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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.01309 |
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| _version_ | 1866915655127662592 |
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| author | Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou |
| author_facet | Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing SPARQL Query Rewriting for Complex Ontology Alignments Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou Databases Artificial Intelligence 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. |
| title | Enhancing SPARQL Query Rewriting for Complex Ontology Alignments |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2505.01309 |