Chapter 7 Ontology engineering

1. Ontology is a model of a particular domain, built for a particular purpose. As a consequence, there is no correct ontology of a specific domain. An ontology is by necessity an abstraction of a particular domain, and there are always viable alternatives.What is included in this abstraction should be determinded by the use to which the ontology will be put, and by future extensions that are already anticipated.
2. >An important advantage of the use of OWL over RDF Schema is the possibility to detect inconsistencies in the ontology itself, or in the set of instances that were defined to populate the ontology.
3. The success of the Semantic Web greatly Depends on the proliferation of ontologies and relational metadata. This requires taht such metadata can be produced at high speed and low cost. To this end, the task of merging and aligning ontologies for establishing semantic interoperability may be supported by machine learning techniques.
4. One has to provide a means for maintaining and adopting the machine-processable data hat is the basic for the Semantic Web. Thus, we need mechanisms that support the dynamic nature of the Web. ...These problems resemble those that knowledge engineers have dealt with over the last two decades as they worked on knoweldge acuisition methodologies or workbenches for defining knowledge bases.

-------------------------------------

Comments by midi:

(I focused on domain ontology)

1. What the ontology of a particular domain looks like will be partly decided by the anticipated usage of the ontology.

Yes, it is just what we do currently.

If new application emerges which demands new concepts and relations (or different meaning on the original concepts & relations), new ontology will be created based on the old one.

Maybe the problem is :

a) when one starts to build its own ontology, he may find many related ontologies already exist, based on which he can build his own one. But how to compare and reuse them correctly and efficiently?

again, the ontology mapping and integration techniques are helpful. (just naively speaking.)

2. point 3 is just what I want to do now. It convinces me that the direction is right.

3. related background: knowlege management.