General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many
of them are constructed pragmatically from web sources and are thus far from complete. This …

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

Advances in information extraction have enabled the automatic construction of large
knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from …

Advances in information extraction have enabled the automatic construction of large
knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are …

During recent years, more and more data has been published as native RDF datasets. In
this setup, both the size of the datasets and the need to process aggregate queries …

Wikidata is a fast-growing, crowdsourced, and entity-centric KB that currently stores over 100
million facts about more than 21 million entities. Such a vast amount of data gives rise to the …

The Semantic Web is commonly interpreted under the open-world assumption, meaning that
information available (eg, in a data source) captures only a subset of the reality. Therefore …

Nowadays, more and more RDF data is becoming available on the Semantic Web. While the
Semantic Web is generally incomplete by nature, on certain topics, it already contains …

Recent advances in information extraction have led to the so-called knowledge graphs
(KGs), ie, huge collections of relational factual knowledge. Since KGs are automatically …

Abstract Information extraction traditionally focuses on extracting relations between
identifiable entities, such as ⟨ Monterey, locatedIn, California ⟩. Yet, texts often also contain …