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Knowledge Graphs

Knowledge Graphs

11 Sep 2021 | AIDAN HOGAN, IMFD, DCC, Universidad de Chile, Chile EVA BLOMQVIST, Linköping University, Sweden MICHAEL COCZEZ, Vrije Universiteit and Discovery Lab, Elsevier, The Netherlands CLAUDIA D'AMATO, University of Bari, Italy GERARD DE MELO, HPI, Germany and Rutgers University, USA CLAUDIO GUTIERREZ, IMFD, DCC, Universidad de Chile, Chile JOSÉ EMILIO LABRA GAYO, Universidad de Oviedo, Spain SABRINA KIRRANE, SEBASTIAN NEUMAIER, and AXEL POLLERES, WU Vienna, Austria ROBERTO NAVIGLI, Sapienza University of Rome, Italy AXEL-CYRILLE NGONGA NGOMO, DICE, Universität Paderborn, Germany SABBIR M. RASHID, Tetherless World Constellation, Rensselaer Polytechnic Institute, USA ANISA RULA, University of Milano–Bicocca, Italy and University of Bonn, Germany LUKAS SCHMELZEISEN, Universität Stuttgart, Germany JUAN SEQUEDA, data.world, USA STEFFEN STAAB, Universität Stuttgart, Germany and University of Southampton, UK ANTOINE ZIMMERMANN, École des mines de Saint-Étienne, France
This paper provides a comprehensive introduction to knowledge graphs, which have gained significant attention from both industry and academia due to their ability to integrate and manage diverse, dynamic, large-scale data collections. The authors motivate and contrast various graph-based data models and query languages used for knowledge graphs, discuss the roles of schema, identity, and context, and explain how knowledge can be represented and extracted using deductive and inductive techniques. They also cover methods for creating, enriching, assessing quality, refining, and publishing knowledge graphs. The paper includes an overview of prominent open and enterprise knowledge graphs, their applications, and future research directions. The goal is to provide a broad and accessible introduction to knowledge graphs for researchers and practitioners new to the field. The paper is structured into several sections, covering data models, schema, identity, context, deductive and inductive techniques, creation and enrichment, quality assessment, refinement, publication, and an overview of existing knowledge graphs.This paper provides a comprehensive introduction to knowledge graphs, which have gained significant attention from both industry and academia due to their ability to integrate and manage diverse, dynamic, large-scale data collections. The authors motivate and contrast various graph-based data models and query languages used for knowledge graphs, discuss the roles of schema, identity, and context, and explain how knowledge can be represented and extracted using deductive and inductive techniques. They also cover methods for creating, enriching, assessing quality, refining, and publishing knowledge graphs. The paper includes an overview of prominent open and enterprise knowledge graphs, their applications, and future research directions. The goal is to provide a broad and accessible introduction to knowledge graphs for researchers and practitioners new to the field. The paper is structured into several sections, covering data models, schema, identity, context, deductive and inductive techniques, creation and enrichment, quality assessment, refinement, publication, and an overview of existing knowledge graphs.
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[slides and audio] Knowledge Graphs