SANSA Notebooks

In this blog post, we highlight the demonstration of SANSA Notebooks. Semantic Analytics Stack (SANSA) is a project for developing open source algorithms for distributed data processing for large-scale RDF knowledge graphs.

SANSA Notebooks are interactive notebooks for the Spark subset of SANSA. The notebooks can be deployed locally following the instructions from the SANSA Notebooks Github repository. To demonstrate the capabilities of the notebooks, we provide the public installation on our servers. To login into the public demo deployment use user/user login and password. In the demo, you can navigate through the notebooks and view their results. The modification and execution of the notebooks are disabled for security reasons.

SANSA POI Notebook

If you have questions feel free to open an issue on our issue tracker.

SANSA 0.2 (Semantic Analytics Stack) Released

The Smart Data Analytics group is happy to announce SANSA 0.2 – the second release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing for semantic technologies in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs.

You can find the FAQ and usage examples at http://sansa-stack.net/faq/.

The following features are currently supported by SANSA:

  • Reading and writing RDF files in N-Triples format
  • Reading OWL files in various standard formats
  • Querying and partitioning based on Sparqlify
  • RDFS/RDFS Simple/OWL-Horst forward chaining inference
  • RDF graph clustering with different algorithms
  • Rule mining from RDF graphs

Deployment and getting started:

  • There are template projects for SBT and Maven for Apache Spark as well as for Apache Flink available to get started.
  • The SANSA jar files are in Maven Central i.e. in most IDEs you can just search for “sansa” to include the dependencies in Maven projects.
  • There is example code for various tasks available.
  • We provide interactive notebooks for running and testing code via Docker.

We want to thank everyone who helped to create this release, in particular, the projects Big Data Europe,  HOBBIT , SAKE and Big Data Ocean.

SANSA Development Team