First international Workshop 

on 

Semantic technologies for Data Management

Knowledge Graphs (KGs) have become a popular format of data representation, mainly due to their flexible data model which renders them particularly suited to those tasks where data coming from multiple, possibly heterogeneous, sources has to be integrated in order to be fully exploited. KGs have received both the attention of academia, through foundational efforts stemming from scientific literature such as Knowledge Representation, Machine Learning, or Databases, and the enterprise world. Enterprise applications, in particular, exploit tools implementing recommendations from the Semantic Web community (such as RDF or OWL), and proprietary formats based on property graphs. The general data model of KGs allows for representing both extensional knowledge, the data itself, and intensional information made available by domain ontologies. Hence, KGs provide a way to enrich data coming from legacy sources with semantic information coming from the application domain. This empowers users with automated inference, enriches interpretability of data, and overall facilitates access and integration.