Grammar Induction and Adaptive Information Disclosure

Abstract

The adaptive information disclosure (AID) project is part of a larger effort that aims at the creation of a so-called Virtual Lab environment for e-science (VL-E). In the context of AID we are building in the coming 4 years a suite of dynamic model driven information and knowledge extraction tools on top of an architecture for grid-based distributed data analysis. Keywords are: semantic models, agent technology, formal concept analysis, datamining, textmining, gridmining, grammar induction, question answering and the dynamic maintenance of ontologies. Key research challenges are:

• How to organize the content of domain specific knowledge: The creation and maintenance of semantics models of complex scientific domains on the basis of a multitude of heterogeneous sources of information.

• How to deploy the models in the knowledge extraction process: The availability of perfect models of the domain does not guarantee a performance boost of existing knowledge extraction algorithms. For each algorithm a careful analysis of the function of the model information in various stages of its execution has to be made. Theoretical assumptions about complexity issues and search bias have to be validated against empirical evidence.

• Integration with Grid technologies: The possibility of implementing this process on the basis of an architecture of co-operative agents in a grid.