E-recruitment processes prioritize matching between job descriptions and user queries to identify relevant candidates. Existing e-recruitment systems face chal lenges in extracting job descriptions due to unstructured nature of content and text nomenclature differences for defining the same content. The systems are par ticularly unable to extract effectively contextual entities, such as job requirements and job responsibilities from job descriptions. They also lack in producing effec tively desired search results due to semantic differences 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 specific dictio nary. The extraction process first 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 ontol ogy provides an underlying schema for defining 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 finding suitable jobs. In the proposed framework, Web Ontology Language (OWL) is used to represent information for machine-understanding. The framework ap 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 verified by HR experts. The evaluation has been performed using precision, recall, F-1 measure, accuracy and error rate. The proposed frame work 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 retriev ing job descriptions from its knowledge base. The job description ontology has an overall concept coverage of 96%. The evaluation results show that the pro posed 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 profile nor automatically extend dictio nary to increase extraction precision. In future, the framework can be extended to resolve these limitations.