In this paper, we present our system Kepler-aSI, for the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2021). This system is participating for the second time in this campaign, bringing improvements and new technical aspects. Kepler-aSI analyzes tabular data to be able to detect correct matches in Wiki-data and DBPedia. It should be noted that each data resource, or each round of the campaign imposes a certain number of constraints, requiring advanced techniques. The aforementioned task turns out to be difficult for the machines, which requires an additional effort in order to deploy the cognitive capacity in the matching methods. Kepler-aSI still relies on the SPARQL query to semantically annotate tables in Knowledge Graphs (KG), in order to solve the critical problems of matching tasks. The results obtained during the evaluation phase are encouraging and show the strengths of the proposed system.