A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead
Ali Hur, Naeem Janjua, Mohiuddin Ahmed

TL;DR
This survey reviews current automated methods for constructing knowledge graphs, emphasizing their importance in enabling intelligent applications and discussing challenges to achieve near-human quality in knowledge graph generation.
Contribution
It provides a comprehensive critique of state-of-the-art automated techniques for knowledge graph construction and highlights key research challenges ahead.
Findings
Automated schemes can reduce construction costs significantly.
Current techniques aim for near-human quality in knowledge graphs.
Several research issues remain to be addressed for high-quality knowledge graphs.
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 multitudes of 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 along with the 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…
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Taxonomy
TopicsData Quality and Management · Advanced Graph Neural Networks · Artificial Intelligence in Healthcare
