SemaGrow (a) develops scalable and robust semantic storage and indexing algorithms that can take advantage of resource naming conventions and other natural groupings of URIs to compress data source annotations about extremely large datasets; (b) develops query decomposition, source selection, and distributed querying methods that take advantage of such algorithms to implement a scalable and robust infrastructure for data service federation; and (c) rigorously tests its components and overall architecture over real, complex, interconnected datasets comprising data and document collections, sensor data, and GIS data. Our involvement in SemaGrow is on ontology alignment, emphasizing on user-related aspects and scalability issues.