Use Cases

The DBpedia Databus is a platform for sharing, discovering, and collaborating on data via a structured metadata Knowledge Graph. It provides a framework and infrastructure for managing and publishing datasets. SPARQL queries on the graph allow to select and recombine data for applications. Its flexibility, interoperability, and community-driven nature make it a valuable platform for managing and sharing data in the Linked Data ecosystem.

  1. Open Data Publishing: The Databus allows organizations and individuals to publish their datasets as Linked Data with RDF metadata, making it available for public discovery and consumption. It facilitates the sharing and reuse of data across all deployed Databuses with different domains and applications, which form a network of interoperable data registries.

  2. Research and Education: Researchers and educators can utilize the Databus to access a wide range of datasets for academic purposes. It provides a repository of curated and interconnected datasets that can be used to conduct experiments, build models, and explore various research questions. The new results of such research can be published on the Databus again using persistent identifiers and linking to input data, thus forming a provenance chain. Automated licence compatibility checks of combined datasets are available via

  3. Semantic Web Development: The Databus offers a valuable resource for developers working on semantic web projects. It provides access to RDF datasets, ontologies, and vocabularies, enabling developers to build applications that leverage Linked Data principles. Apps can be updated automatically by discovering new versions of datasets.

  4. Data Integration: The Databus enables data integration by providing a unified platform where datasets from various sources can be discovered and combined. Users can find relevant datasets, map them to a common schema, and integrate them into their applications or data pipelines. The Databus Client enables a powerful "Download As" functionality and converts multiple datasets into the specified format during download.

  5. Machine Learning and AI: The Databus serves as a valuable source of training data for machine learning and AI algorithms. Researchers and practitioners can access curated datasets to train models, improve algorithms, and develop innovative solutions in areas like natural language processing, knowledge graph construction, and information extraction.

  6. Knowledge Graph Construction: Databuses host a collection of RDF datasets such as DBpedia, which can be used as building blocks for constructing or extending knowledge graphs. Users can find and integrate relevant datasets to enrich their knowledge graphs, improving their coverage and accuracy.

  7. Data Quality Assessment: The Databus provides tools and mechanisms for assessing the quality of datasets. Users can contribute feedback, annotations, and evaluations on datasets as Additional Custom Metadata (ACM) graphs, enabling the community to collectively improve data quality and reliability.

  8. Reusable Data for Collaboration: The Databus facilitates community collaboration by enabling easy reuse of shared data. Users can discover and access a wide range of curated datasets, fostering a collaborative environment where individuals and organizations can leverage existing data to fuel their own projects, applications, and research. By simplifying the process of finding and reusing high-quality data, the Databus encourages a collective effort in building innovative solutions, improving data-driven insights, and driving advancements in various domains. The platform's emphasis on data consumption as a form of collaboration promotes knowledge exchange, accelerates development, and inspires new discoveries within the community.

  9. Data Maintenance and Versioning: The Databus supports dataset versioning, making it easier to track changes over time and maintain the integrity of the data. This is particularly important for 1. persistence and reproducibility, if scientific experiments worked with a specific version of a dataset, 2. applications might break with newer dataset versions and can be tested in a more systematic way before upgrading.

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