The basis of our research is to construct a job recommendation system to the job seekers by collecting the job portals data. Due to huge amounts of the data in job portals the employers are facing difficulty in the identification of right candidate for the required skill and experience. The job seekers are also facing the problem of getting the suitability of the job based on their skill and experience. The knowledge acquisition based on the requirements is very difficult in case of huge amounts of the data sources. In fact classical development of domain ontology is typically entirely based on strong human participation. It does not adequately fit new applications requirements, because they need a more dynamic ontology and the possibility to manage a considerable quantity of concepts that human cannot achieve alone. The main focus of our work is to generate a job recommendation system with the details of job by taking account into the data posted in the web sites and data from the job seekers by the creation of dynamic ontology. We strongly believe that our system will give the best outcome in case of suitable job recommendation for both employers and job seekers without spending much time. To achieve this first we have extracted the data from various web pages and stored the collected data into .csv files. In the second stage the stored input files are used by the similarity measure and ontology creation module by generating the corresponding Web Ontology Language (.owl) file. The third stage is creating the ontology with the generated .owl by using protégé tool.
展开▼
