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.

翻译：当前基于RML的知识图谱构建方法侧重于完整RDF图物化，未考虑用户查询。因此，映射引擎在动态查询环境中效率低下，即使仅需少量数据即可回答用户查询，仍会物化大规模图。本文正式定义了基于RML映射获取的RDF数据上SPARQL查询的可满足性，并利用该特性对RML映射进行剪枝，实现部分RDF图物化。在GTFS-Madrid基准测试上的评估表明，剪枝显著缩短了物化时间和RDF图规模，同时明显提升了查询效率。从而使得现有物化引擎能够高效支持用户查询频繁变化的动态联合查询环境中的RDF图生成。