In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field
concerned with deriving higher-level insights from data. The tasks performed in that field are …

Smart speakers such as Alexa and Google Home introduced Artificial Intelligence (AI) in
millions soon billions of households, making AI an everyday experience. We can now look …

In recent years, several noteworthy large, cross-domain, and openly available knowledge
graphs (KGs) have been created. These include DBpedia, Freebase, OpenCyc, Wikidata …

The development and standardization of Semantic Web technologies has resulted in an
unprecedented volume of data being published on the Web as Linked Data (LD). However …

Knowledge graphs have, for the past decade, been a hot topic both in public and private
domains, typically used for large-scale integration and analysis of data using graph-based …

A knowledge graph (KG), a special form of semantic network, integrates fragmentary data
into a graph to support knowledge processing and reasoning. KG quality control is important …

Abstract With Linked Data, a very pragmatic approach towards achieving the vision of the
Semantic Web has gained some traction in the last years. The term Linked Data refers to a …

A large number of published datasets (or sources) that follow Linked Data principles is
currently available and this number grows rapidly. However, the major target of Linked Data …

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 …