I am currently a Ph.D. Candidate under the supervision of Prof. V.S. Subrahmanian. Although my research interest range widely from automated reasoning to database query optimization and indexing, during my graduate studies I focused on the area of knowledge representation and reasoning. I strongly believe that the future of the World Wide Web largely depends on our ability as computer scientists to discover and make use of the wealth of knowledge lying hidden within the massive amount of data available online. My thesis research takes a first step towards this goal, by defining methods to scale up storage, query and reasoning methods in ontology systems to very large data sizes.

Ontologies have become commonplace as a way to represent both knowledge and data. The biomedical field is a clear success story – many biomedical ontologies are currently in use, several of which large enough to test the limits of existing knowledge storage systems. Such systems typically use a relational database representation to store ontological data. During my research, I discovered that querying and reasoning with ontologies yields access patterns different than those of traditional database queries, to the extent that performance degrades significantly when using relational models. My thesis “Scalable Ontology Systems” describes new query, indexing and integration algorithms tailored for knowledge represented in the form of ontologies. Find out more on my research page.