Knowledge Graph Creation Challenge: Results for SDM-RDFizer

Abstract

The amount of data being generated in recent years has increased drastically. Thus, a unified schema must be defined to bring multiple data sources into a single format. For that reason, the use of knowledge graphs has become much more commonplace. When creating a knowledge graph, different parameters affect the creation process, like the size and heterogeneity of the input data and the complexity of the input mapping. Multiple knowledge graph creation engines have been developed that handle these parameters differently. Therefore, a benchmark is needed to be defined to evaluate the performance of these engines. KGCW 2023 Challenge dataset presents a wide array of test cases to discover each engine's strengths and weaknesses and determine which engine is best suited for each case. This work reports the results of evaluating the performance of SDM-RDFizer while using this dataset.