Enhancing SPARQL Query Rewriting for Complex Ontology Alignments
This work addresses the problem of querying heterogeneous ontologies in the Linked Data Web for users, especially non-experts, by enabling more expressive alignments, though it appears incremental as it builds on existing methods with new integrations.
The paper tackles the challenge of SPARQL query rewriting for complex ontology alignments, particularly rich (c:c) correspondences, by proposing an approach that automatically rewrites queries from natural language using equivalence transitivity and large language models like GPT-4, resulting in efficient handling of these alignments and improved accessibility for non-expert users.
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.