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. Resource type: Dataset License: CC BY-SA 4.0 Dataset DOI: https://doi.org/10.5281/zenodo.14532571 Code URL: https://github.com/dbpedia/dbpedia-temporal .

International audience

Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.