Research & Innovation

Coding da Vinci is the first German open cultural data hackathon. Founded in Berlin in 2014, Coding da Vinci brings cultural heritage institutions together with the hacker & designer community to develop ideas and prototypes for the cultural sector and for the public.

Knowledge Graph completion deals with the addition of missing facts to knowledge graphs. While quite a few approaches exist for type and link prediction in knowledge graphs, the addition of literal values (also called instance or entity attributes) is not very well covered in the literature.

An organisation using personal data should document its data governance processes to maintain and demonstrate compliance with the General Data Protection Regulation (GDPR). As processes evolve, their documentation should reflect these changes with an assessment showing ongoing compliance.

Semantic Web languages such as RDF, RDFS and OWL are suitable for representing static knowledge, which enables the integration of data and information from multiple systems. With knowledge graphs, these technologies are becoming widely deployed in enterprise settings. But many applications require more than just information integration.

The concept of Linked Open Data and the promise of the Web of Data have been around for over a decade now. Yet, the great potential of free access to a broad range of data that these technologies offer has not yet been fully exploited.

The Semantic Web is potentially the massive global open knowledge base that Artificial Intelligence has been looking for since its origins. After two decades, Linked Open Data is the closest existing exemplar of such a resource. Nevertheless, LOD and similar, maybe bigger, private knowledge graphs have unlocked just a little beyond encyclopaedic question-answering.

With its focus on improving the health and well being of people, biomedicine has always been a fertile, if not challenging domain for computational discovery science.

Natural language understanding tasks are key to extracting structured and semantic information from text. One of the most chal- lenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a country.

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use different schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities.

Information associated with regulatory compliance is often siloed as legal documentation that is not suitable for querying or reuse. Utilising open standards and technologies to represent and query this information can facilitate interoperability between stakeholders and assist in the task of maintaining as well as demonstrating compliance.