By decomposing long descriptions into new resources that can be represented with shorter labels or lexical entities, one can facilitate their future translation to other languages.
Resources with very long descriptions are usually a symptom that the knowledge base structure (i.e. vocabulary or ontology) is not sufficiently developed. In general, it is good to foresee to associate short labels with resources. This way, a model where resources have long descriptions can be further decomposed into small pieces with shorter descriptions or labels.
Multilingual applications with more fine grained textual information can be better localized and adapted to other languages.
:juan :jobtitle
"Professor at the University of León"@en .
However, the description can be decomposed in two components:
the title (Professor) and the University
(University of León).
:juan :position :professor .
:juan :workPlace :unileón .
:professor rdfs:label "Professor"@en .
:uniLeón rdfs:label "University of León"@en .
In this way, a multilingual application can recognize the different components
and create a better user experience.
Decomposing the resources of a dataset to more fine grained atoms can make the dataset more user-friendly.
Applications can generate more readable information to the end user, especially when they are localized.
However, this pattern increases the complexity of the model. It is necessary to find a good balance between verbose models with fine-grained resources and lighter models with a fewer resources and longer descriptions.