<p>Tabular data, frequently encountered on the Web, represents information organized in a tabular format composed of rows and columns. This format is extensively employed across various web-based contexts and data storage frameworks. Furthermore, the inherent structure of tabular data encapsulates substantial semantic information, which encourages ongoing analysis and application. Therefore, the process of deriving significant insights from structured data through semantic approaches, including ontologies or Knowledge Graphs, is typically referred to as Semantic Table Interpretation (STI) or Semantic Table Annotation. In this article, we introduce <span>Kepler-aSI.v2</span>, a matching methodology designed to resolve potential semantic inconsistencies between tabular data and a Knowledge Graph. This task continues to be a formidable challenge for computational systems, necessitating additional effort for the integration of cognitive capabilities into matching algorithms. The principal aim of our approach is to devise a rapid and efficient method for the annotation of tabular data using attributes extracted from a specified Knowledge Graph. Our method integrates filtering mechanisms and text pre-processing strategies. The evaluation conducted according to the SemTab challenge has yielded promising and encouraging results.</p>