The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to
indicate its fitness for use in an application. Several quality dimensions are identified, such …

Launched in 2012, Wikidata has already become a success story. It is a collaborative
knowledge graph, whose large community has produced so far data about more than 55 …

Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts.
In some knowledge bases, the correctness of these facts has been evaluated. However …

Abstract Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts. They
are widely used in entity recognition, structured search, question answering, and other …

Information related to the COVID-19 pandemic ranges from biological to bibliographic, from
geographical to genetic and beyond. The structure of the raw data is highly complex, so …

Content gaps in knowledge graphs impact downstream applications. Semantic Web
researchers have studied them mainly in relation to data quality or ontology evaluation, for …

Abstract Knowledge bases such as Wikidata, DBpedia, YAGO, or the Google Knowledge
Vault collect a vast number of facts about the world. But while quite some facts are known …

An attribute is obligatory for a class in a Knowledge Base (KB), if all instances of the class
have the attribute in the real world. For example, hasBirthDate is an obligatory attribute for …

Abstract Knowledge bases (KBs) such as DBpedia, Wikidata, and YAGO contain a huge
number of entities and facts. Several recent works induce rules or calculate statistics on …

Crowdsourced knowledge bases like Wikidata suffer from low-quality edits and vandalism,
employing machine learning-based approaches to detect both kinds of damage. We reveal …