elektronická verzia publikácie - FIIT STU - Slovenská technická ...

240 Selected Studies on Software and Information Systems
– Semantic Bridging – similarities computed in the previously described phase are used
in the semantic bridging phase to establish correspondence between entities from the
source and target ontology, so that each instance represented according to the source
ontology is translated into the most similar instance described according to the target
ontology. It is performed in five steps:
1. Concept bridging step – selection of pairs of entities to be bridged, according to
the similarities found in previous phase. The same source entity may be part of
different bridges.
2. Property bridging step – specification of the matching properties for each concept
bridge.
3. Inferencing step – specification of bridges for concepts that do not have a specific
counterpart target concept.
4. Refinement step – improve quality of bridges between a source concept and sub
concepts of target concepts.
5. Transformation specification step – intends to associate a transformation procedure
to the translation, in a way that source instance may be translated into target
instances.
– Execution – actually transforms instances from the source ontology into target ontology
by evaluating the semantic bridges defined earlier. It could operate in two distinct
modes:
◦ offline static one time transformation) and
◦ online dynamic, continuous mapping between source and the target.
– Post-processing – takes the results of the execution module to check and improve the
quality of the transformation results.
MAFRA is not a fully automated solution and requires a domain expert to drive the creation
of semantic bridges. This requires an extensive graphical support, as deep understanding
of conceptualizations on both sides (source and target ontology) is required on human side.
Moreover, it is not clear whether a generic semantic bridges could lead to fully automatized
solution or it would be useful to define domain-dependent bridges, which contain domain
specific knowledge.
Information Extraction from Available Documents
Many approaches are employing user models to provide personalized information extraction
(IE) which would significantly impact the web search experience [25, 1]. There are also efforts
to use IE techniques to retrieve information about users, but usually for other purposes than
to populate user model with data suitable for personalization. We can find approaches that
are trying to automatically identify social networks around a particular user. We discussed
in the section 8.5.3 that resulting social network can be used to bootstrap user model.