  This paper describes a method for creating structure from heterogeneous
sources, as part of an information database, or more specifically, a 'concept
base'. Structures called 'concept trees' can grow from the semi-structured
sources when consistent sequences of concepts are presented. They might be
considered to be dynamic databases, possibly a variation on the distributed
Agent-Based or Cellular Automata models, or even related to Markov models.
Semantic comparison of text is required, but the trees can be built more, from
automatic knowledge and statistical feedback. This reduced model might also be
attractive for security or privacy reasons, as not all of the potential data
gets saved. The construction process maintains the key requirement of
generality, allowing it to be used as part of a generic framework. The nature
of the method also means that some level of optimisation or normalisation of
the information will occur. This gives comparisons with databases or
knowledge-bases, but a database system would firstly model its environment or
datasets and then populate the database with instance values. The concept base
deals with a more uncertain environment and therefore cannot fully model it
beforehand. The model itself therefore evolves over time. Similar to databases,
it also needs a good indexing system, where the construction process provides
memory and indexing structures. These allow for more complex concepts to be
automatically created, stored and retrieved, possibly as part of a more
cognitive model. There are also some arguments, or more abstract ideas, for
merging physical-world laws into these automatic processes.
