Results for Knowledge Graph Creation Challenge 2025: SDM-RDFizer

Enrique Antonio Iglesias, Maria-Esther Vidal

Keywords: Knowledge Graph Creation, Data Integration System, RDF Mapping Languages

TL;DR: The Results of the SDM-RDFizer in the KGCW 2025 Challenge

Abstract: In recent years, knowledge graphs (KGs) have grown in popularity. Major companies have incorporated them into their products to enhance the user experience. Consequently, numerous methods have emerged to address KG construction. The RDF Mapping Language (RML) is an extension of the W3C mapping language recommendation, R2RML. RML allows for the declarative definition of KG structures based on unified ontologies and data sources in various formats, including CSV, JSON, and XML files. However, RML incorporates CSV, JSON, and XML files. Furthermore, RML has additional functionalities, such as executing functions, using quoted triples, working with collections, and creating logical views. Thus, RML has become a standalone mapping language separate from R2RML. The KGCW 2025 Challenge defines a dataset comprising a series of test cases that cover all RML functionalities. These test cases evaluate the compliance of state-of-the-art knowledge graph (KG) creation engines. This paper reports on the conformance evaluation of SDM-RDFizer executing this dataset, highlighting its strengths and areas for improvement to enhance performance.

Submission Number: 13