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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 

The portable document format (PDF) is currently one of the most popular formats for offline sharing biomedical information. Recently, HTML-based formats for web-first biomedical information sharing have gained popularity. However, machine-interpretable information is required by literature search engines, such as Google Scholar, to index articles in a context-aware manner for accurate biomedical literature searches. The lack of technological infrastructure to add machine-interpretable metadata to expanding biomedical information, on the other hand, renders them unreachable to search engines. Therefore, we developed a portable technical infrastructure (goSemantically) and packaged it as a Google Docs add-ons. The “goSemantically” assists authors in adding machine-interpretable metadata at the terminology and document structural levels While authoring biomedical content. The “goSemantically” leverages the NCBO Bioportal resources and introduces a mechanism to annotate biomedical information with relevant machine-interpretable metadata (semantic vocabularies). The “goSemantically” also acquires schema.org meta tags designed for search engine optimization and tailored to accommodate biomedical information. Thus, individual authors can conveniently author and publish biomedical content in a truly decentralized fashion. Users can also export and host content with relevant machine-interpretable metadata (semantic vocabularies) in interoperable formats such as HTML and JSON-LD. To experience the described features, run this code with Google Doc