Implement type-wise Attribute value normalisation

[jmsfltchr]

Presently it is very difficult to architect a concise way to normalise attribute values.

Current problems include:

• All Things in the graph are treated as if they could be an attribute. This means that:
  • All Things must support a field for long, double, string, date and boolean in case they are an Attribute. In the case that a Thing is an Attribute, then only one of these values will be set to non-default.
  • The vast majority of the attribute value fields are set to a default value. This obfuscates the meaning of zero. In some cases it means an actual value of zero, in others it is present because the Thing is not an Attribute . This is particularly difficult to handle in the case of dates where in unix time zero is Thursday, 1 January 1970.
• Attributes need to be normalised by Type, otherwise the distribution of values from one type will impact that of another
• Normalisation needs to be calibrated on the training set, and the parameters used to normalise data passed subsequently.
• Encoding of the input data takes place inside the TensorFlow computation graph, adding normalisation there may be non-trivial, and there aren't any OOTB components from TensorFlow like the preprocessing.StandardScaler() of scikit-learn

Should be made easier to accomplish by solving #51

[jmsfltchr]

As of the 0.2 release these points have been addressed by a very different architecture.

Attribute value embedding is now type-centric, this means

• Values for entities and relations are ignored entirely.
• Each attribute can now be embedded using its own neural network component. In this way attribute values only need to be consistently normalised on a by-type basis.
• The normalisation can therefore be performed prior to data being fed to the network