earticle

영어

Knowledge graphs (KGs) play a pivotal role in modern applications such as decision-making systems, question answering systems, and searching and retrieval systems. However, the automatic construction of a knowledge graph from unstructured text is a challenging task. Moreover, traditional dictionary-, rule-based and supervised machine learning approaches are not reasonably practical due to their dependency on human-expert annotated resources. It is especially true when a knowledge graph is generated from domain-specific information, updated frequently, such as COVID-19 related information resources. This paper uses a pre-trained embedding model (BERT) to create word vectors from COVID-19 research articles. The proposed model is employed at two levels: entity extraction from the text and querying the knowledge stored in KG.