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Query-Specific Pruning of RML Mappings (Extended Version)

Sitt Min Oo, Olaf Hartig

Abstract

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.

Query-Specific Pruning of RML Mappings (Extended Version)

Abstract

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.

Paper Structure

This paper contains 21 sections, 6 theorems, 2 equations, 1 figure, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Demonstration of the Approach
  3. Preliminaries
  4. Relevant Concepts of RDF and SPARQL
  5. Data Model of the Mapping Algebra
  6. Syntax of RML-Specific Mapping Expressions
  7. Semantics of RML-Specific Mapping Expressions
  8. Satisfiability
  9. Incompatibility
  10. Pruning Algorithm
  11. Evaluation
  12. Benchmark
  13. Setup
  14. Results
  15. Pruning Time.
  16. ...and 6 more sections

Key Result

proposition 1

Let $P$ be a graph pattern and let $M$ and $M'$ be RML-spe-cif-ic mapping expressions such that $\textrm{TrMaps}(M') \subseteq \textrm{TrMaps}(M)$ and, for every TrMap-ex-pres-sion $M_\mathsf{TM} \in \bigl( \textrm{TrMaps}(M) \setminus \textrm{TrMaps}(M') \bigr)$, none of the triple patterns in $P$

Figures (1)

  • Figure 1: The evaluation pipeline consists of four steps: i) translation, ii) pruning, iii) materialization, and iv) querying. While solid-lined arrows indicate the execution flow including our pruning approach, dashed arrows capture the execution flow without pruning.

Theorems & Definitions (19)

  • definition 1
  • definition 2
  • definition 3
  • definition 4
  • definition 5
  • definition 6
  • definition 7
  • definition 8
  • definition 9
  • definition 10
  • ...and 9 more