A Dynamic and Informative Intelligent Survey System Based on Knowledge Graph

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationSemantic Technology
Subtitle of host publication9th Joint International Conference, JIST 2019, Hangzhou, China, November 25–27, 2019, Proceedings
EditorsXin Wang, Francesca Alessandra Lisi, Guohui Xiao, Elena Botoeva
Place of PublicationCham
PublisherSpringer International Publishing
Pages226-241
Number of pages16
ISBN (Electronic)978-3-030-41407-8
ISBN (Print)978-3-030-41406-1
DOIs
Publication statusPublished - 14 Feb 2020
EventThe 9th Joint International Semantic Technology Conference - Hangzhou, China
Duration: 25 Nov 201927 Nov 2019
http://jist2019.openkg.cn/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
Volume12032
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe 9th Joint International Semantic Technology Conference
Abbreviated titleJIST 2019
CountryChina
CityHangzhou
Period25/11/1927/11/19
Internet address

Keywords

  • Commonsense knowledge graph
  • Visual relationship detection
  • Visual Genome

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