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Knowledge graphs

Knowledge graphs
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. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.
Hogan, Aidan
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Blomqvist, Eva
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Cochez, Michael
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d'Amato, Claudia
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de Melo, Gerard
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Gutierrez, Claudio
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Labra Gayo, Jose Emilio
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Kirrane, Sabrina
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Neumaier, Sebastian
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Polleres, Axel
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Navigli, Roberto
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Ngonga Ngomo, Axel-Cyrille
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Rashid, Sabbir M.
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Rula, Anisa
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Schmelzeisen, Lukas
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Sequeda, Juan
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Staab, Steffen
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Zimmermann, Antoine
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Hogan, Aidan
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Blomqvist, Eva
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Cochez, Michael
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d'Amato, Claudia
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de Melo, Gerard
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Gutierrez, Claudio
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Labra Gayo, Jose Emilio
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Kirrane, Sabrina
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Neumaier, Sebastian
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Polleres, Axel
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Navigli, Roberto
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Ngonga Ngomo, Axel-Cyrille
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Rashid, Sabbir M.
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Rula, Anisa
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Schmelzeisen, Lukas
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Sequeda, Juan
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Staab, Steffen
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Zimmermann, Antoine
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Hogan, Aidan, Blomqvist, Eva, Cochez, Michael, d'Amato, Claudia, de Melo, Gerard, Gutierrez, Claudio, Labra Gayo, Jose Emilio, Kirrane, Sabrina, Neumaier, Sebastian, Polleres, Axel, Navigli, Roberto, Ngonga Ngomo, Axel-Cyrille, Rashid, Sabbir M., Rula, Anisa, Schmelzeisen, Lukas, Sequeda, Juan, Staab, Steffen and Zimmermann, Antoine (2020) Knowledge graphs 131pp.

Record type: Monograph (Project Report)

Abstract

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. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

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knowledge graphs - Author's Original
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Published date: 4 March 2020

Identifiers

Local EPrints ID: 438786
URI: http://eprints.soton.ac.uk/id/eprint/438786
PURE UUID: 2ef93c26-257a-4118-84d1-9e57e42e0ae6
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

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Date deposited: 24 Mar 2020 17:31
Last modified: 15 Jul 2020 00:35

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Contributors

Author: Aidan Hogan
Author: Eva Blomqvist
Author: Michael Cochez
Author: Claudia d'Amato
Author: Gerard de Melo
Author: Claudio Gutierrez
Author: Jose Emilio Labra Gayo
Author: Sabrina Kirrane
Author: Sebastian Neumaier
Author: Axel Polleres
Author: Roberto Navigli
Author: Axel-Cyrille Ngonga Ngomo
Author: Sabbir M. Rashid
Author: Anisa Rula
Author: Lukas Schmelzeisen
Author: Juan Sequeda
Author: Steffen Staab ORCID iD
Author: Antoine Zimmermann

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