Expert Finding Systems: A Systematic Review
The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the use...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/9/20/4250 |
| _version_ | 1828174326212329472 |
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| author | Omayma Husain Naomie Salim Rose Alinda Alias Samah Abdelsalam Alzubair Hassan |
| author_facet | Omayma Husain Naomie Salim Rose Alinda Alias Samah Abdelsalam Alzubair Hassan |
| author_sort | Omayma Husain |
| collection | DOAJ |
| description | The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research. |
| first_indexed | 2024-04-12T04:10:58Z |
| format | Article |
| id | doaj.art-64bcbf5b550840808e3a034e055ee5df |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-12T04:10:58Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-64bcbf5b550840808e3a034e055ee5df2022-12-22T03:48:31ZengMDPI AGApplied Sciences2076-34172019-10-01920425010.3390/app9204250app9204250Expert Finding Systems: A Systematic ReviewOmayma Husain0Naomie Salim1Rose Alinda Alias2Samah Abdelsalam3Alzubair Hassan4School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, MalaysiaAzman Hashim International Business School, Universiti Teknologi Malaysia, Skudai 81310, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, MalaysiaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, ChinaThe data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.https://www.mdpi.com/2076-3417/9/20/4250expert finding systemsexpertise retrievalexpertise seeking |
| spellingShingle | Omayma Husain Naomie Salim Rose Alinda Alias Samah Abdelsalam Alzubair Hassan Expert Finding Systems: A Systematic Review Applied Sciences expert finding systems expertise retrieval expertise seeking |
| title | Expert Finding Systems: A Systematic Review |
| title_full | Expert Finding Systems: A Systematic Review |
| title_fullStr | Expert Finding Systems: A Systematic Review |
| title_full_unstemmed | Expert Finding Systems: A Systematic Review |
| title_short | Expert Finding Systems: A Systematic Review |
| title_sort | expert finding systems a systematic review |
| topic | expert finding systems expertise retrieval expertise seeking |
| url | https://www.mdpi.com/2076-3417/9/20/4250 |
| work_keys_str_mv | AT omaymahusain expertfindingsystemsasystematicreview AT naomiesalim expertfindingsystemsasystematicreview AT rosealindaalias expertfindingsystemsasystematicreview AT samahabdelsalam expertfindingsystemsasystematicreview AT alzubairhassan expertfindingsystemsasystematicreview |