Scalable Ontology Systems

Thumbnail Image


umi-umd-5631.pdf (1.23 MB)
No. of downloads: 1035

Publication or External Link






Since the adoption of the Resource Description Framework (RDF) by the World Wide Web Consortium (W3C), ontologies have become commonplace as a way to represent both knowledge and data. RDF databases have flexible schemas, are easy to integrate and allow a semantically rich query language. Unfortunately, these advantages come at the expense of increased query and application complexity. Existing RDF systems have attempted to address this problem by representing RDF data in relational format and translating queries and answers to and from SQL. As we will show, typical access patterns in RDF are substantially different than those in relational databases, to the extent that the performance of relational-backed systems degrades significantly for large datasets or complex queries.

In this dissertation, we propose two solutions to the scalability issue in RDF databases. First, we introduce Annotated RDF, a representation language that extends the semantics of RDF by allowing triples to be annotated with partially ordered information such as temporal validity intervals, probabilities, provenance and many others. In standard RDF, using such information creates a blowup in the size of the database and therefore greatly increases the data complexity of queries. We define a query language for Annotated RDF that extends the RDF query language SPARQL and provides query processing and view maintenance algorithms. Our experimental evaluation shows Annotated RDF can answer queries 1.5 to 3.5 times faster than widely used systems such as Jena2, Sesame2 or Oracle 11g.

Second, we introduce GRIN, to our knowledge the first index structure designed specifically for SPARQL queries. We describe query and update processing algorithms and a theoretical analysis of index optimization. GRIN is extended to Annotated RDF and evaluated thoroughly on real-world datasets of up to 26 million triples and benchmark synthetic datasets of up to 1 billion triples. Our results show that for SPARQL queries, GRIN outperforms all relational index structures at comparable resource expenditure. Moreover, we show GRIN can be integrated with Annotated RDF, but also with existing systems such as Jena2 or LucidDB.