SANSA-ML is the Machine Learning (ML) library in the SANSA stack (see http://sansa-stack.net). Algorithms in this repository perform various machine learning tasks directly on RDF/OWL input data. While most machine learning algorithms are based on processing simple features, the machine learning algorithms in SANSA-ML exploit the graph structure and semantics of the background knowledge specified using the RDF and OWL standards. In many cases, this allows to obtain either more accurate or more human-understandable results. In contrast to most other algorithms supporting background knowledge, they scale horizontally using Apache Spark and Apache Flink.
The ML layer currently supports the following algorithms:
- RDF graph clustering (Power Iteration, Border Flow, Link based clustering, Modularity based clustering, Silvia Link Clustering)
- Rule mining in RDF graphs based on AMIE+
- Semantic similarity measures (Jaccard similarity,Rodríguez and Egenhofer similarity, Tversky Ratio Model, Batet Similarity)
- Knowledge graph embedding approaches:
- TransE (beta status)
- DistMult (beta status)
- Terminological Decision Trees for the classification of concepts(beta status)
- Anomaly detection (beta status)
- RDF graph kernel based on A Fast and Simple Graph Kernel for RDF
Please see https://github.com/SANSA-Stack/SANSA-Examples/tree/master/sansa-examples-spark/src/main/scala/net/sansa_stack/examples/spark/ml for examples on how to use the above machine learning approaches.
Several further algorithms are in development. Please create a pull request and/or contact Jens Lehmann if you are interested in contributing algorithms to SANSA-ML.