iRecruit: towards automating the recruitment process

Usman Shahbaz, Amin Beheshti*, Sadegh Nobari, Qiang Qu, Hye-Young Paik, Mehregan Mahdavi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)


Business world is getting increasingly dynamic. Information processing using knowledge-, service-, and cloud-based systems makes the use of complex, dynamic and often knowledge-intensive activities an inevitable task. Knowledge-intensive processes contain a set of coordinated tasks and activities, controlled by knowledge workers to achieve a business objective or goal. Recruitment process - i.e., the process of attracting, shortlisting, selecting and appointing suitable candidates for jobs within an organization - is an example of a knowledge-intensive process, where recruiters (i.e., knowledge workers who have the experience, understanding, information, and skills) control various tasks from advertising positions to analyzing the candidates’ Curriculum Vitae. Attracting and recruiting right talent is a key differentiator in modern organizations. In this paper, we put the first step towards automating the recruitment process. We present a framework and algorithms (namely iRecruit) to: (i) imitate the knowledge of recruiters into the domain knowledge; and (ii) extract data and knowledge from business artifacts (e.g., candidates’ CV and job advertisements) and link them to the facts in the domain Knowledge Base. We adopt a motivating scenario of recruitment challenges to find the right fit for Data Scientists role in an organization.

Original languageEnglish
Title of host publicationService Research and Innovation
Subtitle of host publication7th Australian Symposium, ASSRI 2018 Sydney, NSW, Australia, September 6, 2018 and Wollongong, NSW, Australia, December 14, 2018 Revised Selected Papers
EditorsHo-Pun Lam, Sajib Mistry
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages14
ISBN (Electronic)9783030322427
ISBN (Print)9783030322410
Publication statusPublished - 2019
Event7th Australasian Symposium on Service Research and Innovation, ASSRI 2018 - Wollongong, Australia
Duration: 14 Dec 201814 Dec 2018

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356


Conference7th Australasian Symposium on Service Research and Innovation, ASSRI 2018


  • Data-driven business processes
  • Knowledge-intensive business processes
  • Process data analytics
  • Process data science


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