Semantic Question Answering The use of Semantic Web technologies led to an increasing number of structured data published on the Web. Despite the advances on question answering systems retrieving the desired information from structured sources is still a substantial challenge.

We are very happy to announce AskNow 0.2 – the second release of Question Answering Components and Tools over RDF Knowledge Graphs.

Artificial intelligence/machine learning and semantic technologies/knowledge graphs are central topics for SDA. Throughout the year, we have been able to accomplish a range of interesting research achievements. One particularly active area was question answering and dialogue systems (with and without knowledge graphs). We acquired new projects for more than a million Euro this year and were able to transfer our expertise to industry via successful projects at Fraunhofer. External interest in our results has been remarkably high. Furthermore, we extended our already established position in scalable distributed querying, inference, and analysis of large RDF datasets. Among the race for ever-improving achievements in AI, which has gone far beyond what many could have imagined 10 years ago, our researchers were able to deliver important contributions and continued to shape different sub-areas of the growing AI research landscape.

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

The Smart Data Analytics group is very happy to announce SANSA 0.1 – the initial release of the Scalable Semantic Analytics Stack. SANSA combines distributed computing and semantic technologies in order to allow powerful machine learning, inference and querying capabilities for large knowledge graphs. You can find the FAQ and usage examples at http://sansa-stack.net/faq/. We want to thank everyone who helped to create this release, in particular, the projects Big Data Europe, HOBBIT and SAKE.

DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis. DL-Learner is used for data analysis tasks within other tools such as ORE and RDFUnit. Technically, it uses refinement operator based, pattern-based and evolutionary techniques for learning on structured data. It also offers a plugin for Protégé, which can give suggestions for axioms to add.