Ontological Queries: Rewriting and Optimization (Extended Version) (1112.0343v1)

Abstract: Ontological queries are evaluated against an ontology rather than directly on a database. The evaluation and optimization of such queries is an intriguing new problem for database research. In this paper we discuss two important aspects of this problem: query rewriting and query optimization. Query rewriting consists of the compilation of an ontological query into an equivalent query against the underlying relational database. The focus here is on soundness and completeness. We review previous results and present a new rewriting algorithm for rather general types of ontological constraints. In particular, we show how a conjunctive query against an ontology can be compiled into a union of conjunctive queries against the underlying database. Ontological query optimization, in this context, attempts to improve this process so to produce possibly small and cost-effective UCQ rewritings for an input query. We review existing optimization methods, and propose an effective new method that works for linear Datalog+/-, a class of Datalog-based rules that encompasses well-known description logics of the DL-Lite family.

• The paper introduces new methods for rewriting and optimizing ontological queries for efficient execution over databases.
• It presents a novel rewriting algorithm that compiles conjunctive queries over ontologies into unions of conjunctive queries for databases.
• The authors discuss optimization methods, including a new one for linear TGDs, enabling efficient first-order rewritability crucial for Ontology Based Data Access (OBDA).

Ontological Queries: Rewriting and Optimization

This paper, "Ontological Queries: Rewriting and Optimization," authored by Georg Gottlob, Giorgio Orsi, and Andreas Pieris, explores the intricacies of evaluating and optimizing ontological queries, which are executed against an ontology rather than a direct database. The key focus is on two pivotal aspects in this domain: query rewriting and optimization.

Query Rewriting

The process of query rewriting involves transforming an ontological query into an equivalent form that can be executed against the underlying relational database. This transformation aims to maintain soundness and completeness. The authors introduce a new rewriting algorithm catering to a broad spectrum of ontological constraints. Specifically, they demonstrate that a conjunctive query applied to an ontology can be compiled into a union of conjunctive queries (UCQ) for databases, thereby enabling efficient database query execution.

Query Optimization

Query optimization is directed towards refining the query compilation process to produce potentially smaller and cost-effective UCQs. The paper discusses existing optimization methods and introduces a novel method applicable to linear TGDs, a class of Datalog-based rules. These rules encompass well-known description logics such as DL-Lite, which are central to Ontology Based Data Access (OBDA). The authors explore how these linear TGDs enable first-order rewritability, facilitating the reformulation of an ontological query into a first-order (FO) query—a process that promises significant data efficiency.

Implications and Future Directions

The implications of this research are substantial for both practical database management and theoretical advancements in logic and computational theory. Practically, it aids in enhancing database systems to seamlessly assimilate ontological querying as part of everyday operations, especially for large, heterogeneous data models. Theoretically, it underscores the relevance of Datalog-based frameworks in maintaining tractability and efficiency in queries across ontologies.

The paper hints at future improvements in the implementation of rewriting strategies, transitioning from UCQs to non-recursive Datalog programs. The authors express interest in exploring advanced optimization techniques that could further reduce the complexity and size of rewritten queries, leveraging cutting-edge algorithms and computational approaches.

This work exemplifies a substantive advancement in the field of query processing over ontologies, providing a robust foundation for future developments in efficient database querying and ontology integration.