Data transformation from diverse formats to semantic data is a major challenge that is being addressed by commercial products available in the market to some extent. However, the transformation process still requires considerable effort from users. Knowledge creation, whichis one of the major steps in knowledge graph (KG) maintenance, needs existing data transformation from various formats to RDF (Resource Description Framework) data. Current data transformation approaches are either manual or through mappings. This work presents a semantic data transformation approach for knowledge creation that is semi-automatic requiring minimum possible input from users and does so with machine learning and natural language processing techniques.