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238 Selected Studies on Software and Information Systems
– cross-domain translation [10] – for example from book recommender to movie recommender.
The translation exploits a KB of books and movies genres that facilitates
the translation through identifying the correlations between the contents of the user
models (e.g., liked/disliked genres, common to movies and books).
The authors did not address completely the issue of actual aggregation of partial user models.
We believe, that this step could be facilitated by exploiting OWL ontologies and its inherent
mapping features, which allows for definition of equivalency between classes and properties.
In [57] we can find a preliminary work on using ontological cultural user modeling to
overcome the cold-start problem, however authors are more focused on the ontology itself
rather than on the mapping problem.
Ontology Mapping Probably the most natural way of solving cold-start problem for
ontology-based systems is to exploit user model ontologies published on the web. Ontologies
were conceived to do so and sharing is one of their main benefits. However, in
order to use data from other ontology, the system needs to “understand” them, i.e. it has to
put it in relation with its own ontology – ontology mapping needs to be done manually or
(preferably) automatically.
It is a process whereby two ontologies are semantically related at conceptual level, and
the source ontology instances are transformed into the target ontology entities according to
those semantic relations. This results in three dimensions of ontology alignment:
– Discovery – manually, automatically or semi-automatically defining the relations between
ontologies
– Representation – a language to represent the relations between the ontologies
– Execution – changing instance of a source ontology to an instance of target ontology
Another point of view takes into consideration the types of mapping process:
– Mapping between an integrated ontology and local ontologies – ontology mapping
is used to map a concept found in one ontology into a view, or a query over other
ontologies (e.g. over the global ontology in the local-centric approach, or over the local
ontologies in the global-centric approach) [19].
– Mapping between local ontologies – the process transforms the source ontology entities
into the target ontology entities based on semantic relation. The source and target
are semantically related at a conceptual level. This is the mapping which is (or is to
be) used on the Semantic Web, because of its de-centralized nature.
– Ontology mapping in ontology merge and alignment – the process establishes correspondence
among source (local) ontologies to be merged or aligned, and determines
the set of overlapping concepts, synonyms, or unique concepts to those sources. This
mapping identifies similarities and conflicts between the various source (local) ontologies
to be merged or aligned [19].