deductive and inductive techniquesgraph-based data modelsknowledge graph query languagesknowledge graphsknowledge representation
Figures from the paper
Abstract (AI)
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.
Key Findings
1
Knowledge graphs are presented as a framework for exploiting diverse, dynamic, and large-scale data collections across industry and academia.
2
Knowledge representation and extraction can combine deductive and inductive techniques.
3
The article compares graph-based data models and discusses languages for querying and validating knowledge graphs.
4
The article identifies high-level future research directions for knowledge graphs.
Research Object
Knowledge graphs
Research Subject
graph-based data models, query and validation languages, and deductive and inductive knowledge representation and extraction
Publication Details
Publication Date
2021-07-02
Journal
Publisher
ISSN
Cited by
1803
Open access PDF
Access Type
Author Information
Download PDF
Subscribe to digest