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.