  Data has exponentially grown in the last years, and knowledge graphs
constitute powerful formalisms to integrate a myriad of existing data sources.
Transformation functions -- specified with function-based mapping languages
like FunUL and RML+FnO -- can be applied to overcome interoperability issues
across heterogeneous data sources. However, the absence of engines to
efficiently execute these mapping languages hinders their global adoption. We
propose FunMap, an interpreter of function-based mapping languages; it relies
on a set of lossless rewriting rules to push down and materialize the execution
of functions in initial steps of knowledge graph creation. Although applicable
to any function-based mapping language that supports joins between mapping
rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy,
e.g., duplicates and unused attributes, and converts RML+FnO mappings into a
set of equivalent rules executable on RML-compliant engines. We evaluate FunMap
performance over real-world testbeds from the biomedical domain. The results
indicate that FunMap reduces the execution time of RML-compliant engines by up
to a factor of 18, furnishing, thus, a scalable solution for knowledge graph
creation.
