Head Concepts Selection for Verbose Medical Queries Expansion
Semantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. How...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9064784/ |
| _version_ | 1818910302734909440 |
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| author | Mohammed Maree Israa Noor Khaled S. Rabayah Mohammed Belkhatir Saadat M. Alhashmi |
| author_facet | Mohammed Maree Israa Noor Khaled S. Rabayah Mohammed Belkhatir Saadat M. Alhashmi |
| author_sort | Mohammed Maree |
| collection | DOAJ |
| description | Semantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. However, there are challenges that hinder their widespread use in real-world applications. Among these challenges is the insufficient knowledge individually encoded in existing medical ontologies, which is magnified when users express their information needs using long-winded natural language queries. In this context, many of the users' query terms are either unrecognized by the used ontologies, or cause retrieving false positives that degrade the quality of current medical information search approaches. In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically-enhanced inverted index documents, and iii) contribute to a heuristical weighting technique in the query-document matching process. To demonstrate the effectiveness of the proposed approach, we conducted several experiments over the CLEF e-Health 2014 dataset. Findings indicate that the proposed method combining several extrinsic semantic resources proved to be more effective than related approaches in terms of precision measure. |
| first_indexed | 2024-12-19T22:40:39Z |
| format | Article |
| id | doaj.art-27aa956e18304d7583bd398818780320 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:40:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-27aa956e18304d7583bd3988187803202022-12-21T20:03:05ZengIEEEIEEE Access2169-35362020-01-018939879399910.1109/ACCESS.2020.29875689064784Head Concepts Selection for Verbose Medical Queries ExpansionMohammed Maree0https://orcid.org/0000-0002-6114-4687Israa Noor1Khaled S. Rabayah2Mohammed Belkhatir3Saadat M. Alhashmi4Department of Information Technology, Faculty of Engineering and Information Technology, Arab American University, Jenin, PalestineProgramming and Electronic Design Unit, An-Najah National University Hospital, Nablus, PalestineDepartment of Information Technology, Faculty of Engineering and Information Technology, Arab American University, Jenin, PalestineFaculty of Computer Science, University of Lyon, Lyon, FranceDepartment of Information Systems, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab EmiratesSemantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. However, there are challenges that hinder their widespread use in real-world applications. Among these challenges is the insufficient knowledge individually encoded in existing medical ontologies, which is magnified when users express their information needs using long-winded natural language queries. In this context, many of the users' query terms are either unrecognized by the used ontologies, or cause retrieving false positives that degrade the quality of current medical information search approaches. In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically-enhanced inverted index documents, and iii) contribute to a heuristical weighting technique in the query-document matching process. To demonstrate the effectiveness of the proposed approach, we conducted several experiments over the CLEF e-Health 2014 dataset. Findings indicate that the proposed method combining several extrinsic semantic resources proved to be more effective than related approaches in terms of precision measure.https://ieeexplore.ieee.org/document/9064784/Medical information indexing and retrievalquery expansionknowledge engineeringmedical semantics |
| spellingShingle | Mohammed Maree Israa Noor Khaled S. Rabayah Mohammed Belkhatir Saadat M. Alhashmi Head Concepts Selection for Verbose Medical Queries Expansion IEEE Access Medical information indexing and retrieval query expansion knowledge engineering medical semantics |
| title | Head Concepts Selection for Verbose Medical Queries Expansion |
| title_full | Head Concepts Selection for Verbose Medical Queries Expansion |
| title_fullStr | Head Concepts Selection for Verbose Medical Queries Expansion |
| title_full_unstemmed | Head Concepts Selection for Verbose Medical Queries Expansion |
| title_short | Head Concepts Selection for Verbose Medical Queries Expansion |
| title_sort | head concepts selection for verbose medical queries expansion |
| topic | Medical information indexing and retrieval query expansion knowledge engineering medical semantics |
| url | https://ieeexplore.ieee.org/document/9064784/ |
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