Towards Practicable Algorithms for Rewriting Graph Queries beyond DL-Lite
Bianca Löhnert, Nikolaus Augsten, Cem Okulmus, Magdalena Ortiz
TL;DR
The article tackles ontology-mediated querying for graph-structured data by extending DL-Lite-style rewriting to a pragmatic fragment, ELH_i^ql, that preserves $NL$ data complexity while enabling navigational reasoning. It introduces NCQs to capture graph query features like reachability, and develops a CDG-based reasoning framework to rewrite atomic queries and NCQs into unions of C2RPQs, later transforming them into UC2RPQs and Cypher queries for Neo4j. A proof-of-concept implementation demonstrates rewriting NCQs to Cypher and evaluating on a cognitive neuroscience dataset using the Cognitive Task Ontology (COGITO) and OpenNeuro MRI data, with promising performance. The work provides a concrete path toward practical OBDA on graph DBMSs and suggests that nested and restricted regular path queries are a viable target for scalable ontology-aware graph querying.
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.