No Cover Image

Journal article 206 views 37 downloads

A Knowledge Graph Based Approach to Social Science Surveys

Jeff Z. Pan, Elspeth Edelstein, Patrik Bansky, Adam Wyner Orcid Logo

Data Intelligence, Volume: 3, Issue: 4, Pages: 477 - 506

Swansea University Author: Adam Wyner Orcid Logo

  • 58564.pdf

    PDF | Version of Record

    © 2021. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

    Download (529.82KB)

Check full text

DOI (Published version): 10.1162/dint_a_00107

Abstract

Recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for scientists. However, efforts to understand the quality of candidate survey questions provided by these methods have been limited. Indeed, existing methods do not consid...

Full description

Published in: Data Intelligence
ISSN: 2641-435X
Published: MIT Press - Journals 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58564
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2021-11-08T09:30:16Z
last_indexed 2023-01-11T14:39:14Z
id cronfa58564
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-09-28T17:23:27.5844150</datestamp><bib-version>v2</bib-version><id>58564</id><entry>2021-11-08</entry><title>A Knowledge Graph Based Approach to Social Science Surveys</title><swanseaauthors><author><sid>51fa34a3136b8e81fc273fce73e88099</sid><ORCID>0000-0002-2958-3428</ORCID><firstname>Adam</firstname><surname>Wyner</surname><name>Adam Wyner</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-11-08</date><deptcode>SCS</deptcode><abstract>Recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for scientists. However, efforts to understand the quality of candidate survey questions provided by these methods have been limited. Indeed, existing methods do not consider the type of on-the-fly content planning that is possible for face-to-face surveys and hence do not guarantee that selection of subsequent questions is based on response to previous questions in a survey. To address this limitation, we propose a dynamic and informative solution for an intelligent survey system that is based on knowledge graphs. To illustrate our proposal, we look into social science surveys, focusing on ordering the questions of a questionnaire component by their level of acceptance, along with conditional triggers that further customise participants' experience. Our main findings are: (i) evaluation of the proposed approach shows that the dynamic component can be beneficial in terms of lowering the number of questions asked per variable, thus allowing more informative data to be collected in a survey of equivalent length; and (ii) a primary advantage of the proposed approach is that it enables grouping of participants according to their responses, so that participants are not only served appropriate follow-up questions, but their responses to these questions may be analysed in the context of some initial categorisation. We believe that the proposed approach can easily be applied to other social science surveys based on grouping definitions in their contexts. The knowledge-graph-based intelligent survey approach proposed in our work allows online questionnaires to approach face-to-face interaction in their level of informativity and responsiveness, as well as duplicating certain advantages of interview-based data collection.</abstract><type>Journal Article</type><journal>Data Intelligence</journal><volume>3</volume><journalNumber>4</journalNumber><paginationStart>477</paginationStart><paginationEnd>506</paginationEnd><publisher>MIT Press - Journals</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2641-435X</issnElectronic><keywords>Intelligent survey system, Dynamic and informative system, Knowledge graph, Linguistic grammaticality judgements</keywords><publishedDay>25</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-10-25</publishedDate><doi>10.1162/dint_a_00107</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2022-09-28T17:23:27.5844150</lastEdited><Created>2021-11-08T09:22:50.0831857</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Jeff Z.</firstname><surname>Pan</surname><order>1</order></author><author><firstname>Elspeth</firstname><surname>Edelstein</surname><order>2</order></author><author><firstname>Patrik</firstname><surname>Bansky</surname><order>3</order></author><author><firstname>Adam</firstname><surname>Wyner</surname><orcid>0000-0002-2958-3428</orcid><order>4</order></author></authors><documents><document><filename>58564__21434__03ab3bf265ac4537a6196e9df7c68306.pdf</filename><originalFilename>58564.pdf</originalFilename><uploaded>2021-11-08T09:27:52.0732190</uploaded><type>Output</type><contentLength>542537</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2021. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/legalcode</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-09-28T17:23:27.5844150 v2 58564 2021-11-08 A Knowledge Graph Based Approach to Social Science Surveys 51fa34a3136b8e81fc273fce73e88099 0000-0002-2958-3428 Adam Wyner Adam Wyner true false 2021-11-08 SCS Recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for scientists. However, efforts to understand the quality of candidate survey questions provided by these methods have been limited. Indeed, existing methods do not consider the type of on-the-fly content planning that is possible for face-to-face surveys and hence do not guarantee that selection of subsequent questions is based on response to previous questions in a survey. To address this limitation, we propose a dynamic and informative solution for an intelligent survey system that is based on knowledge graphs. To illustrate our proposal, we look into social science surveys, focusing on ordering the questions of a questionnaire component by their level of acceptance, along with conditional triggers that further customise participants' experience. Our main findings are: (i) evaluation of the proposed approach shows that the dynamic component can be beneficial in terms of lowering the number of questions asked per variable, thus allowing more informative data to be collected in a survey of equivalent length; and (ii) a primary advantage of the proposed approach is that it enables grouping of participants according to their responses, so that participants are not only served appropriate follow-up questions, but their responses to these questions may be analysed in the context of some initial categorisation. We believe that the proposed approach can easily be applied to other social science surveys based on grouping definitions in their contexts. The knowledge-graph-based intelligent survey approach proposed in our work allows online questionnaires to approach face-to-face interaction in their level of informativity and responsiveness, as well as duplicating certain advantages of interview-based data collection. Journal Article Data Intelligence 3 4 477 506 MIT Press - Journals 2641-435X Intelligent survey system, Dynamic and informative system, Knowledge graph, Linguistic grammaticality judgements 25 10 2021 2021-10-25 10.1162/dint_a_00107 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2022-09-28T17:23:27.5844150 2021-11-08T09:22:50.0831857 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jeff Z. Pan 1 Elspeth Edelstein 2 Patrik Bansky 3 Adam Wyner 0000-0002-2958-3428 4 58564__21434__03ab3bf265ac4537a6196e9df7c68306.pdf 58564.pdf 2021-11-08T09:27:52.0732190 Output 542537 application/pdf Version of Record true © 2021. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. true eng https://creativecommons.org/licenses/by/4.0/legalcode
title A Knowledge Graph Based Approach to Social Science Surveys
spellingShingle A Knowledge Graph Based Approach to Social Science Surveys
Adam Wyner
title_short A Knowledge Graph Based Approach to Social Science Surveys
title_full A Knowledge Graph Based Approach to Social Science Surveys
title_fullStr A Knowledge Graph Based Approach to Social Science Surveys
title_full_unstemmed A Knowledge Graph Based Approach to Social Science Surveys
title_sort A Knowledge Graph Based Approach to Social Science Surveys
author_id_str_mv 51fa34a3136b8e81fc273fce73e88099
author_id_fullname_str_mv 51fa34a3136b8e81fc273fce73e88099_***_Adam Wyner
author Adam Wyner
author2 Jeff Z. Pan
Elspeth Edelstein
Patrik Bansky
Adam Wyner
format Journal article
container_title Data Intelligence
container_volume 3
container_issue 4
container_start_page 477
publishDate 2021
institution Swansea University
issn 2641-435X
doi_str_mv 10.1162/dint_a_00107
publisher MIT Press - Journals
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Recent success of knowledge graphs has spurred interest in applying them in open science, such as on intelligent survey systems for scientists. However, efforts to understand the quality of candidate survey questions provided by these methods have been limited. Indeed, existing methods do not consider the type of on-the-fly content planning that is possible for face-to-face surveys and hence do not guarantee that selection of subsequent questions is based on response to previous questions in a survey. To address this limitation, we propose a dynamic and informative solution for an intelligent survey system that is based on knowledge graphs. To illustrate our proposal, we look into social science surveys, focusing on ordering the questions of a questionnaire component by their level of acceptance, along with conditional triggers that further customise participants' experience. Our main findings are: (i) evaluation of the proposed approach shows that the dynamic component can be beneficial in terms of lowering the number of questions asked per variable, thus allowing more informative data to be collected in a survey of equivalent length; and (ii) a primary advantage of the proposed approach is that it enables grouping of participants according to their responses, so that participants are not only served appropriate follow-up questions, but their responses to these questions may be analysed in the context of some initial categorisation. We believe that the proposed approach can easily be applied to other social science surveys based on grouping definitions in their contexts. The knowledge-graph-based intelligent survey approach proposed in our work allows online questionnaires to approach face-to-face interaction in their level of informativity and responsiveness, as well as duplicating certain advantages of interview-based data collection.
published_date 2021-10-25T04:07:40Z
_version_ 1756962551573250048
score 10.92735