Though there has been tremendous interest in ontologies in the Semantic Web community, and tremendous interest in querying and integrating heterogeneous data sources, the two have not come together. We started by developing methods to improve the quality of answers to database queries (in particular improving recall) by using ontologies. We subsequently developed sophisticated algorithms to automatically infer ontologies. Our specific contributions fall into four categories:
- Ontology Systems
- HOME: a prototype system that supports querying ontology extended relational databases, as well as ontology extended XML databases.
- TOSS: a framework that answers queries to XML data sources by association ontologies with them and using notions of similarity.
- Probabilistic Ontology: we have developed the concept of a probabilistic ontology and extensive algorithms to answer queries efficiently.
- Annotated RDF: a prototype system that permits extensions to RDF with user-defined annotations.
- Ontology Inference
- Ontology Integration
- CROW: a sophisticated algorithm to integrate RDF ontologies together in the presence of a set of interoperation constraints.
- ILIADS: a novel algorithm for integrating OWL ontologies.
- RDF Databases
- RDF Aggregate Queries and Views: we have developed sophisticated view maintenance algorithms and the notion of an aggregate operation for RDF data.
- GRIN: a novel RDF indexing technique that can speed up queries 3-4 times when compared to Jena2, RDFBroker, Sesame, etc.