Journal
ACM COMPUTING SURVEYS
Volume 54, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3447772
Keywords
Knowledge graphs; graph databases; graph query languages; shapes; ontologies; graph algorithms; embeddings; graph neural networks; rule mining
Categories
Funding
- Fondecyt [1181896]
- ANID -Millennium Science Initiative Program [ICN17_002]
- Elsevier's Discovery Lab
- European Union [860801, 731601]
- Spanish Ministry of Economy and Competitiveness [TIN2017-88877-R]
- MOUSSE ERC [726487]
- IBM Research AI through the AI Horizons Network
- German Research Foundation (DFG) [STA 572/18-1]
- Marie Curie Actions (MSCA) [860801] Funding Source: Marie Curie Actions (MSCA)
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This article provides a comprehensive introduction to knowledge graphs, covering their popularity in industry and academia, graph-based data models, query and validation languages, and techniques for knowledge representation and extraction. It also outlines high-level future research directions for knowledge graphs.
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.
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