KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories
We present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-sourc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/14/11/1411 |
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| author | Muzzamil Aziz Ioana Popa Amjad Zia Andreas Fischer Sabih Ahmed Khan Amirreza Fazely Hamedani Abdul R. Asif |
| author_facet | Muzzamil Aziz Ioana Popa Amjad Zia Andreas Fischer Sabih Ahmed Khan Amirreza Fazely Hamedani Abdul R. Asif |
| author_sort | Muzzamil Aziz |
| collection | DOAJ |
| description | We present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-source machine learning tools, such as GROBID, S2ORC, and BioC to streamline the processes of data extraction and data mining. Central to the functionality of KnowVID-19 is its keyword-based text classification process, which plays a pivotal role in organizing and categorizing the extracted information. By employing machine learning techniques for keyword extraction—specifically RAKE, YAKE, and KeyBERT—KnowVID-19 systematically categorizes publication data into distinct topics and subtopics. This topic structuring enhances the system’s ability to match user queries with relevant research, improving both the accuracy and efficiency of the search results. In addition, KnowVID-19 leverages the NetworkX Python library to construct networks of the most relevant terms within publications. These networks are then visualized using Cytoscape software, providing a graphical representation of the relationships between key terms. This network visualization allows researchers to easily track emerging trends and developments related to COVID-19, long COVID, and associated topics, facilitating more informed and user-centered exploration of the scientific literature. KnowVID-19 also provides an interactive web application with an intuitive, user-centered interface. This platform supports seamless keyword searching and filtering, as well as a visual network of term associations to help users quickly identify emerging research trends. The responsive design and network visualization enables efficient navigation and access to targeted COVID-19 literature, enhancing both the user experience and the accuracy of data-driven insights. |
| format | Article |
| id | doaj-art-a9b3b165ff624d4fbda79da79ac5fdc5 |
| institution | Kabale University |
| issn | 2218-273X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-a9b3b165ff624d4fbda79da79ac5fdc52024-11-26T17:54:11ZengMDPI AGBiomolecules2218-273X2024-11-011411141110.3390/biom14111411KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical RepositoriesMuzzamil Aziz0Ioana Popa1Amjad Zia2Andreas Fischer3Sabih Ahmed Khan4Amirreza Fazely Hamedani5Abdul R. Asif6Future Networks, eScience Group, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), 37077 Göttingen, GermanyInstitute for Clinical Chemistry, University Medical Center Göttingen, George-August-University, 37073 Göttingen, GermanyInstitute for Clinical Chemistry, University Medical Center Göttingen, George-August-University, 37073 Göttingen, GermanyInstitute for Clinical Chemistry, University Medical Center Göttingen, George-August-University, 37073 Göttingen, GermanyFuture Networks, eScience Group, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), 37077 Göttingen, GermanyFuture Networks, eScience Group, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), 37077 Göttingen, GermanyInstitute for Clinical Chemistry, University Medical Center Göttingen, George-August-University, 37073 Göttingen, GermanyWe present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-source machine learning tools, such as GROBID, S2ORC, and BioC to streamline the processes of data extraction and data mining. Central to the functionality of KnowVID-19 is its keyword-based text classification process, which plays a pivotal role in organizing and categorizing the extracted information. By employing machine learning techniques for keyword extraction—specifically RAKE, YAKE, and KeyBERT—KnowVID-19 systematically categorizes publication data into distinct topics and subtopics. This topic structuring enhances the system’s ability to match user queries with relevant research, improving both the accuracy and efficiency of the search results. In addition, KnowVID-19 leverages the NetworkX Python library to construct networks of the most relevant terms within publications. These networks are then visualized using Cytoscape software, providing a graphical representation of the relationships between key terms. This network visualization allows researchers to easily track emerging trends and developments related to COVID-19, long COVID, and associated topics, facilitating more informed and user-centered exploration of the scientific literature. KnowVID-19 also provides an interactive web application with an intuitive, user-centered interface. This platform supports seamless keyword searching and filtering, as well as a visual network of term associations to help users quickly identify emerging research trends. The responsive design and network visualization enables efficient navigation and access to targeted COVID-19 literature, enhancing both the user experience and the accuracy of data-driven insights.https://www.mdpi.com/2218-273X/14/11/1411knowledge-based systemnatural language processingweb crawling and scrapingCOVID-19long COVIDartificial intelligence |
| spellingShingle | Muzzamil Aziz Ioana Popa Amjad Zia Andreas Fischer Sabih Ahmed Khan Amirreza Fazely Hamedani Abdul R. Asif KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories Biomolecules knowledge-based system natural language processing web crawling and scraping COVID-19 long COVID artificial intelligence |
| title | KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories |
| title_full | KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories |
| title_fullStr | KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories |
| title_full_unstemmed | KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories |
| title_short | KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories |
| title_sort | knowvid 19 a knowledge based system to extract targeted covid 19 information from online medical repositories |
| topic | knowledge-based system natural language processing web crawling and scraping COVID-19 long COVID artificial intelligence |
| url | https://www.mdpi.com/2218-273X/14/11/1411 |
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