Reasoning by Analogy in the Generation of Domain Acceptable Ontology Renements

Refinements generated for a knowledge base often involve
the learning of new knowledge to be added to or replace existing parts
of a knowledge base. However, the justifiability of the refinement in the
context of the domain (domain acceptability) is often overlooked. The
work reported in this paper describes an approach to the generation
of domain acceptable refinements for incomplete and incorrect ontology
individuals through reasoning by analogy using existing domain knowledge.
To illustrate this approach, individuals for refinement are identified
during the application of a knowledge-based system, EIRA; when EIRA
fails in its task, areas of its domain ontology are identified as requiring
refinement. Refinements are subsequently generated by identifying and
reasoning with similar individuals from the domain ontology. To evaluate
this approach EIRA has been applied to the Intensive Care Unit (ICU)
domain. An evaluation (by a domain expert) of the refinements generated
by EIRA has indicated that this approach successfully produces
domain acceptable refinements.