Innovation and Ontologies

194 Classification – The OntoCube
The topics, represented as nodes, are depicted by multi-headed links which point to the different
occurrences of the object (Gandon, 2002a; Krcmar, 2005). Characteristic of a topic is a set of
names, occurrences and roles; roles are subgroups of occurrences. Topics can be grouped in
classes (topic types) (Gandon, 2002b). In a topic map, the associations can be defined freely (Garshol
& Moore, 2006; Ullrich, Meier & Angele).
figure 68 Toy topic map on researchers
As the toy topic map above shows, every research assistant has a specific academic level, a degree.
Furthermore, every researcher is assigned to a research topic which is written about by a
researcher. First name, last name and age are attributes which are assigned to the researcher.
5.4.4 Lightweight & Heavyweight Ontology
From time to time, the literature uses the term ontology for any knowledge representation or
structure. For better distinguishment, this contribution denominates simple ontologies as
lightweight ontologies (Fluit, Sabou & van Harmelen, 2004; Gómez-Pérez, Fernández-López &
Corcho, 2004). They contain classes and (taxonomic) relations between the classes (Gómez-
Pérez, Fernández-López & Corcho, 2002).
“In the short term, light-weight ontologies […] can be easier to apply in working systems. An
excellent example of this […][are] ontologies used to semantically structure an information
repository as a basis for search and retrieval. (Jasper & Uschold, 1999)
Heavyweight ontologies encompass, besides classes and relations, also rules and axioms and have thus
higher strength of expression than the lightweight versions (Gómez-Pérez, Fernández-López &
Corcho, 2002).
“[…] in the search area, there are obvious benefits of richer ontologies. For example, they
can support inference […] used to increase both precision and recall in a semantically well
structured information repository. (Jasper & Uschold, 1999)