Dr Jingyun Wang jingyun.wang@durham.ac.uk
Assistant Professor
In this paper, we present a method to extract the possible relationships between knowledge points by analyzing e-book log and mining quiz data and mining Wikipedia articles. This method will be implemented in an ontology-based visualization support system to support the instructor to construct course-centered ontologies semi-automatically.
Wang, J., Flanagan, B., & Ogata, H. (2017). Semi-automatic Construction of Ontology Based on Data Mining Technique. . https://doi.org/10.1109/iiai-aai.2017.202
Conference Name | 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) |
---|---|
Conference Location | Hamamatsu, Japan |
Start Date | Jul 9, 2017 |
End Date | Jul 13, 2017 |
Online Publication Date | Nov 16, 2017 |
Publication Date | 2017 |
Deposit Date | Jul 15, 2021 |
Pages | 511-515 |
ISBN | 978-1-5386-0622-3 |
DOI | https://doi.org/10.1109/iiai-aai.2017.202 |
Public URL | https://durham-repository.worktribe.com/output/1138721 |
Multiplayer Serious Games Supporting Programming Learning
(2023)
Conference Proceeding
BETTER: An Automatic feedBack systEm for supporTing emoTional spEech tRaining
(2023)
Book Chapter
Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry
(2022)
Conference Proceeding
A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics
(2022)
Conference Proceeding
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search