Knowledge Graphs

Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and de Melo, Gerard and Gutierrez, Claudio and Kirrane, Sabrina ORCID: https://orcid.org/0000-0002-6955-7718 and Labra Gayo, José Emilio and Navigli, Roberto and Neumaier, Sebastian and Ngonga Ngomo, Axel-Cyrille and Polleres, Axel and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine (2021) Knowledge Graphs. ACM Computing Surveys, 54 (4). pp. 1-37. ISSN 0360-0300

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Abstract

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 require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

Item Type: Article
Additional Information: © 2021 Copyright held by the owner/author(s).
Keywords: Knowledge graphs, graph databases, graph query languages, shapes, ontologies, graph algorithms, embeddings, graph neural networks, rule mining
Divisions: Departments > Wirtschaftsinformatik u. Operations Mgmt > Wirtschaftsinformatik und Neue Medien
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 16 Jul 2021 15:18
Last Modified: 16 Jul 2021 15:18
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/100677/
URI: https://epub.wu.ac.at/id/eprint/8214

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