Abstract
This paper describes a theoretical approach on data mining, information classifying and a global overview of our OntoExtractor application, concerning the analysis of incoming data flow and generate metadata structures.
In order to help the user to classify a big and varied group of data, our proposal is to use fuzzy-based techniques to compare and classify the data.
Before comparing the elements, the incoming flow of information has to be converted into a common structured format like XML.
With those structured documents now we can compare and cluster the various data and generate a metadata structure about this data repository.
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References
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RDF W3C Recommendation http://www.w3.org/TR/rdf-primer/
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© 2005 Springer-Verlag Berlin Heidelberg
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Cui, Z., Damiani, E., Leida, M., Viviani, M. (2005). OntoExtractor: A Fuzzy-Based Approach in Clustering Semi-structured Data Sources and Metadata Generation. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_17
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DOI: https://doi.org/10.1007/11552413_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28894-7
Online ISBN: 978-3-540-31983-2
eBook Packages: Computer ScienceComputer Science (R0)