Artículo
Discovering and Analysing Ontological Models from Big RDF Data
Autor/es | Rivero, Carlos R.
Hernández Salmerón, Inmaculada Concepción Ruiz Cortés, David Corchuelo Gil, Rafael |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2015 |
Fecha de depósito | 2021-02-17 |
Publicado en |
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Resumen | The Web of Data, which comprises web sources that provide
their data in RDF, is gaining popularity day after day. Ontological
models over RDF data are shared and developed with the consensus of
one or more communities. ... The Web of Data, which comprises web sources that provide their data in RDF, is gaining popularity day after day. Ontological models over RDF data are shared and developed with the consensus of one or more communities. In this context, there usually exist more than one ontological model to understand RDF data; therefore, there might be a gap between the models and the data, which is not negligible in practice. In this article, we present a technique to automatically discover ontological models from raw RDF data. It relies on a set of SPARQL 1.1 structural queries that are generic and independent from the RDF data. The output of our technique is a model that is derived from these data and includes the types and properties, subtypes, domains and ranges of properties and subproperties. Our experiments with millions of triples prove that our technique is suitable to deal with Big RDF Data. As far as we know, this is the first technique to discover such ontological models in the context of RDF data and the Web of Data. |
Agencias financiadoras | Ministerio de Educación y Ciencia (MEC). España Junta de Andalucía Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | TIN2007-64119
P07-TIC-2602 P08-TIC-4100 TIN2010-21744 TIN2010-09809-E TIN2010-10811-E TIN2010-09988-E |
Cita | Rivero, C.R., Hernández Salmerón, I.C., Ruiz Cortés, D. y Corchuelo Gil, R. (2015). Discovering and Analysing Ontological Models from Big RDF Data. Journal of Database Management (JDM), 26 (2) |
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