A survey on state-of-the-art techniques for knowledge graphs construction and challenges ahead
Document Type
Conference Proceeding
Publication Title
2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
Publisher
IEEE
School
School of Science
RAS ID
45048
Abstract
Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251. However, most of the content is unstructured and is not understandable by machines. Structuring this data into a knowledge graph enables intelligent applications such as deep question answering, recommendation systems, semantic search, etc. The knowledge graph is an emerging technology that allows logical reasoning and uncovers new insights using content and context. Thereby, it provides necessary syntax and reasoning semantics that enable machines to solve complex healthcare, security, financial institutions, economics, and business problems. As an outcome, enterprises are putting their effort into constructing and maintaining knowledge graphs to support various downstream applications. Manual approaches are too expensive. Automated schemes can reduce the cost of building knowledge graphs up to 15–250 times. This paper critiques state-of-the-art automated techniques to produce knowledge graphs of near-human quality autonomously. Additionally, it highlights different research issues that need to be addressed to deliver high-quality knowledge graphs.
DOI
10.1109/AIKE52691.2021.00021
Access Rights
subscription content
Comments
Hur, A., Janjua, N., & Ahmed, M. (2021). A survey on state-of-the-art techniques for knowledge graphs construction and challenges ahead. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 99-103). IEEE. https://doi.org/10.1109/AIKE52691.2021.00021