Keywords
Text Mining
Entity Recognition
How to Cite
Abstract
Knowledge graphs (KGs) have emerged as powerful tools for representing structured knowledge in a machine-readable format. Constructing KGs from unstructured text data is a challenging yet crucial task, as it enables machines to understand and reason over vast amounts of information. This paper provides an overview of techniques and approaches for constructing knowledge graphs from text, focusing on methods that extract entities, relationships, and concepts from textual data. We discuss various aspects of KG construction, including entity recognition, relationship extraction, and knowledge fusion. Additionally, we explore the applications and challenges of using knowledge graphs constructed from text data.
References
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