Fuzzy-Semantic Information Management System for Dispersed Information

The internet is the most popular way to retrieve information today. However, many users are overwhelmed by the vast amount of data on the internet, which limits its potential significantly. To maximize the potential, extracting semantics from the raw data is one of the key technologies. The challeng...

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Bibliographic Details
Published inThe Journal of computer information systems Vol. 52; no. 1; pp. 96 - 105
Main Authors Hong, Seong-Yong, Kim, Jun-Woo, Hwang, Yong-Hyun
Format Journal Article
LanguageEnglish
Published Stillwater Taylor & Francis 01.09.2011
Taylor & Francis Ltd
Subjects
Alliances
Decision making
Electronic commerce
Extensible Markup Language
Fuzzy Inference
Fuzzy logic
Fuzzy sets
Information management
Internet service providers
Metadata
Online shopping
Prices
Resource Description Framework-RDF
Semantic Information
Semantic web
Semantics
Shopping centers
Studies
Web Ontology Language-OWL
XML
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Summary:The internet is the most popular way to retrieve information today. However, many users are overwhelmed by the vast amount of data on the internet, which limits its potential significantly. To maximize the potential, extracting semantics from the raw data is one of the key technologies. The challenge on the extraction is that the data on the internet is often dispersed where a single property of a subject might have a list of values. To address the challenge, this paper proposes a novel framework that can extract useful semantics out of a semantic context in the dispersed data. Since the semantic information is vague in its nature, our framework deploys the fuzzy inference method to effectively extract the useful semantic. Experimental results show that the proposed framework is effective in realizing semantic web with abundant semantic information, which allows web users to entertain full potential of the internet.
ISSN:0887-4417
2380-2057
DOI:10.1080/08874417.2011.11645526