Biomedical data integration and ontology-driven multi-facets visualization

With the proliferation of different heterogeneous biomedical data sources and with the growing amount of their content available over the Web, there is, on one side, the need to support mashing and data integration and, on the other side, the more urgent need to relate literature and research results that are often enclosed in unstructured textual documents. Nowadays, ontologies have been used as a common access knowledge layer playing a crucial role to support categorized access to the information resources. Moreover, manual construction of a domain-specific ontology and content categorization is a labor intensive and a time-consuming process. This work focuses on the development of a novel biomedical ontology-driven multi-facets visualization to support categorized access to heterogeneous and unstructured biomedical data sources (e.g., PubMed, WikiGenes). Specifically, the framework relies on: knowledge extraction methodology, to automatically extract ontology exploiting the Fuzzy Formal Concept Analysis theory; and ontology matching strategy to find relation between extracted ontology and the available ones in the field of biomedicine (e.g., Ontology of Gene and Genomes, Gene Ontology, Protein Ontology). The evaluation will be shown in terms of Precision and Recall by using biomedical ontology concepts as input query to the multi-facets visualization engine.