@inproceedings{7feebfef0f9748508dd8b71318fc3f17,
title = "Handling uncertainty in ontology construction based on Bayesian approaches: A comparative study",
abstract = "Ontology is widely used to represent knowledge in many software applications. By default, ontology languages such as OWL and RDF is built on discrete logic, so that it can not handle uncertain information about a domain. Various approaches have been made to represent uncertainty in ontology, one of which with a Bayesian approach. Currently, there are four published approaches: BayesOWL or OntoBayes, Multi-Entity Bayesian Networks (MEBN), Probabil istic OWL (PR-OWL), and Dempster-Shafer Theory. This paper provides a comparative study on those approaches based on complexity, accuracy, ease of implementation, reasoning, and tools support. The study concluded that Baye-sOWL is the most recommended approach to handle uncertainty in ontology construction among others.",
keywords = "Bayesian Network, Ontology, Semantic Web, Uncertainty",
author = "Setiawan, {Foni Agus} and Wibowo, {Wahyu Catur} and Ginting, {Novita Br}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; 4th International Conference on Soft Computing, Intelligent Systems and Information Technology, ICSIIT 2015 ; Conference date: 11-03-2015 Through 14-03-2015",
year = "2015",
doi = "10.1007/978-3-662-46742-8_22",
language = "English",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "234--246",
editor = "Chi-Hung Chi and Rolly Intan and Palit, {Henry N.} and Santoso, {Leo Willyanto}",
booktitle = "Intelligence in the Era of Big Data - 4th International Conference on Soft Computing, Intelligent Systems and Information Technology, ICSIIT 2015, Proceedings",
address = "Germany",
}