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The RDF scene is much more mature nowadays. You could simply get an RDF dataset and start analyzing using a large variety of tools. No need to configure/fix/tune anything. It would be as simple as doing the tables JOIN of your example.

I guess if you're more comfortable with that, then perhaps yes, a CSV dataset would be better for you and it would make no difference for your use case. But for me, I could say the same: that with CSV I'd have to learn how to import it into an SQL database and figure out exactly what to JOIN because it's not a graph but a relation db made of tables.

So it really depends on what you're used to. But RDF adds more to the table really. Things like URIs for identifying things which could be directly deferenceable (if HTTP URIs). So you know what your columns actually mean and they're not simply a string of letters. And you'd know what it's in your cells and exactly what type of data it's in the cells.

These are important features that make data integration a lot simpler imho. But if you're used to CSVs and don't care to make your workflow more efficient with the features RDF offers, then I guess you're better off?

Also please check out this interesting answer by Jarven: http://answers.semanticweb.com/questions/19183/advantages-of...




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