This paper introduces the DBpedia Temporal Knowledge Graph (DBpedia-TKG), an extension of the DBpedia extraction process to generate temporal versions of the knowledge graph. DBpedia has long served as a vital resource in the Semantic Web community and as a primary data source for research, offering structured information extracted from Wikipedia. However, it lacks a temporal dimension that captures the evolving nature of knowledge. Our approach addresses this gap by enabling the creation of temporal graph versions that reflect changes in Wikipedia pages across various revisions sourced from the Wikipedia meta-history dumps.  Our implementation runs in a containerized data extraction system, scaling across an eight-node cluster to extract the first version in under three days.

Our setup facilitates the generation of distinct DBpedia temporal graph variants through configurable settings, using different page extractors, temporal filters, and DBpedia ontology versions.  In our initial evaluation, we present comprehensive statistics demonstrating the impact of Wikipedia changes on the extracted data and provide insights into the temporal diversity of the knowledge graph. Finally, we discuss the potential benefits of DBpedia Temporal KG for various research domains. The first English version consists of around 1.7 billion extracted triples between 270 million different time points.