SEMAGROW

SemaGrow EU Project

use cases covered:: IE, OA, LLD and SAOM

"SemaGrow envisages to develop the scalable, efficient, and robust data services needed to take full advantage of the data-intesive and inter-disciplinary Science of 2020 and to re-shape the way that data analysis techniques are applied to the heterogeneous data cloud"

To obtain real use cases anchored to available partner/users in the project, in WP2 we are lifting to RDF structured data available from partners, while in WP3 we are:

eliciting knowledge from unstructured content, by focusing on architectures and frameworks for doing it on a systematic way
implementing scalable solutions for ontology matching in real distributed scenarios

The first objective could benefit of results from specifications related to the IE scenario. The second objective fits the SAOM and OA use cases, with requirements related to LLD too.

The work will start from the observation that, beyond the many studies conducted on "in most of the cases" pre-elaborated examples, no OM system is really able to sustain the ontology matching task in a open, heterogeneous and distributed environment, cause there is nothing to really coordinate automatic, independent agents in a process of mediation involving problems such as heterogeneities in the natural (e.g. italian vs english, and to which extent? which coverage of the resource?) and formal (e.g. which formalism is used to describe the resource?) languages used to linguistically characterize an RDF dataset.