Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach
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...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133253 |
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| author | Asim Abbas Mutahira Khalid Sebastian Chalarca Fazel Keshtkar Syed Ahmad Chan Bukhari |
| author_facet | Asim Abbas Mutahira Khalid Sebastian Chalarca Fazel Keshtkar Syed Ahmad Chan Bukhari |
| author_sort | Asim Abbas |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-a24481a7cdf44bcc9b4a45b66f7c5a28 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-a24481a7cdf44bcc9b4a45b66f7c5a282025-08-20T01:52:19ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13325369559Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based ApproachAsim Abbas0Mutahira Khalid1Sebastian Chalarca2Fazel Keshtkar3https://orcid.org/0000-0002-7022-6238Syed Ahmad Chan Bukhari4https://orcid.org/0000-0002-6517-5261St. Johns UniversitySchool of Electrical Engineering and Computer Science, NUST, H-12, Islamabad, PakistanSt. Johns UniversitySt. John's UniversitySt. John's UniversityAn 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/Semanticallyhttps://journals.flvc.org/FLAIRS/article/view/133253semantic annotationsemantic knowledge graphannotation recommendationannotation rankingpeer-to-peer recommendations |
| spellingShingle | Asim Abbas Mutahira Khalid Sebastian Chalarca Fazel Keshtkar Syed Ahmad Chan Bukhari Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach Proceedings of the International Florida Artificial Intelligence Research Society Conference semantic annotation semantic knowledge graph annotation recommendation annotation ranking peer-to-peer recommendations |
| title | Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach |
| title_full | Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach |
| title_fullStr | Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach |
| title_full_unstemmed | Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach |
| title_short | Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach |
| title_sort | enhancing biomedical semantic annotations through a knowledge graph based approach |
| topic | semantic annotation semantic knowledge graph annotation recommendation annotation ranking peer-to-peer recommendations |
| url | https://journals.flvc.org/FLAIRS/article/view/133253 |
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