Semantic Annotation and Retrieval in E-Recruitment

Abstract

E-recruitment processes prioritize matching between job descriptions and user queries to identify relevant candidates. Existing e-recruitment systems face challenges in extracting job descriptions due to unstructured nature of content and text nomenclature di erences for de ning the same content. The systems are particularly unable to extract e ectively contextual entities, such as job requirements and job responsibilities from job descriptions. They also lack in producing e ectively desired search results due to semantic di erences in job descriptions and users English natural language queries. This thesis proposes a framework to cater for challenges in the existing e-recruitment systems. The proposed Semantic Extraction, Enrichment and Transformation (SExEnT) framework extracts entities from job descriptions using a domain speci c dictionary. The extraction process rst performs linguistic analysis and then extracts entities and compound words. After the extraction of entities and compound words, it builds job context using a job description domain ontology. The ontology provides an underlying schema for de ning how concepts are related to each other. Besides building a contextual relationship among entities, the entities are also enriched using Linked Open Data (LOD) that improves search capability in nding suitable jobs. In the proposed framework, Web Ontology Language (OWL) is used to represent information for machine-understanding. The framework api ii propriately matches users queries and job descriptions. The evaluation data set has been collected from various jobs portals, such as Indeed, Personforce, DBWorld. A total of 860 jobs were collected that belong to multiple categories, such as technology, medical, management and others. The data set was vetted and veri ed by HR experts. The evaluation has been performed using precision, recall, F-1 measure, accuracy and error rate. The proposed framework achieved an overall F-1 measure of 87.83% and accuracy of 94% for entities extraction. The application has a precision of 99.9% in representing and retrieving job descriptions from its knowledge base. The job description ontology has an overall concept coverage of 96%. The evaluation results show that the proposed framework performs well in extracting, modelling, enriching, and retrieving job description against queries. At current, the proposed framework is neither able to automatically generate pattern/action rules, nor provide a complex ranked retrieval of job descriptions against a user pro le nor automatically extend dictionary to increase extraction precision. In future, the framework can be extended to resolve these limitations.