Disambiguation of medical abbreviations for knowledge organization |
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| Institution: | 1. School of Information Management, Nanjing University, Nanjing 210023, PR China;2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210093, PR China;3. Science, Mathematics and Technology, Singapore University of Technology and Design, 487372, Singapore;1. College of Artificial Intelligence, Beijing Information Technology College, Beijing, 100018, China;2. College of Engineering and IT University of Dubai, UAE;3. Independent Researcher, USA;4. Department of Computer Science, College of Computer and Information Sciences, Majmaah University. Al-Majmaah, 11952, Saudi Arabia;5. Department of Electrical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia;1. School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Department of Economics, University of Reading, Reading RG6 6UD, UK;1. College of Economics, Shenzhen University, Shenzhen, Guangdong 518060, China;2. School of Management, Huazhong University of Science and Technology, Wuhan 430074, China;1. School of Information Management, Nanjing University, Nanjing 210023, China;2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China |
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| Abstract: | The disambiguation of abbreviations is a crucial step in medical knowledge organization. In the past, most scholars have focused on the problem of disambiguating medical abbreviations in single sentences; they have not systematically considered full-article abbreviation disambiguation tasks. In this work, we present a research framework for full-article medical abbreviation disambiguation (FMADRF) based on the structural characteristics of abbreviation–definition pairs in a full scientific medical article. Our method utilizes the information including context semantic information, external linguistic features, and the mapping relationships and structural similarities between abbreviations and their expansions. The model includes a four-pronged approach, identification of abbreviations and abbreviation–definition pairs, alignment and complementation of abbreviations and abbreviation expansions. The results show that our novel BBF-BLC-R model improves the recognition and modification effects of abbreviation–definition pairs, achieving the best F1 score of 91.83%. Furthermore, our new strategy combines semantic and structural information to significantly improve the effects of term alignment, with an F1 score of 97.11%. In our test, a thesaurus of abbreviations and their expansions was constructed from 13,472 full-text medical articles, resulting in 14,742 abbreviations, with 31,327 corresponding expansions. This work enhances the semantic association of terms in full medical texts, eliminating the problems of “rich” semantics and association–relation roadblocks caused by term misalignments. It further provides technical and methodological support for the organization of medical knowledge, facilitating the deep knowledge-mining capabilities of full-text medical articles. |
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