2016/05/13 by Dag Hovland, Davide Lanti, Hovland, Dag +5 · 1 citation
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Databases (cs.DB) #FOS: Computer and information sciences #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.48550/arxiv.1605.04263
openalex publication_date 2016/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In Ontology Based Data Access (OBDA) users pose SPARQL queries over an ontology that lies on top of relational datasources. These queries are translated on-the-fly into SQL queries by OBDA systems. Standard SPARQL-to-SQL translation techniques in OBDA often produce SQL queries containing redundant joins and unions, even after a number of semantic and structural optimizations. These redundancies are detrimental to the performance of query answering, especially in complex industrial OBDA scenarios with large enterprise databases. To address this issue, we introduce two novel notions of OBDA constraints and show how to exploit them for efficient query answering. We conduct an extensive set of experiments on large datasets using real world data and queries, showing that these techniques strongly improve the performance of query answering up to orders of magnitude.