Abstract
In the paper we propose a dynamic and informative solutionto an intelligent survey system that is based on knowledge graph. To illustrate our proposal, we focus on ordering the questions of the questionnaire component by their acceptance, along with conditional triggers that further customise participants’ experience, making the system dynamic. Evaluation of the system shows that the dynamic component can be beneficial in terms of lowering the number of questions asked and improving the quality of data, allowing more informative data to be collected in a survey of equivalent length. Fine-grained analysis allows assessment of the interaction of specific variables, as well as of individual respondents rather than just global results. The paper explores and evaluates two algorithms for the presentation of survey questions, leading to additional insights about how to improve the system.
Original language | English |
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Title of host publication | Semantic Technology |
Subtitle of host publication | 9th Joint International Conference, JIST 2019, Hangzhou, China, November 25–27, 2019, Proceedings |
Editors | Xin Wang, Francesca Alessandra Lisi, Guohui Xiao, Elena Botoeva |
Place of Publication | Cham |
Publisher | Springer |
Pages | 226-241 |
Number of pages | 16 |
ISBN (Electronic) | 978-3-030-41407-8 |
ISBN (Print) | 978-3-030-41406-1 |
DOIs | |
Publication status | Published - 14 Feb 2020 |
Event | The 9th Joint International Semantic Technology Conference - Hangzhou, China Duration: 25 Nov 2019 → 27 Nov 2019 http://jist2019.openkg.cn/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer, Cham |
Volume | 12032 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | The 9th Joint International Semantic Technology Conference |
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Abbreviated title | JIST 2019 |
Country/Territory | China |
City | Hangzhou |
Period | 25/11/19 → 27/11/19 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Commonsense knowledge graph
- Visual relationship detection
- Visual Genome