Making the vision of a reliable Web of Data become a reality (RELWEB)

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The World Wide Web is one of the most influential inventions that have radically changed our lives. One part of it, however, is designed for machines and provides access to vast amounts of data: the Web of Data – a building block of the Semantic Web. Although the information on the Web of Data is freely available and covers a broad range of topics, it remains mostly unexploited. The goal of this project is to develop technologies that overcome current challenges and establish a reliable Web of Data.

• Funding: The project is funded by the Danish Council for Independent Research (DFF)
• Project period: 2019-2024
   

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Digitally supported Environmental  Assessment for Sustainable Development Goals (DREAMS)

The vision of this project is to promote progress on Sustainable Development Goals (SDGs) by digitally transforming the way society accesses and communicates information about environmental impacts of projects and plans, in order to enable the best  decisions towards green transition in a transparent and inclusive democratic process.

• Partners:15 Danish partners 
• Funding: The project is funded by Innovation Fund Denmark
• Project period: 2020-2023
   

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Open Data for Sustainability Assessment (ODA)

Sustainability Assessment in the form of Life Cycle Assessment (LCA) is essential for European research and technology development, SDG-assessment, but existing data is difficult to establish and obsolete quickly.

The ODA project creates and demonstrates the concept for a smart platform for collecting and organizing data in a multidisciplinary collaboration between computer science researchers, sustainability researchers at AAU, and in cooperation with an international network of experts.

• Partners: Department of Computer Science and Department of Planning, Aalborg University
• Project period: 2019-2022

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Data Science Meets Microbial Dark Matter

Bacteria are present everywhere. They can make us ill. They can keep us healthy. In other words, bacteria have an endless range of applications. But if we are to utilize this potential to the full, we need to know a much larger part of the genome of bacteria. And therein lies the catch: Today, we know the genome of far less than one percent of bacteria in nature. 

In this project researchers pool their knowledge on DNA sequencing, graph analysis and machine learning for mapping more bacterial genomes faster.

• Partners: Department of Chemistry and Bioscience and the Department of Computer Science, Aalborg University 
• Funding: The project is funded by the VILLUM FOUNDATION 
• Project period: 2021-2022

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Example-Driven Analytics of Open Knowledge Graphs (EDAO) 

The Linked Open Data cloud (LOD) contains a very rich corpus of information that requires dedicated business analytics and information extractions technologies for the elicitation of valuable insights. Yet, to access this data and perform such analysis, the typical gateways are specialized query languages that are usually challenging to use to non-expert users. This constitutes a major impediment in the successful exploitation of Linked Open Data.

To support advanced LOD analytics, this project proposes a novel data exploration system which allows users to extract insights within complex and unfamiliar datasets.

• Funding: The project is funded by the European Commission Union's Horizon 2020
• Project period: 2019-2021

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