Συλλογή, ολοκλήρωση και ανάλυση δεδομένων για Covid-19 με τεχνολογίες σημασιολογικού ιστού
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Keywords
COVID-19 ; Σημασιολογικός ΙστόςAbstract
The present dissertation aims to combine heterogeneous data to find correlations between variables and to draw conclusions about the evolution of the Covid-19 pandemic. Initially, data were collected from officially sources that directly refer to pandemic metrics, data on air pollution and data on population mobility. This data were organized and structured according to the principles of semantic web, while the corresponding ontology was created using the OWL language and the Protégé tool. Then, with the creation of appropriate lexical and grammatical rules and the use of RDF-Gen, the final files were produced that include the desired RDF triplets. Therefore, the extraction of the desired information was carried out by constructing the appropriate SPARQL queries, which were based on the ontology’s structure. In addition, secondary data was collected using SPARQL and corresponding queries stemming from their semantic web sources which are related to the number of hospitals, sports facilities, and population density for a set of specific pilot country capitals. According to the results, it seems that exist interesting correlations between the data that need further investigation.