ORCID Identifiers
Document Type
Article
Source of Publication
Applied Sciences
Publication Date
7-17-2019
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.
DOI Link
ISSN
Publisher
MDPI
Volume
9
Disciplines
Physical Sciences and Mathematics
Keywords
Semantic web, Information retrieval, Information extraction, E-recruitment
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ahmed Awan, Malik Nabeel; Khan, Sharifullah; Latif, Khalid; and Khattak, Asad Masood, "A New Approach to Information Extraction in User-Centric E-Recruitment Systems" (2019). All Works. 177.
https://zuscholars.zu.ac.ae/works/177
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series