Handling uncertainty in ontology construction based on Bayesian approaches: A comparative study

Foni Agus Setiawan, Wahyu Catur Wibowo, Novita Br Ginting

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIntelligence in the Era of Big Data - 4th International Conference on Soft Computing, Intelligent Systems and Information Technology, ICSIIT 2015, Proceedings
EditorsChi-Hung Chi, Rolly Intan, Henry N. Palit, Leo Willyanto Santoso
PublisherSpringer Verlag
Pages234-246
Number of pages13
ISBN (Electronic)9783662467411
DOIs
Publication statusPublished - 1 Jan 2015
Event4th International Conference on Soft Computing, Intelligent Systems and Information Technology, ICSIIT 2015 - Bali, Indonesia
Duration: 11 Mar 201514 Mar 2015

Publication series

NameCommunications in Computer and Information Science
Volume516
ISSN (Print)1865-0929

Conference

Conference4th International Conference on Soft Computing, Intelligent Systems and Information Technology, ICSIIT 2015
Country/TerritoryIndonesia
CityBali
Period11/03/1514/03/15

Keywords

  • Bayesian Network
  • Ontology
  • Semantic Web
  • Uncertainty

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