Author First name, Last name, Institution

Malik Nabeel Ahmed Awan
Sharifullah Khan
Khalid Latif
Asad Masood Khattak

ORCID Identifiers


Document Type


Source of Publication

Applied Sciences

Publication Date



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.








Physical Sciences and Mathematics

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus


Open Access


Open Access Type

Gold: This publication is openly available in an open access journal/series