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5 CONCLUSION AND OUTLOOK
Especially in industry, Semantic Web technologies
are rather seldom used. Since corporations would
greatly benefit from available and future RDF-based
datasets, we suggested to bridge the technology gap
by a fully automatic conversion of RDF to RDBs to-
gether with CRUD REST APIs. Our experiments
suggest that in comparison to related work, our re-
engineered databases reflect their RDF counterparts
with less tables. Moreover, our generated REST
APIs are able to reproduce rather simple and com-
mon SPARQL queries. We identified as a remaining
challenge the generation of unnecessary data redun-
dancies because of multi-typed instances.
Future work should find an appropriate way to
model the database to reduce the high number of ta-
bles and data redundancies. In this regard, as already
pointed out, algorithms should provide a configurable
trade-off to decide whether properties become many-
to-many tables or simple columns. Thus, the right set-
ting can be dependent on a particular use case. Since
RDF datasets can change over time, we also suggest
that future procedures provide an update-mechanism
in order to avoid rebuilding the whole database (and
possibly removing already inserted data). Moreover,
to also process larger datasets (like DBpedia), we in-
tend to reduce our algorithm’s memory usage.
ACKNOWLEDGEMENTS
This work was funded by the BMBF project SensAI
(grant no. 01IW20007).
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