Funded Projects

SDA is currently funded with the following regional, national and European research projects.


Big Data EuropeBig Data Europe

Big Data Europe will undertake the foundational work for enabling European companies to build innovative multilingual products and services based on semantically interoperable, large-scale, multi-lingual data assets and knowledge, available under a variety of licenses and business models. Read more about BigDataEurope

 


HOBBITcropped-Hobbit_Logo_Claim_2015_rgb_300_130

HOBBIT is a European project that develops a holistic open-source platform and industry-grade benchmarks for benchmarking big linked data. Read more about HOBBIT


GEISERGEISER

GEISER is a European project that develops a holistic open-source platform for benchmarking sensor data to Internet-based Geo-Services. Read more about GEISER


SLIPOSlipo_vertical_1(145x130)

 

SLIPO is a European project that develops a Scalable Linking and Integration of Big POI data. Read more about SLIPO


qrowd-logo(145x130)QROWD

QROWD is a European project that will deliver innovative solutions to improve transport and mobility in European cities combining the power of the Qrowd and  RDF. Read more about QROWD


 Open Source Projects

SDA is currently working on high-impact R&D OpenSource projects.


SANSA Stacksansa-logo-blue (1)

 

Open source platform for distributed batch data processing for RDF large-scale datasets.Read more about SANSA Stack

 


AskNowAskNow

AskNow is a Question Answering (QA) system for RDF datasets. Read more about AskNow



DL-Learner_Logo2015_rgb-300x95DL-Learner

DL-Learner is a tool for learning concepts in Description Logics (DLs) from user-provided examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. The goal of DL-Learner is to support knowledge engineers in constructing knowledge and learning about the data they created.  Read more about DL-Learner



sml-benchSML-Bench

The ultimate goal of SML-Bench is to foster research in machine learning from structured data as well as increase the reproducibility and comparability of algorithms in that area. This is important, since a) the preparation of machine learning tasks in that area involves a significant amount of work and b) there are hardly any cross comparisions across languages as this requires data conversion processes. Read more about SML-Bench

 


 candidate1DeFacto

DeFacto (Deep Fact Validation) is an algorithm for validating statements by finding confirming sources for it on the web. It takes a statement (such as “Jamaica Inn was directed by Alfred Hitchcock”) as input and then tries to find evidence for the truth of that statement by searching for information in the web.  Read more about DeFacto


MEX Vocabularycandidate1

MEX Vocabulary: A Light-Weight Interchange Format for Machine Learning Experiments.  Read more about MEX Vocabulary


 

Incubator Projects


candidate1ML-DSLs

Domain Specific Languages for Machine Learning algorithms. Read more about this ML-DSLs


candidate1Cognitive Robotics

Cognitive Robotics. Read more about CogRob


candidate1Experimental Analysis of Class CS Problems

Experimental Analysis of Class CS Problems. Read more about this project


candidate1Smoothed Analysis ML

Smoothed Analysis of Structured Machine Learning Algorithms from Knowledge Graphs. Read more about this project


candidate1Tensor Factorisation and Visualization for Knowledge Graphs

Tensor Factorisation and Visualization for Knowledge Graphs. Read more about this project