With the rapid development of the data economy, the demand for effective data copyright protection has become increasingly pressing. Digital watermarking is considered one of the most promising techniques for verifying the ownership of digital data, and various relational database watermarking methods have been proposed. However, most existing schemes embed information into numerical attributes through slight perturbations, which are difficult to extend to categorical attributes whose values are discrete and subject to strict semantic constraints. To overcome this limitation, we propose a novel and robust database watermarking scheme designed for categorical data in relational databases. It employs mutual information to quantify inter-attribute dependency strength and embeds watermarks via semantically consistent substitutions under the joint distribution constraints of the k most relevant attributes associated with each target attribute. This design preserves attribute-level semantic consistency while effectively minimizing data utility loss. Experimental results on real-world datasets demonstrate that the proposed scheme exhibits strong robustness against various attack scenarios, while exerting negligible influence on the performance of downstream classification tasks and exhibiting high efficiency. These findings indicate that our method provides a reliable mechanism for copyright verification and traceability in databases containing categorical attributes.