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

Each day a vast amount of unstructured content is generated in the biomedical domain from various sources such as clinical notes, research articles and medical reports. Such content contain a sufficient amount of efficient and meaningful information that needs to be converted into actionable knowledge for secondary use. However, accessing precise biomedical content is quite challenging because of content heterogeneity, missing and imprecise metadata and unavailability of associated semantic tags required for search engine optimization. We have introduced a socio-technical semantic annotation optimization approach that enhance the semantic search of biomedical contents. The proposed approach consist of layered architecture. At First layer (Preliminary Semantic Enrichment), it annotates the biomedical contents with the ontological concepts from NCBO BioPortal. With the growing biomedical information, the suggested semantic annotations from NCBO Bioportal are not always correct. Therefore, in the second layer (Optimizing the Enriched Semantic Information), we introduce a knowledge sharing scheme through which authors/users could request for recommendations from other users to optimize the semantic enrichment process. To guage the credibility of the the human recommended, our systems records the recommender confidence score, collects community voting against previous recommendations, stores percentage of correctly suggested annotation and translates that into an index to later connect right users to get suggestions to optimize the semantic enrichment of biomedical contents. At the preliminary layer of annotation from NCBO, we analyzed the n-gram strategy for biomedical word boundary identification. We have found that NCBO recognizes biomedical terms for n-gram-1 more than for n-gram-2 to n-gram-5. Similarly, a statistical measure conducted on significant features using the Wilson score and data normalization. In contrast, the proposed methodology achieves an suitable accuracy of ≈90% for the semantic optimization approach.