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Published biomedical information has and continues to rapidly increase. Therecent advancements in Natural Language Processing (NLP), have generatedconsiderable interest in automating the extraction, normalization, andrepresentation of biomedical knowledge about entities such as genes anddiseases. Our study analyzes germline abstracts in the construction ofknowledge graphs of the of the immense work that has been done in this area forgenes and diseases. This paper presents SimpleGermKG, an automatic knowledgegraph construction approach that connects germline genes and diseases. For theextraction of genes and diseases, we employ BioBERT, a pre-trained BERT modelon biomedical corpora. We propose an ontology-based and rule-based algorithm tostandardize and disambiguate medical terms. For semantic relationships betweenarticles, genes, and diseases, we implemented a part-whole relation approach toconnect each entity with its data source and visualize them in a graph-basedknowledge representation. Lastly, we discuss the knowledge graph applications,limitations, and challenges to inspire the future research of germline corpora.Our knowledge graph contains 297 genes, 130 diseases, and 46,747 triples.Graph-based visualizations are used to show the results.

Applying BioBERT to Extract Germline Gene-Disease Associations for Building a Knowledge Graph from the Biomedical Literature