Query-Specific Pruning of RML Mappings (Extended Version)
This work addresses the problem of inefficient knowledge graph construction for users in dynamic federated querying environments where queries change frequently, representing an incremental improvement over existing methods.
The paper tackles the inefficiency of RML mapping engines in dynamic query environments by introducing query-specific pruning of RML mappings for partial RDF graph materialization, resulting in significant reductions in materialization time and RDF graph size, with noticeable improvements in querying time as shown in the GTFS-Madrid benchmark.
Current approaches for knowledge graph construction with RML focus on full RDF graph materialization without considering user queries. As a result, mapping engines are inefficient in dynamic query environments, materializing large graphs even when only a small subset is needed to answer user queries. In this paper, we formally define satisfiability for SPARQL queries with respect to RDF data obtained via RML mappings and use this property to prune RML mappings for partial RDF graph materialization. Evaluation on the GTFS-Madrid benchmark shows that pruning significantly reduces materialization time, and RDF graph size while also noticeably improving querying time. Thus, enabling existing materialization engines to efficiently support generating RDF graphs in dynamic federated querying environment where user queries change frequently.