A Dynamic and Informative Intelligent Survey System based on Knowledge Graph

Patrik Bansky; Elspeth Claire Edelstein; Jeff Z Pan; Adam Wyner

doi:10.1007/978-3-030-41407-8_15

A Dynamic and Informative Intelligent Survey System based on Knowledge Graph

Patrik Bansky, Elspeth Claire Edelstein, Jeff Z Pan^*, Adam Wyner

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

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Abstract

In the paper we propose a dynamic and informative solution to 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
Title of host publication	Joint International Semantic Technology Conference
Publisher	Springer
Pages	226-241
Number of pages	16
ISBN (Electronic)	978-3-030-41407-8
ISBN (Print)	978-3-030-41406-1
DOIs	https://doi.org/10.1007/978-3-030-41407-8_15
Publication status	E-pub ahead of print - 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
Volume	12032

Conference

Conference	The 9th Joint International Semantic Technology Conference
Abbreviated title	JIST2019
Country/Territory	China
City	Hangzhou
Period	25/11/19 → 27/11/19
Internet address	http://jist2019.openkg.cn/

Keywords

Intelligent survey system
dynamic and informative system
Linguistic grammaticality judgements

Access to Document

10.1007/978-3-030-41407-8_15

Banksy_etal_JIST2019-KGISS_AAM
This is a post-peer-review, pre-copyedit version of an article published in [insert journal title]. The final authenticated version is available online at: http://dx.doi.org/[insert DOI]
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Cite this

Bansky, P, Edelstein, EC, Pan, JZ & Wyner, A 2020, A Dynamic and Informative Intelligent Survey System based on Knowledge Graph. in Joint International Semantic Technology Conference. Lecture Notes in Computer Science, vol. 12032, Springer , pp. 226-241, The 9th Joint International Semantic Technology Conference, Hangzhou, China, 25/11/19. https://doi.org/10.1007/978-3-030-41407-8_15

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title = "A Dynamic and Informative Intelligent Survey System based on Knowledge Graph",

abstract = "In the paper we propose a dynamic and informative solution to 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.",

keywords = "Intelligent survey system, dynamic and informative system, Linguistic grammaticality judgements",

author = "Patrik Bansky and Edelstein, {Elspeth Claire} and Pan, {Jeff Z} and Adam Wyner",

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doi = "10.1007/978-3-030-41407-8_15",

language = "English",

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N2 - In the paper we propose a dynamic and informative solution to 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.

AB - In the paper we propose a dynamic and informative solution to 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.

KW - Intelligent survey system

KW - dynamic and informative system

KW - Linguistic grammaticality judgements

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A Dynamic and Informative Intelligent Survey System based on Knowledge Graph

Abstract

Publication series

Conference

Keywords

Access to Document

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Cite this