Query Rewriting and Optimization for Ontological Databases

TL;DR

This paper introduces a novel query rewriting algorithm for ontological databases, enabling efficient translation of complex ontological queries into executable database queries, and discusses optimization techniques for improved performance.

Contribution

It presents a new, practical query rewriting algorithm for Datalog+/- ontologies and explores optimization strategies to generate efficient query rewritings.

Findings

Rewrote ontological queries into unions of conjunctive queries (UCQ).

Supported linear and sticky existential rules in rewriting process.

Enhanced query rewriting efficiency for practical applications.

Abstract

Ontological queries are evaluated against a knowledge base consisting of an extensional database and an ontology (i.e., a set of logical assertions and constraints which derive new intensional knowledge from the extensional database), rather than directly on the extensional 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 first-order query against the underlying extensional database. We present a novel query rewriting algorithm for rather general types of ontological constraints which is well-suited for practical implementations. In particular, we show how a conjunctive query against a knowledge base, expressed using linear and sticky existential rules, that is, members of the recently introduced Datalog+/- family of ontology languages, can be compiled into a union of conjunctive queries (UCQ) against the underlying database. Ontological query optimization, in this context, attempts to improve this rewriting process so to produce possibly small and cost-effective UCQ rewritings for an input query.