Knowledge Extraction and Modeling

The objective of the Workshop is to give an overview on the theme of "Knowledge Extraction & Modeling" with uptodate lectures showing the stateofart but also the most recent advances and future challenges. The Workshop is aimed at focusing on a theme that is not yet firmly established in literature or research.
Namely, the Workshop is meant to address the analysis of "complex systems" where
the difficulty of analysis is not only the availability of huge masses of data but
also the complex structure of relationships. It is somehow the problem of extracting
information from models, not just data.
Several statistical techniques for exploring a data structure are naturally
interpretable in the context of the following operational model:
where the sign "+" does not necessarily refer to an additive relation. This model reflects an exploratory context where, usually, a random part (the error) is combined with a structural one (the model). Once the data have been cleaned and either a class of models have been identified or a specific model has been estimated, the information on models (e.g. parameter estimates, fitness values, nature of the model, etc.) actually becomes the metadata to use for the extraction of further knowledge.
The challenge consists in considering the interaction between Knowledge Extraction and Modeling by investigating two possible directions: