As organizations continue to access diverse datasets, the demand for effective data integration has increased. Key tasks in this process, such as schema matching and entity resolution, are essential but often require significant effort. Although previous studies have aimed to automate these tasks, the influence of dataset characteristics on the matching effectiveness has not been thoroughly examined, and combinations of different methods remain limited. This study introduces a contextual graph embedding technique that integrates structural details from tabular data and contextual elements such as column descriptions and external knowledge. Tests conducted on datasets with varying properties such as domain specificity, data size, missing rate, and overlap rate showed that our approach consistently surpassed existing graph-based methods, especially in difficult scenarios such those with a high proportion of numerical values or significant missing data. However, we identified specific failure cases, such as columns that were semantically similar but distinct, which remains a challenge for our method. The study highlights two main insights: (i) contextual embeddings enhance the matching reliability, and (ii) dataset characteristics significantly affect the integration outcomes. These contributions can advance the development of practical data integration systems that can support real-world enterprise applications.