  Semantic web technologies have significantly contributed with effective
solutions for the problems of data integration and knowledge graph creation.
However, with the rapid growth of big data in diverse domains, different
interoperability issues still demand to be addressed, being scalability one of
the main challenges. In this paper, we address the problem of knowledge graph
creation at scale and provide MapSDI, a mapping rule-based framework for
optimizing semantic data integration into knowledge graphs. MapSDI allows for
the semantic enrichment of large-sized, heterogeneous, and potentially
low-quality data efficiently. The input of MapSDI is a set of data sources and
mapping rules being generated by a mapping language such as RML. First, MapSDI
pre-processes the sources based on semantic information extracted from mapping
rules, by performing basic database operators; it projects out required
attributes, eliminates duplicates, and selects relevant entries. All these
operators are defined based on the knowledge encoded by the mapping rules which
will be then used by the semantification engine (or RDFizer) to produce a
knowledge graph. We have empirically studied the impact of MapSDI on existing
RDFizers, and observed that knowledge graph creation time can be reduced on
average in one order of magnitude. It is also shown, theoretically, that the
sources and rules transformations provided by MapSDI are data-lossless.
