Research & Innovation

This paper describes a new semantic metadata-based approach to describing and integrating diverse data processing activity descriptions gathered from heteroge-neous organisational sources such as departments, divisions, and external proces-sors. This information must be collated to assess and document GDPR legal compliance, such as creating a Register of Processing Activities (ROPA).

Knowledge graphs facilitate systematic large-scale data analysis by providing both human and machine-readable structures, which can be shared across different domains and platforms. Nowadays, knowledge graphs can be used to standardise the collection and sharing of user information in many different sectors such as transport, insurance, smart cities and Internet of Things (IoT).

Matching tables against Knowledge Graphs is a crucial task in many applications. A widely adopted solution to improve the precision of matching algorithms is to refine the set of candidate entities by their type in the Knowledge Graph. However, it is not rare that a type is missing for a given entity.

Social media as infrastructure for public discourse provide valuable information that needs to be preserved. Several tools for social media harvesting exist, but still only fragmented workflows may be formed with different combinations of such tools. On top of that, social media data but also preservation-related metadata standards are heterogeneous, resulting in a costly manual process.

With the significant growth in RDF datasets, application developers demand their online availability to meet the end users' expectations. Various interfaces are available for querying RDF data using SPARQL query language. Studies show that SPARQL endpoints may provide high query runtime performance at the cost of low availability.

The results of a SPARQL query are generally presented as a table with one row per result, and one column per projected variable. This is an immediate consequence of the formal definition of SPARQL results as a sequence of mappings from variables to RDF terms. However, because of the flat structure of tables, some of the RDF graph structure is lost.

The Semantic Web research community understood since its beginning how crucial it is to equip practitioners with methods to transform non-RDF resources into RDF. Proposals focus on either engineering content transformations or accessing non-RDF resources with SPARQL. Existing solutions require users to learn specific mapping languages (e.g.

Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning.

Nowadays, the demand in industry of dialogue systems to be able to naturally communicate with industrial systems is increasing, as they allow to enhance productivity and security in these scenarios. However, adapting these systems to different use cases is a costly process, due to the complexity of the scenarios and the lack of available data.

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.