We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We discuss the roles of schema, identity, and context in knowledge graphs. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. Antoine Zimmermann, École des mines de Saint-Étienne, France.Steffen Staab, Universität Stuttgart, Germany and University of Southampton, UK.Lukas Schmelzeisen, Universität Stuttgart, Germany.Anisa Rula, University of Milano-Bicocca, Italy and University of Bonn, Germany.Rashid, Tetherless World Constellation, Rensselaer Polytechnic Institute, USA Axel-Cyrille Ngonga Ngomo, DICE, Universität Paderborn, Germany.Roberto Navigli, Sapienza University of Rome, Italy.José Emilio Labra Gayo, Universidad de Oviedo, Spain.Claudio Gutierrez, IMFD, DCC, Universidad de Chile, Chile.Gerard de Melo, Rutgers University, USA.Claudia d’Amato, University of Bari, Italy.Michael Cochez, Vrije Universiteit and Discovery Lab, Elsevier, The Netherlands.Eva Blomqvist, Linköping University, Sweden.
0 Comments
Leave a Reply. |