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Concept Trees: Building Dynamic
Concepts from Semi-structured Data
Using Nature-Inspired Methods
Kieran Greer
Abstract This paper describes a method for creating structure from heterogeneous
sources, as part of an information database, or more speci
cally, a
'
concept base
'
.
Structures called
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 statis-
tical 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 opti-
misation or normalisation of the information will occur. This gives comparisons
with databases or knowledge-bases, but a database system would
'
concept trees
'
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.
Keywords Concept
Tree
Database
Self-organise
AI
Semi-structured
Semantic
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