SHARP: A Hybrid Approach for SPARQL Query Relaxation
Ginwa Fakih, Patricia Serrano-Alvarado, Matthieu Perrin · Studies on the semantic web · 2025
Purpose: Existing relaxation works either focus on ontology-based or entity-based elements, leaving a gap in handling queries that combine both. The purpose of this paper is to fill this gap and relax real-world SPARQL queries combining ontology-based and entity-based techniques to retrieve top-k relevant results. Methodology: We propose a hybrid query relaxation solution with a ranking system based on information content, integrating ontology-based and entity-based strategies by extending similarity measures beyond classes to entities and literals. Findings: The model retrieves relevant top-k results and better aligns with user expectations across diverse query types. It effectively ranks relaxed queries involving ontology-based and instance-based relaxations. Value: This work introduces a unified relaxation framework and an evaluation benchmark grounded in human-judgment-based relevance. It advances the literature by providing a relaxation system that aligns closely with user-validated relevance, especially for queries involving annotations.