research-article

Automatic Fuzzy Ontology Generation for Semantic Web

Authors:

Quan Thanh Tho,

Siu Cheung Hui,

Tru Hoang CaoAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 18, Issue 6

Pages 842 - 856

https://doi.org/10.1109/TKDE.2006.87

Published: 01 June 2006 Publication History

Abstract

Ontology is an effective conceptualism commonly used for the Semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (Fuzzy Ontology Generation frAmework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: Fuzzy Formal Concept Analysis, Concept Hierarchy Generation, and Fuzzy Ontology Generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed.

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Index Terms

Automatic Fuzzy Ontology Generation for Semantic Web
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 18, Issue 6

June 2006

141 pages

ISSN:1041-4347

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Copyright © 2006.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 June 2006

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Cited By

Devarajan GNagarajan SDaniel AVignesh TKaluri R(2023)Consumer Product Recommendation System Using Adapted PSO With Federated Learning MethodIEEE Transactions on Consumer Electronics10.1109/TCE.2023.331937470:1(2708-2715)Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3319374
Vigné ASarthou GClodic A(2023)Primitive Action Recognition Based on Semantic FactsSocial Robotics10.1007/978-981-99-8715-3_29(350-362)Online publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1007/978-981-99-8715-3_29
Corno FDe Russis LMonge Roffarello A(2021)From Users’ Intentions to IF-THEN Rules in the Internet of ThingsACM Transactions on Information Systems10.1145/344726439:4(1-33)Online publication date: 16-Aug-2021
https://dl.acm.org/doi/10.1145/3447264
Sweidan SEl-Bakry HSabbeh S(2020)Construction of Liver Fibrosis Diagnosis Ontology From Fuzzy Extended ER ModelingInternational Journal of Decision Support System Technology10.4018/IJDSST.202001010312:1(46-69)Online publication date: 1-Jan-2020
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Zhang YWang MSaberi MChang E(2020)Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challengesScientometrics10.1007/s11192-020-03683-3125:3(2637-2666)Online publication date: 28-Aug-2020
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Qasim IAlam MKhan SKhan AMalik KSaleem MBukhari S(2020)A comprehensive review of type-2 fuzzy OntologyArtificial Intelligence Review10.1007/s10462-019-09693-953:2(1187-1206)Online publication date: 1-Feb-2020
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Fong AHong G(2019)Ontology-Powered Hybrid Extensional-Intensional LearningProceedings of the 2019 International Conference on Information Technology and Computer Communications10.1145/3355402.3355406(18-23)Online publication date: 16-Aug-2019
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Zhang Z(2018)Constructing L-fuzzy concept lattices without fuzzy Galois closure operationFuzzy Sets and Systems10.1016/j.fss.2017.05.002333:C(71-86)Online publication date: 15-Feb-2018
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Zhang FMa ZTong QCheng J(2018)Storing fuzzy description logic ontology knowledge bases in fuzzy relational databasesApplied Intelligence10.1007/s10489-017-0965-548:1(220-242)Online publication date: 1-Jan-2018
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