Adding Context to Concept Trees
  A Concept Tree is a structure for storing knowledge where the trees are
stored in a database called a Concept Base. It sits between the highly
distributed neural architectures and the distributed information systems, with
the intention of bringing brain-like and computer systems closer together.
Concept Trees can grow from the semi-structured sources when consistent
sequences of concepts are presented. Each tree ideally represents a single
cohesive concept and the trees can link with each other for navigation and
semantic purposes. The trees are therefore also a type of semantic network and
would benefit from having a consistent level of context for each node. A
consistent build process is managed through a 'counting rule' and some other
rules that can normalise the database structure. This restricted structure can
then be complimented and enriched by the more dynamic context. It is also
suggested to use the linking structure of the licas system [15] as a basis for
the context links, where the mathematical model is extended further to define
this. A number of tests have demonstrated the soundness of the architecture.
Building the trees from text documents shows that the tree structure could be
inherent in natural language. Then, two types of query language are described.
Both of these can perform consistent query processes to return knowledge to the
user and even enhance the query with new knowledge. This is supported even
further with direct comparisons to a cognitive model, also being developed by
the author.
