A New Approach to Information Extraction in User-Centric E-Recruitment Systems

Abstract

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users' profiles, it is complicated to appropriately match a user's profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users' profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs' portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs.

Towards a New Job Offers Recommendation System Based on the Candidate Resume

“…The SAJ project [2] presents the method of extracting data from the job offer description and from the candidate profile in order to be able to match them; they used the Linked Open Data system, ontology job posting description domain, and domain-specific dictionaries for data mining. SAJ enriches and builds the context between the extracted data to minimize the loss of information in the extraction process.…”

“…This implies a considerable waste of time for candidates to manage their accounts, fill in all the information concerning the e-portfolio and consult the job offers published on each platform. A study was carried out on the recommendation system for job offers (Teambuilder [1], SAJ [2], etc.). We find that most recommendation systems are oriented towards the company and not towards the candidate and depend heavily on the platform, which means, each recommendation system is linked with a platform offering the job offers.…”

Building a recommendation system based on the job offers extracted from the web and the skills of job seekers

“…The proposed e-recruitment system called (SAJ project) [2] presents the method of extracting data from the job offer description and from the candidate profile to be able to match them; they used the linked open data system, ontology job posting description domain, and domain specific dictionaries for data mining. SAJ enriches and builds the context between the extracted data to minimize the loss of information in the extraction process.…”

“…The conventional information retrieval or recommendation methods using vector modeling directly deduce the relevance based on the similarity measure between the vector representing the user and that representing the item. Following a study carried out on job offer recommendation systems (the word processor project [1], and the e-recruitment system analysis and design (SAJ) [2]). Most job recommender systems are company-oriented, not candidate-oriented, and highly dependent on the platform offering the jobs, i.e., each recommender system is linked with a platform.…”