Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach

An abundance of biomedical data is generated in the form clinical notes, reports, and research articles available online. This holds valuable information that requires extraction, retrieval, transformation into actionable knowledge. However, this has various access challenges due to need for precise machine-interpretable semantic metadata required by search engines. Despite engines' efforts interpret semantics information, they still struggle index, search, retrieve relevant accurately. To address these challenges, we propose a novel graph-based knowledge-sharing approach enhance quality annotation engaging domain experts. In approach, entities environment are interlinked play critical roles. Authorial queries can be posted on "Knowledge Cafe," community experts provide recommendations annotations. The further validate evaluate expert responses through voting scheme resulting transformed Cafe" functions as knowledge graph with semantically linked entities. We evaluated proposed series scenarios, precision, recall, F1-score, accuracy assessment matrices. Our results showed an acceptable level at approximately 90%. source code "Semantically" freely at: https://github.com/bukharilab/Semantically