TY - JOUR
T1 - A relationship selection task
AU - Waters, Andrew E.
AU - Chaudhri, Vinay K.
AU - Mallick, Debshila Basu
AU - Baraniuk, Richard G.
N1 - Funding Information:
★ Supported by the National Science Foundation (NSF). We thank Abhay Agarwal for deploying the task on AWS and making significant additions to the code documentation. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Publisher Copyright:
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - An intelligent textbook is a traditional textbook enhanced with a knowledge graph (KG) making it a source of enhanced learning and instruction. The nodes of a KG are key terms in a textbook and the edges are the relationships between the terms. Relationship selection is the process of selecting the most appropriate relationship type between two different terms to be incorporated into a KG. We demonstrate a tool that allows learners to select relationships between terms embedded in a textbook sentence. We created this tool as a key component of a scalable infrastructure for KG construction through crowdsourcing of relationships between automatically extracted terms from a textbook. This task has the potential to be flexibly adapted to different textbooks and content domains. It is also suitable for encouraging relational processing and, we believe that it has instructional value. Therefore, our future work is focused on the pedagogical evaluation of the relationship selection task with students reading from a textbook.
AB - An intelligent textbook is a traditional textbook enhanced with a knowledge graph (KG) making it a source of enhanced learning and instruction. The nodes of a KG are key terms in a textbook and the edges are the relationships between the terms. Relationship selection is the process of selecting the most appropriate relationship type between two different terms to be incorporated into a KG. We demonstrate a tool that allows learners to select relationships between terms embedded in a textbook sentence. We created this tool as a key component of a scalable infrastructure for KG construction through crowdsourcing of relationships between automatically extracted terms from a textbook. This task has the potential to be flexibly adapted to different textbooks and content domains. It is also suitable for encouraging relational processing and, we believe that it has instructional value. Therefore, our future work is focused on the pedagogical evaluation of the relationship selection task with students reading from a textbook.
KW - Concept Mapping
KW - Intelligent Textbooks
KW - Knowledge Graph
KW - Relationship Selection
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M3 - Conference article
AN - SCOPUS:85109732015
SN - 1613-0073
VL - 2895
SP - 88
EP - 92
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 3rd International Workshop on Intelligent Textbooks, iTextbooks 2021
Y2 - 15 June 2021
ER -