Graph-based Information Exploration over Structured and Unstructured Data

With the rise of the Semantic Web, several public semantic repositories like Knowledge Bases, Ontologies and Taxonomies have been developed in a variety of domains. For specific domains like the biomedical domain they have already formed a huge valuable infrastructure. On the other hand, the development of efficient algorithms for Natural Language Processing gave us access to the massive knowledge hidden in many unstructured resources. Combining and harvesting these two worlds would result into a very productive knowledge fusion applicable in several domains. In this paper, an extensible framework is presented that focuses on accessing and graphically presenting the knowledge coming from all available structured and unstructured resources. An abstraction formalism for representing any type of query based on graphs is the base of this approach. This formalism makes the framework accessible to non-expert users that have no knowledge of constructing queries in any querying language and barely understand what structured and unstructured resources are. The architecture that will allow for the framework to be adaptable to all available resources is described along with a proof of concept implementation in the biomedical domain.