SDA is currently funded with the following regional, national and European research projects.
Big 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.
The main objective of the BigDataOcean project is to enable maritime big data scenarios for EU-based companies, organizations and scientists, through a multi-segment platform that will combine data of different velocity, variety and volume under an inter-linked, trusted, multilingual engine to produce a big-data repository of value and veracity back to the participants and local communities. Read more about BigDataOcean.
The biggest European initiative in Big Data for Industry 4.0. With a 20M€ budget and leveraging 100M€ of private investment, Boost 4.0 will lead the construction of the European Industrial Data Space to improve the competitiveness of Industry 4.0 and will guide the European manufacturing industry in the introduction of Big Data in the factory, providing the industrial sector with the necessary tools to obtain the maximum benefit of Big Data. Read more about Boost4.0.
The CLEOPATRA ITN, a Marie Skłodowska-Curie Innovative Training Network aims to make sense of the massive digital coverage generated by the intense disruption in Europe over the past decade – including appalling terrorist incidents and the dramatic movement of refugees and economic migrants. Read more about Cleopatra.
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.
Innovation and Entrepreneurship for Iranian HE Graduates through Data Analytics. Read more about GraDAna.
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.
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.
A textual & graphical domain-specific language for semantic data analytics workflows. Read more about Simple-ML.
SLIPO is a European project that develops a Scalable Linking and Integration of Big POI data. Read more about SLIPO.
SDA was formerly funded with the following regional, national and European research projects:
- Be-IoT – The business engine for IoT pilots: Turning the Internet of things in Europe into an economically successful and socially accepted vibrant ecosystem.
- DIACHRON – Preserving the Evolving Data Web: Making Open / Linked Data Diachronic.
- EDSA – European Data Science Academy.
- LiDaKrA – Linked-data-based crime analysis.
- LinDA – Enabling Linked Data and Analytics for SMEs by renovating public sector information.
- LUCID – Linked Value Chain Data.
- LOD2 – Creating Knowledge out of Interlinked Data.
- ODINE – Open Data Incubator for Europe.
- OpenBudgets.eu – Financial Transparency Platform for the Public Sector.
- OSCOSS – A shared platform for Opening Scholarly Communication in the Social Sciences.
- SeReCo – Semantics, Coordination and Reasoning.
Open Source Projects
SDA is currently working on high-impact R&D OpenSource projects:
AskNow is a Question Answering (QA) system for RDF datasets. Read more about AskNow.
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 on the web. Read more about DeFacto.
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.
MEX Vocabulary: A Light-Weight Interchange Format for Machine Learning Experiments. Read more about MEX Vocabulary.
A Python library for learning and evaluating knowledge graph embeddings. Read more about pyKEEN.
An open-source platform for distributed batch data processing for RDF large-scale datasets. Read more about SANSA Stack.
A SPARQL-SQL rewriter. Read more about Sparqlify.
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-comparisons across languages as this requires data conversion processes. Read more about SML-Bench.