Call for Papers ESWC 2021 Semantic Data Management, Querying, and Distributed Data


Semantic data management refers to approaches that focus on maintaining and using data in terms of its meaning. While there exist effective solutions for semantic data management, the decentralization and the big data characteristics of the web prevent such solutions from being used at large scale.

The aim of this track is to gather researchers and developers from the Semantic Web, Databases, and Artificial Intelligence fields to discuss research issues, experiences, and results in designing, implementing, deploying, and evaluating theories, techniques, and applications related to semantic data management on distributed semantic data sources.

Negative Results

As a new theme in 2021, ESWC encourages the submission of negative results papers. Specific instructions for negative results papers can be found here.

Topics of Interest

Topics of interest include, but are not limited to

  • Efficient representations of Semantic Data
  • Storage models for Semantic Data
  • Distributed infrastructures for Semantic Data Management
  • Efficient techniques for query processing of distributed, decentralized, or federated Semantic Data
  • Semantic searching and browsing
  • Semantic data integration and quality assessment
  • Machine Learning for Semantic Data Management
  • Management of context and provenance of Semantic Data
  • Access control and privacy in Semantic Data
  • Ranking of Semantic Data
  • Semantic Data analytics
  • Ontology-Based data management
  • Extensions to SPARQL and novel query languages for Semantic Data
  • Semantic Data management for Linked (Open) Data
  • Semantic data management techniques for Findable, Accessible, Interoperable and Reusable (FAIR) Data
  • Novel domain-specific Semantic Data management approaches for, e.g., life sciences, e-government, healthcare, finance, cultural heritage

Delineation from other tracks

  • Concrete applications of Semantic Data based on existing solutions should be submitted to the In-Use or Industry Track.
  • Empirical evaluations and novel data management benchmarks should be submitted to the Resources Track.


Information on deadlines and submission formats can be found here.

Track Chairs

  • Maribel Acosta, Ruhr-University Bochum, Germany
  • Hala Skaf-Moli, LS2N, University of Nantes, France

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