# SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs

arXiv: https://arxiv.org/abs/2008.07176 · HTML: https://mlsanity.com/paper/2008.07176/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Stratified Data Integration](https://mlsanity.com/paper/2105.09432/) (2021)
- [Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs](https://mlsanity.com/paper/2308.13433/) (2023)
- [VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph](https://mlsanity.com/paper/2309.13610/) (2023)
- [Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems](https://mlsanity.com/paper/2307.11637/) (2023)
- [Construct, Align, and Reason: Large Ontology Models for Enterprise Knowledge Management](https://mlsanity.com/paper/2602.00029/) (2026)
- [EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines](https://mlsanity.com/paper/2112.07493/) (2021)
- [Towards Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories](https://mlsanity.com/paper/2308.01732/) (2023)
- [Scaling Up Knowledge Graph Creation to Large and Heterogeneous Data Sources](https://mlsanity.com/paper/2201.09694/) (2022)
- [Semantic Data Management in Data Lakes](https://mlsanity.com/paper/2310.15373/) (2023)
- [A Protocol for KG Construction Tasks Involving Users](https://mlsanity.com/paper/2412.16766/) (2024)
- [Semantic Web: Past, Present, and Future (with Machine Learning on Knowledge Graphs and Language Models on Knowledge Graphs)](https://mlsanity.com/paper/2412.17159/) (2024)
- [A Pythonic Functional Approach for Semantic Data Harmonisation in the ILIAD Project](https://mlsanity.com/paper/2604.13042/) (2026)

## Abstract (arXiv)

In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the impact negatively of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.