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[IHS19] Enhancing the Conciseness of Linked Data by Discovering Synonym Predicates

Conférence Internationale avec comité de lecture : The 12th International Conference on Knowledge Science, Engineering and Management (KSEM 2019), August 2019, pp.12, Athens, Greece,

Mots clés: Linked data quality, OWL2, conciseness, ontology alignment

Résumé: The widespread use of semantic web technologies such as RDF, SPARQL and OWL enables individuals to build their databases on the web, write vocabularies and define rules to arrange and explain the relationships between data according to the Linked Data principles. As a consequence, a large amount of structured and interlinked data are being generated daily. A close examination of the quality of these data could be very critical, especially if important research and professional decisions depend on it. Nowadays, several linked data quality metrics have been proposed which cover numerous dimensions of linked data quality such as completeness, consistency, conciseness and interlinking. However, most of these metrics need to be refined to take advantage of the powerful and the expressivity of semantic languages like OWL2. In this paper, we propose an approach to enhance the conciseness of linked datasets by discovering synonym predicates. This approach is based, in addition to statistical analysis, on a deep semantic analysis of data and on learning algorithms. A set of experiments are conducted on a real-world dataset to evaluate the approach.

BibTeX

@inproceedings {
IHS19,
title="{Enhancing the Conciseness of Linked Data by Discovering Synonym Predicates}",
author=" S. Issa and F. Hamdi and S. Si-Said Cherfi ",
booktitle="{The 12th International Conference on Knowledge Science, Engineering and Management (KSEM 2019)}",
year=2019,
month="August",
pages="12",
address="Athens, Greece",
}