Many research efforts have dedicated to bridge the semantic
gap between data mining results and users. Marinica et al. [49],
[50] used ontology for the post pruning and filtering of the
association rule mining results. Mansingh et al. [48] used ontology
to assist the subjective analysis for the association rule
post-pruning task. The data mining results can be represented
by ontologies in the semantic rich format which help sharing
and reuse. For example, information extraction (IE) is the task
of automatically extracting structured information from text.
The data/text mining results are sets of structured information
and knowledge with regarding to the domain. To represent
the structured and machine-readable information, it is nature
to represent the information with ontology. Ontology Based
Information Extraction (OBIE) [77] has extensively used this
representation. With OBIE, the information extracted is not
only well structured but also represented by predicates in the
ontology which are easy for sharing and reuse.