Managing Data with Changing Schemata in RDF databases

1. UMBCAN HONORS UNIVERSITY IN MARYLAND Managing Data with Changing Schemata in RDF databases Lushan Han, Tim Finin, Anupam Joshi and Yelena Yesha Relational data model vs. RDF data model Motivation Use RDF databases and Semantic Web technology to gracefully handle exten-sible and/or dynamic schemata in relational databases. Problem Statement Relational databases have inherent difficulties when dealing with chang-ing schemata. Because their schemata are defined and constrained by physical tables, it is hard to accommodate a new schema when large amounts of data are already stored in the existing schema. Changing schemata in RDF databases 3. Incrementally transfer data between schemes 1. A subclass hierarchy 2. Mapping between two schemes Ontology 2 Ontology 1 • Approach Outline • Use the RDF data model instead of a relational data model to store data. • Use Semantic Web technology to gracefully handle changing scheme. The tradeoff of mapping between schemata is query performance. If a schema becomes obsolete, we can transfer its data to another schema. If large amounts of data are involved, we can reduce the transfer cost by incre-mentally performing the transfer while still make the mapping work. Rlue set The subclass hierarchy enables us to view objects in different class perspectives. We first define a general ontology in the project’s domain. When new customers give their specific schemata, we can add new subclasses or sub-properties. An inference on subclass relationship can allow users to query on objects in different class perspectives. We can define a mapping between two schemata using rules. Once a mapping is defined, we can query with just one schema whereas the data linked to the other schema is also available to the query. The mapping is transparent to users.