Query Evaluation and Optimization in the Semantic Web

TL;DR

This paper introduces a cost-based query optimization method for Deductive Ontology Bases in the Semantic Web, improving query efficiency through a hybrid cost model and dynamic programming.

Contribution

It presents a novel hybrid cost model and a dynamic programming algorithm for optimizing query evaluation plans in Deductive Ontology Bases.

Findings

The cost model accurately predicts query costs and cardinalities.

Optimization techniques significantly improve query performance.

Experimental results validate the effectiveness on real-world and synthetic ontologies.

Abstract

We address the problem of answering Web ontology queries efficiently. An ontology is formalized as a Deductive Ontology Base (DOB), a deductive database that comprises the ontology's inference axioms and facts. A cost-based query optimization technique for DOB is presented. A hybrid cost model is proposed to estimate the cost and cardinality of basic and inferred facts. Cardinality and cost of inferred facts are estimated using an adaptive sampling technique, while techniques of traditional relational cost models are used for estimating the cost of basic facts and conjunctive ontology queries. Finally, we implement a dynamic-programming optimization algorithm to identify query evaluation plans that minimize the number of intermediate inferred facts. We modeled a subset of the Web ontology language OWL Lite as a DOB, and performed an experimental study to analyze the predictive capacity of our cost model and the benefits of the query optimization technique. Our study has been conducted over synthetic and real-world OWL ontologies, and shows that the techniques are accurate and improve query performance. To appear in Theory and Practice of Logic Programming (TPLP).