Abstract
Big Data are collections of large data sets of both unstructured and structured data characterized by volume, variety and velocity. These characteristics accentuate the heterogeneity and complexity of data, which exceeds the capacity of traditional systems to cope with it. Due to the aforementioned characteristics, it is crucial to have a unified conceptual view of the data, as well as, an efficient representation of knowledge for big data management. Since the ontology has the maturity we need to understand and provide the meaning to process big data. However, Construction of ontology by hand is an incredibly challenging and error-prone process. Learning Ontology from existing resource gives a reasonable alternative. Therefore, this paper proposes an approach to learn OWL ontology from data in Couchbase database by the application of six mapping rules, we use Ontop reasoner to evaluate the consistency of extracted ontology.
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Mhammedi, S., El Massari, H., Gherabi, N. (2021). Cb2Onto: OWL Ontology Learning Approach from Couchbase. In: Gherabi, N., Kacprzyk, J. (eds) Intelligent Systems in Big Data, Semantic Web and Machine Learning. Advances in Intelligent Systems and Computing, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-72588-4_7
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