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A Kohonen self-organising network will be adopted as the AI architecture. The self-organising map (invented by Teuvo Kohonen) uses a form of unsupervised learning where a set of artificial neurons learn to map points in an input space to coordinates in the output space. The input space can have different dimensions and topologies from the output space, and the SOM (self-organising map) will attempt to preserve these.
The idea behind such a system is to allow the intricacies of the AI implementation to be hidden form the user, providing an efficient way to catalogue data within the system.
Unsupervised learning is a method of machine-learning where a model is fit to observations. It is distinguished from supervised learning by the fact that there is no priori output. In unsupervised learning, a data set of input objects is gathered and input objects are typically treats as a set of random variables.
In this case, the input objects will be a collection of keywords to check the Euclidean space. A joint density model is then built for the data set.
Unsupervised learning can be used to produce conditional probabilities (i.e. supervised learning) for any of the variables generated while querying the root complex database table. Typically, this is classed under a feed-forward architecture.
The algorythm:
1.Initialise the database and root complex table and cast it as the SOM class.
2.Grab an input vectorand (i.e a keyword)
3.Traverse each node in the SOM
Use Euclidean distance formula to find similarity between the input vector theandmap'sandnode's weight vector . Track the node that produces the smallest distance (this node will be called the Best Matching Unit)
4.Update the nodes in the neighbourhood of BMU by pulling them closer to the input vector Wv(t + 1) = Wv(t) + ˜(t)±(t)(D(t) - Wv(t))
5.During the training process, a map is built. The neural network organises itself by pulling back data from the database. The network must be given a large number of inputs or keywords, so the ANN can retrieve several possible results from the database. Otherwise, all input vectors must be administered several times.
6.During the mapping process, a new input vector may quickly be given a location on the map and will automatically be classified or categorised. There will be one single winning neuron, whose weight vector lies closest to the input vector (this can be simply determined by calculating the Euclidean distance between input vector and weight vector). Within the proposed software, the root complex will be used as the Euclidean space where all the meta-data will be stored. The data will then be retrieved from the system in binary objects which will be cast back to the specific class type. This class will include encapsulated data. The closest match of relevancy and accuracy will be presented to the user as the answer to the question.
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