Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora

The number of biomedical articles published is increasing rapidly over the years. Currently there are about 30 million articles in PubMed and over 25 million mentions in Medline. Among these fundamentals, Biomedical Named Entity Recognition (BioNER) and Biomedical Relation Extraction (BioRE) are the most essential in analysing the literature. In the biomedical domain, Knowledge Graph is used to visualize the relationships be-tween various entities such as proteins, chemicals and diseases. Scientific publications have increased dramat-ically as a result of the search for treatments and potential cures for the new Coronavirus, but efficiently analysing, integrating, and utilising related sources of information remains a difficulty. In order to effectively combat the disease during pandemics like COVID-19, literature must be used quickly and effectively. In this paper, we introduced a fully automated framework consists of BERT-BiLSTM, Knowledge graph, and Repre-sentation Learning model to extract the top diseases, chemicals, and proteins related to COVID-19 from the literature. The proposed framework uses Named Entity Recognition models for disease recognition, chemical recognition, and protein recognition. Then the system uses the Chemical -Disease Relation Extraction and Chemical -Protein Relation Extraction models. And the system extracts the entities and relations from the CORD -19 dataset using the models. The system then creates a Knowledge Graph for the extracted relations and entities. The system performs Representation Learning on this KG to get the embeddings of all entities and get the top related diseases, chemicals, and proteins with respect to COVID-19.