  In recent years, there have been valuable efforts and contributions to make
the process of RDF knowledge graph creation traceable and transparent;
extending and applying declarative mapping languages is an example. One
challenging step is the traceability of procedures that aim to overcome
interoperability issues, a.k.a. data-level integration. In most pipelines, data
integration is performed by ad-hoc programs, preventing traceability and
reusability. However, formal frameworks provided by function-based declarative
mapping languages such as FunUL and RML+FnO empower expressiveness. Data-level
integration can be defined as functions and integrated as part of the mappings
performing schema-level integration. However, combining functions with the
mappings introduces a new source of complexity that can considerably impact the
required number of resources and execution time. We tackle the problem of
efficiently executing mappings with functions and formalize the transformation
of them into function-free mappings. These transformations are the basis of an
optimization process that aims to perform an eager evaluation of function-based
mapping rules. These techniques are implemented in a framework named Dragoman.
We demonstrate the correctness of the transformations while ensuring that the
function-free data integration processes are equivalent to the original one.
The effectiveness of Dragoman is empirically evaluated in 230 testbeds composed
of various types of functions integrated with mapping rules of different
complexity. The outcomes suggest that evaluating function-free mapping rules
reduces execution time in complex knowledge graph creation pipelines composed
of large data sources and multiple types of mapping rules. The savings can be
up to 75%, suggesting that eagerly executing functions in mapping rules enable
making these pipelines applicable and scalable in real-world settings.
