BioDig : architecture for integrating heterogeneous biological data repositories using ontologies

Abstract

High-throughput experiments generate vast quantities of biological information that are stored in autonomous data repositories distributed across the World Wide Web. There exists a need to integrate information from multiple data repositories for the purposes of data mining; however, current methods of integration require a significant amount of manual work that is often tedious and time consuming. The thesis proposes a flexible architecture that facilitates the automation of data integration from multiple heterogeneous biological data repositories using ontologies. The design uses ontologies to resolve the semantic conflicts that usually hinder schema integration and searching for information. The architecture implemented successfully demonstrates how ontologies facilitate the automation of data integration from multiple data repositories. Nevertheless, many optimizations to increase the performance of the system were realized during the implementation of various components in the architecture and are described in the thesis.