2022
DOI: 10.1002/aaai.12033
|View full text |Cite
|
Sign up to set email alerts
|

Knowledge graphs: Introduction, history, and perspectives

Abstract: Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b)… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
7
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
2

Relationship

0
7

Authors

Journals

citations
Cited by 15 publications
(12 citation statements)
references
References 62 publications
0
7
0
Order By: Relevance
“…Only when we are explicit and transparent about our causal assumptions, will we be able to have constructive discussions and productive tensions that improve our thinking and science. There are several graphic tools that may help us with this endeavor (such as knowledge graphs 8 and directed acyclic graphs 25,78 ). It might be time to have a conversation about whether such visualizations of our causal assumptions should become a necessary ingredient of research and publications.…”
Section: Discussionmentioning
confidence: 99%
“…Only when we are explicit and transparent about our causal assumptions, will we be able to have constructive discussions and productive tensions that improve our thinking and science. There are several graphic tools that may help us with this endeavor (such as knowledge graphs 8 and directed acyclic graphs 25,78 ). It might be time to have a conversation about whether such visualizations of our causal assumptions should become a necessary ingredient of research and publications.…”
Section: Discussionmentioning
confidence: 99%
“…Knowledge graphs have drawn a lot of interest (Ehrlinger and Wöß 2016;Hogan et al 2021) because of their ability to represent complex information in a structured and interconnected format (Chaudhri et al 2022). Their inherent nature of capturing and modelling relationships among entities allows for meaningful analyses (Angles et al 2017) and reasoning (Hogan et al 2021).…”
Section: Related Work and Research Objectivementioning
confidence: 99%
“…Open knowledge graphs draw their underlying data from publicly available sources. Proprietary knowledge graphs, on the other hand, obtain data from non-public, enterprise internal bases (Chaudhri et al 2022).…”
Section: Related Work and Research Objectivementioning
confidence: 99%
“…Then, the plausibility of the facts is maximized to obtain the entity and relation embeddings (Chaudhri et al. 2022 ; Sun et al. 2022 ).…”
Section: Technical Challengesmentioning
confidence: 99%