Ontology based ranking of documents using Graph Databases: a Big Data Approach

Today recruiters find their suitable human resources by searching in the job related web sites. In the same way, the job seekers also select their suitable jobs. The job seekers post their resumes in the multiple web sites. The HR tools available in the market make the job of recruiters easy by giving them the suitable resumes. Still, there is a good chance that the relevant documents may be missing in the list and unwanted document may exist in the list. This paper proposes a model for extracting resume information from different websites and makes the job of job recruiter easier by finding the suitable resume to fit their needs. Ontology is created with the suitable entities and their relationships for this domain. Each resume is split into four different sections namely – personal, education, skills and work experience. Attribute values are extracted from the resume documents. These values are updated in four different Resource Description Framework (RDF) files for each resume through ontology mapping. Resumes are ranked based on cosine similarity measure and then the ontologies are updated correspondingly. Here Graph database (i.e RDF), a NoSQL data model is used for storing the resumes and SPARQL is used for querying the documents. Experiments are carried out based on the retrieval time of relevant documents to find out the effective RDF model for storing the resumes.