In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19 and biomedical. We build a heterogeneous entity-based knowledge graph network, where edges are shared between biomedical entities and paper names, where entities appear in abstract of the paper. The biomedical entities are derived from the abstract of the scientific articles using a fine-tuned Bio-BERT model. For a user query, entities are derived using a fine-tuned Bio-BERT model and then semantic similarity to query is employed for the return of the top-most relevant papers on the titles. We also provide a small set of results for the information retrieval system. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Entity-Based Knowledge Graph Information Retrieval for Biomedical Articles