Automatic fuzzy ontology generation for semantic Web

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...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 18; H. 6; S. 842 - 856
Hauptverfasser: Tho, Q.T., Hui, S.C., Fong, A.C.M., Tru Hoang Cao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.06.2006
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
" probabilistic
"fuzzy
Applied sciences
Approximation
Computer science; control theory; systems
concept learning
Data mining
Exact sciences and technology
Fuzzy logic
Fuzzy set theory
Hierarchies
Information systems. Data bases
Intelligent Web services and semantic Web
Knowledge representation
knowledge representation formalisms and methods
Memory organisation. Data processing
Ontologies
ontology design
OWL
Search engines
Semantic Web
Semantics
Software
Uncertainty
Web services
Concept learning
Semantic web
Ontology
Knowledge representation
Web service
Intelligent Web services and semantic Web
uncertainty
Conceptual analysis
knowledge representation formalisms and methods
fuzzy, probabilistic
ontology design
ISSN:1041-4347, 1558-2191
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Beschreibung
Zusammenfassung: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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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2006.87