With the advent of reasoning problems in dynamic environments, there is an increasing
need for automated reasoning systems to automatically adapt to unexpected changes
in representations. In particular, the automation of the evolution of their ontologies
needs to be enhanced without substantially sacrificing expressivity in the underlying
representation. Revision of beliefs is not enough, as adding to or removing from beliefs
does not change the underlying formal language. General reasoning systems employed
in such environments should also address situations in which the language for representing
knowledge is not shared among the involved entities, e.g., the ontologies in
a multi-ontology environment or the agents in a multi-agent environment. Our techniques
involve diagnosis of faults in existing, possibly heterogeneous, ontologies and
then resolution of these faults by manipulating the signature and/or the axioms.
This thesis describes the design, development and evaluation of GALILEO (Guided
Analysis of Logical Inconsistencies Lead to Evolution of Ontologies), a system designed
to detect conflicts in highly expressive ontologies and resolve the detected conflicts
by performing appropriate repair operations. The integrated mechanism that
handles ontology evolution is able to distinguish between various types of conflicts,
each corresponding to a unique kind of ontological fault. We apply and develop our
techniques in the domain of Physics. This an excellent domain because many of its
seminal advances can be seen as examples of ontology evolution, i.e. changing the
way that physicists perceive the world, and case studies are well documented – unlike
many other domains. Our research covers analysing a wide ranging development set
of case studies and evaluating the performance of the system on a test set. Because
the formal representations of most of the case studies are non-trivial and the underlying
logic has a high degree of expressivity, we face some tricky technical challenges,
including dealing with the potentially large number of choices in diagnosis and repair.
In order to enhance the practicality and the manageability of the ontology evolution
process, GALILEO incorporates the functionality of generating physically meaningful
diagnoses and repairs and, as a result, narrowing the search space to a manageable size