  The heterogeneity of data poses a great challenge when data from different
sources is to be merged for one application. Solutions for this are offered,
for example, by ontology-based data management (OBDM). A challenge of OBDM is
the automatic creation of semantic models from datasets. This process is
typically performed either data- or label-driven and always involves manual
human intervention. We identified textual descriptions of data, a form of
metadata, quickly to be produced and consumed by humans, as third possible
basis for automatic semantic modelling. In this paper, we present, how we plan
to use textual descriptions to enhance semantic data management. We will use
state of the art NLP technologies to identify concepts within textual
descriptions and build semantic models from this in combination with an
evolving ontology. We will use automatically identified models in combination
with the human data provider to automatically extend the ontology so that it
learns new verified concepts over time. Finally, we will use the created
ontology and automatically identified semantic models to either rate
descriptions for new data sources or even to automatically generate descriptive
texts that are easier to understand by the human user than formal models. We
present the procedure which we plan for the ongoing research, as well as
expected outcomes.
