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Automated expertise retrieval: a taxonomy-based survey and open issues
Gonçalves R., Dorneles C.  ACM Computing Surveys 52 (5): 1-30, 2019. Type: Article
Date Reviewed: Aug 31 2021

Due to the rigorous advancement in skill sets through knowledge enhancement, multiple methods of sharing the required skills can challenge industries that are looking for the “right fit.” At the same time, the storage of information in the paperback version is becoming restrained, therefore leading to the transfer of information through the Internet, database services, and social media. Manually analyzing expertise linked to data and information could be a daunting job, hence demands for an automated approach.

In this detailed survey, the authors focus on the structure of the automated expertise retrieval process. Readers are also introduced to a faceted taxonomy where several existing methodologies are considered and classified into various approaches and classifications. Section 1, “Introduction,” is very well written in a simplified tone, to keep the reader’s attention. The survey includes reviews of existing work done in similar areas. The proposed faceted taxonomy in section 2 serves as a guide and introduces new issues for discussion. Taxonomy facets in tabular form present the general picture before diving into the details of each component in sections 3 through 6, making the flow very clear for readers.

Extensive comparison is made between “a selected set of works related to expertise retrieval” in section 7. The study covers 26 related works and includes the most recent and/or relevant ones. Finally, section 8 highlights open issues, discussion, and suggestions.

This article clearly identifies a platform and recommendations for future studies in the field. Its comprehensive list of references depicts the extensive study already performed by the authors. I would recommend it to data scientists involved in expertise retrieval system design.

Reviewer:  Brijendra Singh Review #: CR147344 (2112-0290)
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General (H.0 )
 
 
Information Search And Retrieval (H.3.3 )
 
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