Development of high throughput data acquisition in a number of domains (e.g., biological sciences, space sciences, etc.) along with advances in digital storage, computing, and communication technologies have resulted in unprecedented opportunities in scientific discovery, learning, and decision-making. In practice, the effective use of increasing amounts of data from a variety of sources is complicated by the autonomous and distributed nature of the data sources, and the heterogeneity of structure and semantics of the data. In many applications e.g., scientific discovery, it is necessary for users to be able to access, interpret, and analyze data from diverse sources from different perspectives in different contexts. This thesis presents a novel ontology-driven approach, which builds on recent advances in artificial intelligence, databases, and distributed computing to support customizable information extraction and integration in such domains. The proposed approach has been realized as part of a prototype implementation of INDUS, an environment for data-driven knowledge acquisition from heterogeneous, distributed, autonomous data sources in Bioinformatics and Computational Biology.