SANSA includes several APIs for creating applications:
- RDF-X/OWL-X API for RDF/OWL operations,
- RDF-Query API support a query language on top of distributed RDF library,
- OWL-Inference API implementation of the closed world reasoner on OWL library,
- RDF-ML/OWL-ML Machine Learning Core Library
At SANSA-Stack, when in search for a big data science and engineering framework, we look for one key thing: productivity. Big data science and engineering need a framework for much better productivity. Instead of viewing things bottom-up, we take a top-down view of the big data stack, and ask what kind of API we would want to maximize data-engineering productivity. We ended up with developing the SANSA-Stack, a Distributed Structured ML framework.
RDF-X/OWL-X Library
RDF and OWL Library provide a base structure for Knowledge Processing. Read the RDF-X/OWL-X guide, which includes various usage examples.
RDF-Querying Library
Support a query language on top of our distributed RDF library. In many cases, it might be more convenient to perform queries directly in programs instead of writing the code corresponding to those queries (grouping, sorting, filtering etc.)
OWL-Inference Library
Implementation of the closed world reasoner in Spark / Flink.
RDF/OWL-ML Library
SANSA-ML is the Machine Learning (ML) scalable library for SANSA. With ML we aim to provide scalable ML algorithms, an intuitive API, and tools that help minimize glue code in end-to-end ML systems. SANSA use in-memory iterative computation, which enabling SANSA-ML to run fast. SANSA-ML contains high-quality algorithm that leverage iteration which can provide better results then other approaches which use MapReduce paradigm. It consists of common learning algorithms and utilities, including classification, regression, clustering, frequent pattern mining, as well as lower-level optimization primitives and higher-level pipeline APIs. Refer to the SANSA-ML guide for usage examples.
RDF/OWL-ML Library
SANSA-ML is the Machine Learning (ML) scalable library for SANSA. With ML we aim to provide scalable ML algorithms, an intuitive API, and tools that help minimize glue code in end-to-end ML systems. SANSA use in-memory iterative computation, which enabling SANSA-ML to run fast. SANSA-ML contains high-quality algorithm that leverage iteration which can provide better results then other approaches which use MapReduce paradigm. It consists of common learning algorithms and utilities, including classification, regression, clustering, frequent pattern mining, as well as lower-level optimization primitives and higher-level pipeline APIs. Refer to the SANSA-ML guide for usage examples.
Algorithms
ML currently supports the following algorithms:
Supervised Learning
- Classification
- Distributed SPARQL Query Tree Learning
- Decision Tree Learning
- Regression
- Decision Tree Learning
Unsupervised Learning
- Frequent Pattern Mining
- Association Rule Learning