Concept Trees: Building Dynamic Concepts from Semi-structured Data Using Nature-Inspired Methods - Complex System Modelling and Control Through Intelligent Soft Computations - page 222

functionality is more important for the Internet or Cloud at the moment (Aslam
et al. 2007 ; Atkinson et al. 2007 , for example). The paper Carr et al. ( 2001 )
describes slightly earlier ideas about linked data and marking-up documents on the
Internet. It notes how the lines between search and link, or web and database have
become blurred and even just searching over metadata tags can be considered as a
sort of database operation.

3.1 Ontologies and Semantics

A tree structure, or directed graph, is often used to model text sequences, because it
allows for the reuse of sequence paths, extending from the same base. Ontologies
are essentially de

nitions of domains that describe the concepts in that domain and
how they relate to each other. A section from the topic Greer ( 2008 , Chap. 4)
describes that ontologies can be used to represent a domain of knowledge, allowing
a system to reason about the contents of that domain. The concepts are related
through semantics, for example,

. For traditional constructions,
relations can then be organised into hierarchical tree-like structures. The

' a car is a vehicle '

'
relation is particularly useful, where the previous example shows that a car is a
subclass of a vehicle. There are different definitions of what an ontology is
depending on what subject area you are dealing with. Gruber ( 1993 ) gives the
following de

'

subclass

nition for the area of ' AI and knowledge representation ' , which is

suitable for this work:

An ontology is an explicit speci cation of a conceptualisation. The term is borrowed from
philosophy, where an ontology is a systematic account of Existence. For knowledge-based
systems, what ' exists ' is exactly that which can be represented. When the knowledge of a
domain is represented in a declarative formalism, the set of objects that can be represented
is called the universe of discourse. This set of objects, and the describable relationships
among them, are reflected in the representational vocabulary with which a knowledge-
based program represents knowledge. Thus, we can describe the ontology of a program by
defining a set of representational terms. In such an ontology, definitions associate the names
of entities in the universe of discourse (e.g., classes, relations, functions, or other objects)
with human-readable text describing what the names are meant to denote, and formal
axioms that constrain the interpretation and well-formed use of these terms.

nition, but because a concept base is constructed slightly
differently, the related ontology construction will also be slightly different. The
additional knowledge that de

This is a desirable de

is not automatically
present, where the system has to determine the correct position, relation and
ordering for any concept, mostly from statistics. Because the knowledge is missing
however, the relation must also be more simplistic and would probably normally
just be

nes something like ' subclass '

' '

. It is also worth noting that the future vision of the Web would
probably require distributed ontologies. Again from Greer ( 2008 ), the future
Internet should maybe describe itself at a local level, with larger centralised rep-
resentations being created by specific applications, based on the domains of

related to