Ontology Learning Process Using Fuzzy Formal Concept Analysis

J.JELSTEEN; D.EVANGELIN; J.ALICE PUSHPARANI; J.NELSON SAMUEL JEBASTIN

doi:https://doi.org/10.14445/22315381/IJETT-V4I2P213

Research Article | Open Access | Download PDF

Volume 4 | Issue 2 | Year 2013 | Article Id. IJETT-V4I2P213 | DOI : https://doi.org/10.14445/22315381/IJETT-V4I2P213

Ontology Learning Process Using Fuzzy Formal Concept Analysis

J.JELSTEEN , D.EVANGELIN , J.ALICE PUSHPARANI , J.NELSON SAMUEL JEBASTIN

Citation :

J.JELSTEEN , D.EVANGELIN , J.ALICE PUSHPARANI , J.NELSON SAMUEL JEBASTIN, "Ontology Learning Process Using Fuzzy Formal Concept Analysis," International Journal of Engineering Trends and Technology (IJETT), vol. 4, no. 2, pp. 148-152, 2013. Crossref, https://doi.org/10.14445/22315381/IJETT-V4I2P213

Abstract

Currently reliable and appropriate information is difficult to find on the Internet. Bayesian networks were used earlier for probabilistic reasoning of unknown values and for determining knowledge representation. Various probabilistic approaches were used to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain manually 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, a nd Fuzzy Ontology Generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. This project describes some evaluation of information retrieval system designed to support fuzzy ontology based search refinement. The objective is to implement generation and learning of knowledge representation using fuzzy logic and ontology for reasoning. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Finally automatic fuzzy ontology g eneration is proposed for knowledge domains like semantic web .

Keywords

Intelligent Web services and semantic Web, ontology design, uncertainty, “fuzzy,” knowledge representation formalisms and

methods, concept learning.

References

1. N. Guarino and P. Giaretta, Ontologies and Knowledge Bases: Towards a Terminological Clarification. Toward Very Large Knowledge Bases: Knowledge Building an d Knowledge Sharing. Amsterdam: IOS Press, 1995.
2. T. Berners - Lee, J. Hendler, and O. Lassila, “The Semantic Web,” Scientific Am.,htt tp://www.sciam.com/2001/0501issue/0501bern ers - lee.html, 2001.
3. L.A. Zadeh, “Fuzzy Logic and Approximate Reasoning,” Synthese , vol . 30, pp. 407 - 428, 1975.
4. M.Z. Islam and L. Brankovic, “A Framework for Privacy Preserving Data Mining,” Proc. Australasian Workshop Data Mining and Web Intelligence (DMWI ’04), pp. 163 - 168, 2004.
5. B. Bachimont, A. Isaac, and R. Troncy, “Semantic Commitme nt for Designing Ontologies: A Proposal,” Proc. Int’l Conf. Knowledge Eng. and Knowledge Management, pp. 114 - 121, 2002. F uzzy Systems, pp. 1291 - 1294, 2001.
6. D. Fisher, “Knowledge Acquisition via Incremental Conceptual Clustering,” Machine Learning, vol. 2, p p. 139 - 172, 1987.
7. W3C, “Web Ontology Language Overview, ”http://www.w3.org/ TR/owl - features/, 2006.