Unveiling Breast Cancer Causes Through Knowledge Graph Analysis and BioBERT-Based Factuality Prediction

Hasna El Haji, Nada Sbihi, Kaoutar El Handri, Adil Bahaj, Mohammed Elkasri, Amine Souadka, Mounir Ghogho

Abstract: Worldwide, millions of women are affected by breast cancer, with the impact significantly worsened in under-served regions. The profound effect of breast cancer on women’s health has driven research into its causes, with the aim of developing methods for the prevention, diagnosis, and treatment of the disease. The significant influx of research on this subject is overwhelming and makes manual exploration arduous, which motivates automated knowledge exploration approaches. Knowledge Graphs (KGs) are one of these approaches that attracted significant attention in the last few years for their ability to structure and present knowledge, making it easier to explore and analyze. Current KGs that include causes of breast cancer are deficient in contextual information, highlighting the uncertainty of these causes (facts). In this work, we present a method for extracting a sub-graph of breast cancer causes and fine-tuning BioBERT to evaluate the uncertainty of these causes. Our automated appr