Using Machine Learning to Support Continuous Ontology Development
Loading...
Authors
Author (Corporation)
Publication date
2010
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Proceedings of 17th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2010
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Place of publication / Event location
Lisbon
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
This paper presents novel algorithms to support the continuous development of ontologies; i.e. the development of ontologies during their use in social semantic bookmarking, semantic wiki or other social semantic applications. Our goal is to assist users in placing a newly added concept in a concept hierarchy. The proposed algorithm is evaluated using a data set from Wikipedia and provides good quality recommendation. These results point to novel possibilities to apply machine learning technologies to support social semantic applications.
Keywords
Subject (DDC)
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
No
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
License
Citation
Ramezani, M., Witschel, H. F., Braun, S., & Zacharias, V. (2010). Using Machine Learning to Support Continuous Ontology Development. Proceedings of 17th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2010. https://doi.org/10.26041/fhnw-2804