Final Year Project (FYP) Lua, E. J. N. (2022). A knowledge graph based survey system for Ikigai. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156437 https://hdl.handle.net/10356/156437 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Lua, Emily Jia Ning A knowledge graph based survey system for Ikigai |
| title | A knowledge graph based survey system for Ikigai |
| title_full | A knowledge graph based survey system for Ikigai |
| title_fullStr | A knowledge graph based survey system for Ikigai |
| title_full_unstemmed | A knowledge graph based survey system for Ikigai |
| title_short | A knowledge graph based survey system for Ikigai |
| title_sort | knowledge graph based survey system for ikigai |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/156437 |
| work_keys_str_mv | AT luaemilyjianing aknowledgegraphbasedsurveysystemforikigai AT luaemilyjianing knowledgegraphbasedsurveysystemforikigai |