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 users�...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/88419/1/NaomieBtSalim2019_ExpertFindingSystemsASystematicReview.pdf |
| _version_ | 1796864627873677312 |
|---|---|
| author | Husain, Omayma Salim, Naomie Alias, Rose Alinda Abdelsalam, Samah Hassan, Alzubair |
| author_facet | Husain, Omayma Salim, Naomie Alias, Rose Alinda Abdelsalam, Samah Hassan, Alzubair |
| author_sort | Husain, Omayma |
| collection | ePrints |
| 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 | 2024-03-05T20:44:44Z |
| format | Article |
| id | utm.eprints-88419 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:44:44Z |
| publishDate | 2019 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | utm.eprints-884192020-12-15T00:06:14Z http://eprints.utm.my/88419/ Expert finding systems: A systematic review Husain, Omayma Salim, Naomie Alias, Rose Alinda Abdelsalam, Samah Hassan, Alzubair QA75 Electronic computers. Computer science 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. MDPI AG 2019-10 Article PeerReviewed application/pdf en http://eprints.utm.my/88419/1/NaomieBtSalim2019_ExpertFindingSystemsASystematicReview.pdf Husain, Omayma and Salim, Naomie and Alias, Rose Alinda and Abdelsalam, Samah and Hassan, Alzubair (2019) Expert finding systems: A systematic review. Applied Sciences (Switzerland), 9 (20). p. 4250. ISSN 2076-3417 http://dx.doi.org/10.3390/app9204250 |
| spellingShingle | QA75 Electronic computers. Computer science Husain, Omayma Salim, Naomie Alias, Rose Alinda Abdelsalam, Samah Hassan, Alzubair Expert finding systems: A systematic review |
| 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 | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/88419/1/NaomieBtSalim2019_ExpertFindingSystemsASystematicReview.pdf |
| work_keys_str_mv | AT husainomayma expertfindingsystemsasystematicreview AT salimnaomie expertfindingsystemsasystematicreview AT aliasrosealinda expertfindingsystemsasystematicreview AT abdelsalamsamah expertfindingsystemsasystematicreview AT hassanalzubair expertfindingsystemsasystematicreview |