I discovered and patented how to structure any data: Language has its own Internal parsing, indexing and statistics. For instance, there are two sentences:

a) ‘Sam!’
b) ‘A loud ringing of one of the bells was followed by the appearance of a
smart chambermaid in the upper sleeping gallery, who, after tapping at
one of the doors, and receiving a request from within, called over the
balustrades -‘Sam!’.’

Evidently, that the ‘Sam’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrases, which contain ‘Sam’, weights: the first has 1, the second – 0.08; the greater weight signifies stronger emotional ‘acuteness’.
First you need to parse obtaining phrases from clauses, restoring omitted words, for sentences and paragraphs.
Next, you calculate Internal statistics, weights; where the weight refers to the frequency that a phrase occurs in relation to other phrases.
After that data is indexed by common dictionary, like Webster, and annotated by subtexts.
This is a small sample of the structured data:
speaking – done – once : 333112
speaking – was – both : 333109
place – is – in : 250000
To see the validity of technology – pick up any sentence.

Do you have a pencil?

All other technologies depend on spying, on quires, on SQL, all of them on External statistics. See IBM, Oracle, Microsoft, Google and Yahoo? Apache Hadoop and NoSQL? My technology is the only one that obtains Internal statistics directly from texts themselves.
Being structured information will search for users based on their profiles of structured data. Each and every user can get only specifically tailored for him information: there is no any privacy issue, nobody ever will know what the user got and read. (No spam!)
The technology came from Analytic Philosophy, Internal Relations Theory.