Towards Practicable Algorithms for Rewriting Graph Queries beyond DL-Lite

TL;DR

This paper introduces a new algorithmic approach for rewriting complex navigational graph queries within an extended ELHI fragment, enabling practical use with graph databases like Neo4j, demonstrated through a neuroscience case study.

Contribution

It presents a novel query rewriting technique for an extended ELHI fragment, supporting navigational queries and practical implementation in Cypher for graph databases.

Findings

Prototype successfully rewrites queries into Cypher

Applied to real-world neuroscience data

Achieved promising performance results

Abstract

Despite the many advantages that ontology-based data access (OBDA) has brought to a range of application domains, state-of-the-art OBDA systems still do not support popular graph database management systems such as Neo4j. Algorithms for query rewriting focus on languages like conjunctive queries and their unions, which are fragments of first-order logic and were developed for relational data. Such query languages are poorly suited for querying graph data. Moreover, they also limit the expressiveness of the ontology languages that admit rewritings, restricting them to those where the data complexity of reasoning is not higher than it is in first-order logic. In this paper, we propose a technique for rewriting a family of navigational queries for a suitably restricted fragment of ELHI that extends DL-Lite and that is NL-complete in data complexity. We implemented a proof-of-concept prototype that rewrites into Cypher queries, and tested it on a real-world cognitive neuroscience use case with promising results.