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There are huge on-going challenges to timely access of accurate online biomedical content due to exponential growth of unstructured biomedical data. Therefore, semantic annotations are essentially required with the biomedical content in order to improve search engines’ context-aware indexing, search efficiency, and precision of the retrieved results. In this study, we propose a personalized semantic annotation recommendations approach to biomedical content through an expanded socio-technical approach. Our layered architecture generates annotations on the users’ entered text in the first layer. To optimize the yielded annotations, users can seek help from professional experts by posing specific questions to them. The socio-technical system also connects help seekers (users) to help providers (experts) employing the pre-trained BERT embedding, which matches the profile similarity scores of users and experts at various levels and suggests a run-time compatible match (of the help seeker and the help provider). Our approach overcomes previous systems’ limitations as they are predominantly non-collaborative and laborious. While performing experiments, we analyzed the performance enhancements offered by our socio-technical approach in improving the semantic annotations in three scenarios in various contexts. Our results show overall achievement of 89.98% precision, 89.61% recall, and an 89.45% f1-score at the system level. Comparatively speaking, a high accuracy of 90% was achieved with the socio-technical approach whereas the traditional approach could only reach 87% accuracy. Our novel socio-technical approach produces apt annotation recommendations that would definitely be helpful for various secondary uses ranging from context-aware indexing to retrieval accuracy improvements.