Towards Practicable Algorithms for Rewriting Graph Queries beyond DL-Lite (Extended Abstract)

2024-05-28

Description Logics

AI Summary

Existing OBDA systems lack native support for graph databases (e.g., Neo4j), and their query rewriting algorithms—designed for relational query languages and DL-Lite ontologies—fail to capture graph navigation semantics, with reasoning capabilities limited to first-order logic. To address this, we propose the first navigation-aware query rewriting method for extended ELHI ontologies—strictly more expressive than DL-Lite and NL-complete—enabling semantics-preserving translation into native graph query languages such as Cypher. Our approach operates over a tractable fragment of ELHI, integrating graph pattern matching with path-constraint reasoning to achieve fully automated rewriting. Experimental evaluation on a real-world cognitive neuroscience dataset demonstrates that our prototype system efficiently handles complex navigational queries. The results validate significant advances in expressive power, reasoning capability, and practical applicability for graph-based OBDA.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Lack of OBDA support for graph databases like Neo4j
• Existing query languages poorly suited for graph data
• Limited expressiveness of ontology languages for rewritings

Innovation

• Rewriting navigational queries for ELHI fragment
• Extending DL-Lite with NL-complete data complexity
• Implementing Cypher query rewriting prototype