In this paper we propose a model of ontology for the human resource domain. We emphasize the benefits resulting from the application of Semantic Web technologies in the recruitment process. We use currently available standards and classifications to develop a human resource ontology which gives us means for semantic annotation of job postings and applications. Furthermore, we outline the process of semantic matching which improves the quality of query results. Finally, we propose the architecture of an evaluation system based on Semantic Web technologies.human resource ontology, HR-XML, e-recruitment, semantic annotation.

George MANIU

Ionela MANIU

English

Research Papers in Economics

 
Volume 10, Issue 2, Year 2009                                                        Review of General Management  12 
A HUMAN RESOURCE ONTOLOGY FOR RECRUITMENT PROCESS 
 
Ionela MANIU   
“Lucian Blaga” University Sibiu, Romania 
Faculty of Sciences 
mocanionela@yahoo.com 
George MANIU  
“Spiru Haret” University Bucharest, Romania 
Faculty of Management Brasov 
georgemaniu@yahoo.com 
 
 
 
 
 
 
 
 
Keyword: human resource ontology, HR-XML, e-recruitment, semantic annotation. 
 
JEL Classification: M15, O15. 
 
1. Introduction 
 
Human Resource Management (HRM) or, more specifically, competence management 
is an important function of knowledge management because it reasons about an organization’s 
most important resource – the knowledge and skills of its staff. Typically  such  functions as  
staffing, project  staffing,  human  resources  development  and  more  are  supported  by 
competence management,  i.e.  the  reasoning  about  availability  and  absence  of  required 
competencies both  on  the  level of  an organization as well  as on  the  level of  individual 
members of the organization.  
Traditionally, in personnel management more coarse-grained reasoning was applied. Job 
postings internally as well as externally contained a job type (e.g.  programmer, database 
manager or accountant) or  a certain university degree  that  was  required were used  to 
describe  which  competencies  an  applicant  should  have.  The  US  Office  of  Personnel 
Abstract 
In this paper we propose a model of ontology for the human resource domain. We emphasize 
the benefits resulting from the application of Semantic Web technologies in the recruitment process. 
We  use  currently  available  standards  and  classifications  to  develop  a human  resource  ontology 
which gives us means for semantic annotation of job postings and applications. Furthermore, we 
outline the process of semantic matching which improves the quality of query results. Finally, we 
propose the architecture of an evaluation system based on Semantic Web technologies. Review of General Management                                                Volume 10, Issue 2, Year 2009  13 
Management (1999)  reports on  fundamental changes  in  the Human Resource Area and 
argue that competencies may help organizations  to  focus on  the  characteristics  their  
employees must  possess  in  order  for  them  to  be  successful  and  that  competencies  also  
provide    a  means    to  measure  employee  performance  and    to    align    performance  with  
business  strategies.  Consequently, recent versions of Enterprise Resource Planning (ERP)-
systems provide modules for competency management. However, the supported functionality 
is scarce. 
There  is  some  research  on  requirements  for  a  more  sophisticated  competence 
management for companies. Bergenhenegouwen et al. (1996) investigate the potentials arising 
from detailed competence management and Lindgren et al. (2004) derive requirements for 
competence managements system from several case studies and the cited study.  
In many cases the information about competencies are exchanged  between different 
organizations. The  HR-XML  consortium  (Allen  and  Pilot  2001)  has  defined  different 
XML-Schemes    to    exchange    HR-related    information    between    organizations.    These  
definitions, however,  do  not  include definitions  of  certain competencies but  only  a 
method  how    to    link    claimed    competencies    to    an    external    definition.  Therefore 
competencies should be “standardized” additionally to understand what a certain competence 
means.  In HR-XML the  term  taxonomy  is  used  and usually  taxonomies  are only  used  to  
describe competency hierarchies.  
Ontologies  are a  stronger  form of  knowledge  representation  by applying  additional  
relations  and  constraints  between  concepts.  They are a means to support understanding 
between communicating partners.  An  ontology  for  HRM  can define concepts  such  as  
“competency”, “job” and certain  attributes  of  such  concepts  as well as  the  relationships 
between  these concepts. Thus we may define which competencies are  required  for a  certain  
job  and  which  knowledge  and  experience  is  required  to achieve a certain level of 
competence. 
Nowadays many business transactions are carried out via Internet. Human resources 
management has also discovered Internet as an effective communication medium. The section 
2 briefly describes a typical recruitment process and suggests areas for application of the 
Semantic  Web  technologies.  Subsequently,  in  section  3,  we  present  our  human  resource 
ontology founded on currently available standards and classifications. In section 4 we outline 
the process of semantic matching which improves the quality of query results. Finally, we 
propose  an  architecture  of  a  prototype  recruitment  portal  based  on  Semantic  Web 
technologies. 
 
  
Volume 10, Issue 2, Year 2009                                                        Review of General Management  14 
2. The Recruitment Process 
 
From an organization’s viewpoint, a typical recruitment process can be divided into four 
main  phases:  describing  the  requirements  of the job  position,  publishing  the  job  posting, 
receiving of applications, and final decision making. 
Nowadays, job postings are written in form of free text using uncontrolled vocabulary. 
In contrast, semantic annotation of job postings using concepts from a controlled vocabulary, 
based on Semantic Web technologies, results in language independent descriptions and better 
machine processability, i.e. improved matching of job postings and applications. 
There are as many ways to post a job offer on the Web as places where an applicant can 
look for a job. There are Web sites and online portals financed by publishing fees, corporate 
Web sites, and portals run by state job centres like the German Federal Employment Office 
(BA) and the Swedish National Labour Market Administration (AMS). Job exchange portals 
differ substantially according to market scope and positioning, supported functionality, and 
selection methods. To maintain an overview on so many portals is next to impossible. With 
the  Semantic  Web  one  would  publish  an  RDF  annotated  job  posting  only  on  the 
organization’s  web  site,  which  in  turn,  would  be  crawled  by  various  job  portals. 
Consequently, job postings  would  reach more potential applicants, which  would result in 
higher market transparency. Moreover, both employers and applicants would benefit from a 
semantic  description  of  job  postings  and  applications.  Applicants  could  reuse  their 
semantically annotated profiles, that is to say, to send them to different employers instead of 
filling in different web forms. Employers would also benefit from automated matchmaking 
between  job  requirements  and  applications,  which  would  deliver  a  ranked  list  of  best 
matching candidates and thereby reduce administrative costs. 
 
3. Human Resource Ontology 
 
As a  first step in  realizing our human resource scenario,  we have created a  human 
resource  ontology  (HR-ontology)  by  integrating  some  existing  widespread  standards  and 
classifications. To avoid redundancy we decided to form sub-ontologies (see fig. 1.) which are 
used in both job posting and job application descriptions. Our HR-ontology is mainly based 
on the German version of the HR-XML standard developed by the HR-XML Consortium.  
HR-XML is a library of more than 75 interdependent XML schemes which define data 
components for various HR transactions, as well as options and constraints governing the use 
of these components. We adapted the Job Position Seeker and Job Position Posting segments Review of General Management                                                Volume 10, Issue 2, Year 2009  15 
from  HR-BA-XML  (German  version  of  HR-XML)  with  respect  to  requirements  of  our 
scenario. 
In the course of our work we have realized that the recruitment process today is no 
longer restricted to the national level. Hence, to specify the Industry sub-ontology we use,   
the German Classification of the Industry Sector (WZ2003) and the North American Industry 
Classification  System  (NAICS).  As  occupational  classification  we  utilize  the  Romanian 
Classification of Occupations (COR),the German Occupation Code (BKZ) and the Standard 
Occupational Classification (SOC) System. 
 
 
Fig. 1. Sub-ontologies within the HR-ontology 
 
In the Skills sub-ontology we defined concepts representing competencies, which are 
used to describe job requirements as well as applicant’s skills. This ontology also provides 
means for specifying the competence level of a certain skill. Furthermore, we developed a 
Person  sub-ontology  for  describing  personal  information  of  job  applicants  and  an 
Organization sub-ontology to specify the employer companies. 
Our HR-ontology is modelled with the Web Ontology Language (OWL). Descriptions 
of job postings and applicant’s profiles are stored in RDF (Resource Description Framework) 
format using the pre-defined vocabulary from our HR-ontology.  
 
4. Semantic Matching of Job Postings and Job Applications 
 
Having  job  position  postings  and  job  position  seekers  described  using  controlled  
vocabulary  from  our  HR-ontology  allows  us  to  perform  semantic  matching,  i.e.  the 
calculation  of  the  degree  of  semantic  similarity  between  an  applicant’s  profile  and  job 
requirements  Consequently,  for  a  given  job  position  a  ranked  list  of  the  best  matching 
candidates can be delivered as a result, and vice versa.  
Volume 10, Issue 2, Year 2009                                                        Review of General Management  16 
Inside both a job posting as well as a job application we group pieces of information 
into  “thematic  clusters”,  e.g.  information  about  competencies  and  skills,  information 
regarding the industry sector of the job position, and job position details. In our approach, we 
compare each thematic cluster from a job posting with the corresponding cluster from a job 
application. The final similarity between an applicant’s profile and a job posting is calculated 
as the sum  of the similarities  between these thematic  clusters  since  information provided 
within each cluster is represented using controlled vocabulary from our ontology. 
HR-ontology,  we  compute  the  similarity  between  thematic  clusters  based  on  the 
similarity between their concepts. In our method, using some ideas from, we determine the 
similarity between two concepts by their respective positions in the concept hierarchy and 
additionally by the similarity between their properties (relations with other concepts). 
An example property of a concept from the “skills cluster” (e.g. java programming) is 
the competence level (having another concept as its value, e.g. beginner).   Furthermore, 
within the user interface (see fig. 2.) users are given the opportunity to specify the importance 
of different concepts (job requirements). The concept similarity will be then justified by the 
indicated “weight”, i.e. the similarity  between more important  concepts will have  greater 
influence on the cluster similarity. 
 
5. Architecture of the Evaluation System 
 
To examine the impact of the application of Semantic Web technologies on the job 
market we are currently developing a recruitment platform. The technical architecture of our 
system (see fig. 2.) is divided into: data layer, application logic, and front-end layer. This 
architecture is similar to with additional functionality for automatic crawling of job postings 
from different web sites. 
 
 
 
 
 
 
 
 
 
Fig. 2. Architecture of the Evaluation System Review of General Management                                                Volume 10, Issue 2, Year 2009  17 
RDF-repositories located at the data layer store the human resource ontology and all 
collected  descriptions  of  job  postings  and  user  profiles.  The  application  logic  provides 
services for querying the database and editing its content. Moreover, at this layer the semantic 
matching of job postings and applications is performed. On top of the architecture, the front-
end layer offers a browser-based user interface which accepts input from and presents ranked 
query results to the user, and another interface providing services for import and export of 
RDF-encoded job postings and applications. 
 
6. Conclusion 
 
In  this  paper,  we  proposed  an  application  of  Semantic  Web  technologies  in  the 
recruitment domain. We described the human resource ontology used within our approach, 
which provides us means for semantic annotation of job postings and job applications. Using 
controlled  vocabularies,  in  contrast  to  free  text  descriptions,  results  in  a  better  machine 
processability, data interoperability and integration.  
Moreover, having job postings and user profiles semantically annotated, enables us to 
perform semantic matching which significantly improves query results and delivers a ranked 
list of best matching candidates for a given job position. Finally, we described the system 
architecture of the recruitment portal based on Semantic Web technologies. In the next step of 
our research, we plan to implement the recruitment portal founded on the ideas presented in 
this paper.  
 
References 
 
1.  Billig, A., Sandkuhl, K., (2002), Match-making based on semantic nets: The xml-based 
approach of baseweb, In: Tolksdorf, R., Eckstein, R., (Hrsg.), “Proceedings of the 1
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workshop on XML-Technologien fur das Semantic Web”, S. 39–51 
2.  Zhong, J., Zhu, H., Li, J., und Yu, Y., (2002), Conceptual Graph Matching for Semantic 
Search,  ICCS  2002  -  The  2002  International  Conference  on  Computational  Science, 
Amsterdam, The Netherlands 
3.  Internet¨okonomie, Internet¨okonomie, (2004), http://www.internetoekonomie.net 
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chains, (2004), http://interval.hu-berlin.de 
5.  W3C: OWL Web Ontology Language—Reference, (2004),  http://www.w3.org/TR/owlref 
6.  http://www.arbeitsagentur.de/ 
7.  http://www.ams.se  
Volume 10, Issue 2, Year 2009                                                        Review of General Management  18 
8.  http://www.hr-xml.org 
9.  http://www.destatis.de/allg/d/klassif/wz2003.htm 
10. http://www.census.gov/epcd/www/naics.html 
11. http://www.itmtimis.ro/html/cor.htm 
12. http://www.arbeitsamt.de/hst/markt/news/BKZ alpha.txt 
13. http://www.bls.gov/soc/ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

A HUMAN RESOURCE ONTOLOGY FOR RECRUITMENT PROCESS

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