Enhancing SPARQL Query Rewriting for Complex Ontology Alignments

TL;DR

This paper introduces a novel method for automatically rewriting SPARQL queries across complex ontologies by leveraging large language models like GPT-4, improving handling of expressive alignments and accessibility for non-expert users.

Contribution

It presents an innovative approach that combines equivalence transitivity principles with GPT-4 to enhance query rewriting for complex ontology alignments.

Findings

Efficient handling of complex (c : c) alignments.

Improved accessibility for non-expert users.

Enhanced query rewriting accuracy with LLMs.

Abstract

SPARQL query rewriting is a fundamental mechanism for uniformly querying heterogeneous ontologies in the Linked Data Web. However, the complexity of ontology alignments, particularly rich correspondences (c : c), makes this process challenging. Existing approaches primarily focus on simple (s : s) and partially complex ( s : c) alignments, thereby overlooking the challenges posed by more expressive alignments. Moreover, the intricate syntax of SPARQL presents a barrier for non-expert users seeking to fully exploit the knowledge encapsulated in ontologies. This article proposes an innovative approach for the automatic rewriting of SPARQL queries from a source ontology to a target ontology, based on a user's need expressed in natural language. It leverages the principles of equivalence transitivity as well as the advanced capabilities of large language models such as GPT-4. By integrating these elements, this approach stands out for its ability to efficiently handle complex alignments, particularly (c : c) correspondences , by fully exploiting their expressiveness. Additionally, it facilitates access to aligned ontologies for users unfamiliar with SPARQL, providing a flexible solution for querying heterogeneous data.