Abstract
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.
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Chen, J.X., Reformat, M.Z. (2014). Learning Categories from Linked Open Data. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_41
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DOI: https://doi.org/10.1007/978-3-319-08852-5_41
Publisher Name: Springer, Cham
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