Knowledge Graphs
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 some opening remarks, 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.
Cite
@article{arxiv.2003.02320,
title = {Knowledge Graphs},
author = {Aidan Hogan and Eva Blomqvist and Michael Cochez and Claudia d'Amato and Gerard de Melo and Claudio Gutierrez and José Emilio Labra Gayo and Sabrina Kirrane and Sebastian Neumaier and Axel Polleres and Roberto Navigli and Axel-Cyrille Ngonga Ngomo and Sabbir M. Rashid and Anisa Rula and Lukas Schmelzeisen and Juan Sequeda and Steffen Staab and Antoine Zimmermann},
journal= {arXiv preprint arXiv:2003.02320},
year = {2021}
}
Comments
Revision from v5: Correcting errata from previous version for entailment/models, and some other minor typos