Proficient Annotation Recommendation in a Biomedical Content Authoring Environment

Abbas, Asim; Mbouadeu, Steve Fonin; Bisram, Avinash; Iqbal, Nadeem; Keshtkar, Fazel; Bukhari, Syed Ahmad Chan

doi:10.1007/978-3-031-21422-6_11

Cited by 2 publications

(1 citation statement)

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“…Timely information transfer from the scientific research community to peer investigators and other healthcare professionals entails efficient techniques for acquiring biomedical publications. This continued increase in the biomedical field has led to various access-level challenges for practitioners and researchers [Abbas A 2022]. Due to the lack of machine-interpretable meta-data, this indispensable information in the contents on the web is still concealed from information retrieval and knowledge extraction search engines.Search engines, on the other hand, require metadata to properly index the content in a context-aware manner and to support ancillary operations like automated integration for meta-analysis [S. A. C. Bukhari 2017].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach

Abbas,

Khalid,

Chalarca

et al. 2023

FLAIRS

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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

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Section: Introductionmentioning

confidence: 99%