RMLWeaver-JS: An algebraic mapping engine in the KGCW Challenge 2024

Sitt Min Oo, Tristan Verbeken, Ben De Meester

Keywords: Mapping engine, mapping algebra, RML, knowledge graph construction

TL;DR: A proof of concept algebraic mapping engine which executes RML mappings

Abstract:

We present the Knowledge Graph Construction Workshop (KGCW) Challenge 2024
results of our proof of concept mapping engine, RMLWeaver-JS, implemented in
JavaScript and based on the reactive programming paradigm.
RML documents are translated into a mapping plan consisting of algebraic
mapping operators, which RMLWeaver-JS uses to execute the mapping workload.
RMLWeaver-JS is evaluated for Track 2 on performance for the Knowledge Graph
Construction Challenge for CSV files.
The results of the challenge showed that RMLWeaver-JS has a constant memory
usage across different workloads, and scales linearly regarding CPU usage and
execution time.
However, the results also show that the execution time of RMLWeaver-JS greatly
depends on the generated mapping plan.
As future works, we will focus on the optimizations of the generated mapping
plan.

Submission Number: 10