Knowledge graph representation and reasoning (Editorial)

No Thumbnail Available
File version
Author(s)
Cambria, Erik
Ji, Shaoxiong
Pan, Shirui
Yu, Philip S
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2021
Size
File type(s)
Location
License
Abstract

Recent years have witnessed the release of many open-source and enterprise-driven knowledge graphs with a dramatic increase of applications of knowledge representation and reasoning in fields such as natural language processing, computer vision, and bioinformatics. With those large-scale knowledge graphs, recent research tends to incorporate human knowledge and imitate human’s ability of relational reasoning. Factual knowledge stored in knowledge bases or knowledge graphs can be utilized as a source for logical reasoning and, hence, be integrated to improve real-world applications.

Journal Title

Neurocomputing

Conference Title
Book Title
Edition
Volume

461

Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Neural networks

Science & Technology

Computer Science, Artificial Intelligence

Persistent link to this record
Citation

Cambria, E; Ji, S; Pan, S; Yu, PS, Knowledge graph representation and reasoning (Editorial), Neurocomputing, 2021, 461, pp. 494-496

Collections