Learning Categories from Linked Open Data

The growing presence of Resource Description Framework (RDF) as a data representation format on the web brings opportunity to develop new approaches to data analysis. One of important tasks is learning categories of data. Although RDF-based data is equipped with properties indicating its type and su...

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Bibliographic Details
Published inInformation Processing and Management of Uncertainty in Knowledge-Based Systems pp. 396 - 405
Main Authors Chen, Jesse Xi, Reformat, Marek Z.
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2014
SeriesCommunications in Computer and Information Science
Subjects
clustering
fuzziness
RDF triples
similarity
Online AccessGet full text
ISBN9783319088518
3319088513
ISSN1865-0929
1865-0937
DOI10.1007/978-3-319-08852-5_41

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Summary:The growing presence of Resource Description Framework (RDF) as a data representation format on the web brings opportunity to develop new approaches to data analysis. One of important tasks is learning categories of data. Although RDF-based data is equipped with properties indicating its type and subject, building categories based on similarity of entities contained in the data provides a number of benefits. It mimics an experience-based learning process, leads to construction of an extensional-based hierarchy of categories, and allows to determine degrees of membership of entities to the identified categories. Such a process is addressed in the paper.
ISBN:9783319088518
3319088513
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-319-08852-5_41