Great question. To be honest, I used deep learning algorithms as a metaphor into Neo4j's property graph data model. Graph databases like Neo4j store data as a graph, which is a similar data structure to a neural network. I store weights in the relationships based on the frequency that a feature has been matched from the low-level representations near the bottom of the tree, to higher-level representations.

So there are two parts, there is building a natural language parsing model and then there is a Vector Space Model classifier that uses TF-IDF weights as vectors to calculate the cosine similarity between inputs.

I explain more about the high-level idea here: http://bit.ly/1lMjSm5

Let it be known that I've arrived at most of this stuff by means of intuition and graph data modeling in Neo4j. I'm a hobbyist when it comes to the machine learning stuff. My goal is to show how amazing a combined application/persistency solution, like a Neo4j extension, is for solving these kind of machine learning problems.

People smarter than me should take a look at it to solve similar problems.