The Semantic Web - Enabling Semantic Web Services: The Web Service Modeling Ontology - page 26

Fig. 3.2. Example is-a hierarchy (or taxonomy)
can reason with them and draw the same conclusions as a human can. These
relationships, which are implicitly known to humans (e.g. a human knows that
every student is a person) are encoded in a formally explicitly way so that
they can be understood by a machine. In a sense, the machine does not gain
real “understanding”, but the understanding of humans is encoded in such
a way that a machine can process it and draw conclusions through logical
reasoning.
3.1.1 Types of Ontologies
There exist many different ontologies, built for many different types of appli-
cations; they vary in both the amount of detail they express and the generality
of their use.
WordNet [37], for example, aims to cover all of the English language by
providing a (natural-language) description for every English term and by spec-
ifying simple relationships, such as synonym (equivalent terms) and hyper-
/hyponym (more/less general terms) relationships. The scope of WordNet is
very broad (all of the English language) and the level of detail is relatively
low. There are only natural-language descriptions for the terms, which are
not machine-understandable, and there exist only very simple relationships
between the terms.
CYC [82] is another example of an ontology with a very broad scope, which
attempts to capture all commonsense knowledge (e.g. space and time), but
with a high level of detail. There are many very strict formal relationships be-
tween different terms. These formal relationships are machine-understandable.
We shall refer to the scope of an ontology as the “generality” and the level
of detail as the “expressiveness”. We provide a more detaild description of the
generality and expressiveness of ontologies below and use these as dimensions
to classify existing ontologies.