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Internet has become the primary medium for Human Resource Management, specifically job

recruitment and employment process. Most classical job recruitment portals on the internet rely

solely on the keyword based search technique in plain text to locate jobs. However, this

technique results in high recall with low precision and also without considering the semantic

similarity between these keywords. Many researchers have also proposed several semantic

matching approaches by developing ontologies as a reference to determine matching accuracy

qualitatively, however these approaches do not quantify how closely matched applicants and

employers are based on core skills. This dissertation proposes a technique that uses an ontology

based approach to enhance keyword searching by leveraging on the similarity between concepts

in the ontology, which represent core skills needed and required for a job in order to determine

how closely matched an applicant is to a job advertisement and vice-versa . This was achieved

by developing a Curriculum Vitae (CV) Ontology, annotating applicant profile and job postings

using a common vocabulary and modifying the semantic concept similarity algorithm to

accurately compute and rank matching score between profiles when a query is performed. The

model was compared with the work of Tran (2016). The results showed that improvements were

achieved in overall matching accuracy between core skills supplied by applicants and those

required by employers. Improvements of 54% and 36% were obtained for Recall and F-measure

respectively, over Tran (2016).



1.1    Research Background

Internet has become the primary medium for recruitment and employment processes. According

to Jobberman’s Online Recruitment Service Report (2015) (Nigeria’s foremost online job portal),

applications on its job recruitment portal increased by over 50% between May and September

2015. This clearly indicates an upward trajectory in online job portals being a major player in

contemporary job recruitment process. The relevance of the Internet in job recruitment process

cannot be overemphasized, more than three-quarter of the age class qualified for recruitment are

active internet users and there is an increasing number of companies that publish their job

vacancies on the web. (Report, 2015)

There is a large number of online commercial job portals competing to publish job postings for a

fee. On the other hand, each company can publish job postings on its company’s own Website

(Mulder, 2010). However, publishing postings on the corporate website reaches a very limited

audience, because the indexing capabilities of current search engines are too imprecise to support

searches for open positions. Beside this, meta-search engines are limited in their ability to

generate offers that match the precise needs of the clients since job postings are written in free

text form using uncontrolled vocabulary (Mochol et al., 2007). Furthermore, some dedicated

search engines are entering into the market, allowing detailed queries as opposed to keyword-

based search of current search engines. However, the quality of search results depends not only

on the search and index methods applied, but on the relevance of the search result to the user’s

query. Influential factors include the process ability of the used web technologies and the quality


of the automated interpretation of the company specific terms occurring in the job descriptions.

The problems of a website’s machine process ability result from the inability of classical web

technologies to semantically annotate the content of a given website. Semantic Web extends the

Web with machine-understandable data, in addition to classic Hypertext Mark- up Language

(HTML) pages. This implies that viable improvements can be made for all parties involved in the

recruitment process by tapping into the capabilities of semantic web technologies based on the

semantic annotation of job postings and profiles.

In human resource management, it is often necessary to locate and match individuals and

positions. Examples of such tasks include human resource recruiting, selecting individuals for

teams based on different skills and qualifications, and finding the right expert to acquire

information or to learn from within an organization. For human resource recruiting, the Internet

is being mainly used to place online job advertisements, to perform resume search, and to

acquire information about skills and competencies of individuals (Dafoulas et al., 2003). In order

to augment and assist this process, the study and development of totally or partially automated

techniques and tools have received the attention of both researchers and organizations. To

effectively locate and match individuals and positions, within or from outside an organization, it

is important to use semantic technology (Coulucci et al., 2003). Semantic descriptions of job

offers and applicant profiles allow for qualitative and quantitative reasoning about matching

between available and required skills and competencies which is needed to improve the process

of deciding whom to hire and assigning individuals to tasks and teams. Furthermore, semantic

descriptions of applicant profiles within an organization helps improve the management of

individual skills and competencies of available human resources, and provide a global view of

the skills available at the organizational level. The World Wide Web is a fast growing medium of


information and servi

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