Dealing with heterogeneous sources is an important chal- lenge in the field of knowledge discovery and management. Schema match- ing methods are employed to solve this problem using three approaches: schema-based, instance-based, or a combination. This paper focuses on mapping between a schema-available (only) data source and a data source containing both schema and instance (both). Given the lack of suit- able methods for aligning these two types of sources, we propose an ap- proach using embedding models to provide vector modelling of sources and calculate similarities between data. Our solution consists in com- bining domain-specific embedding models and cross-domain embedding models to make data matching possible and efficient between the above- mentioned data sources. We have conducted several experiments using the Valentine datasets to evaluate our data matching method on sev- eral disparate tabular data. The result indicate effectiveness in terms of stability and ablation handling.