  In recent years, following FAIR and open data principles, the number of
available big data including biomedical data has been increased exponentially.
In order to extract knowledge, these data should be curated, integrated, and
semantically described. Accordingly, several semantic integration techniques
have been developed; albeit effective, they may suffer from scalability in
terms of different properties of big data. Even scaled-up approaches may be
highly costly because tasks of semantification, curation and integration are
performed independently. In order to overcome these issues, we devise ConMap, a
semantic integration approach which exploits knowledge encoded in ontology in
order to describe mapping rules to perform these tasks at the same time.
Experimental results performed on different data sets suggest that ConMap can
significantly reduce the time required for knowledge graph creation by up to
70\% of the time that is consumed following a traditional approach.
