As the usage of RDF knowledge graphs (KGs) becomes more pervasive in practical applications, there is a burgeoning need for high-quality RDF data. The SHApes Constraint Language (SHACL) enables precise constraint expression on RDF graphs, ensuring data structure compliance. However, a SHACL validation that overlooks the crucial implicit information encoded in the ontology of the KG may result in unsound results. Semantic-aware SHACL validation addresses this by considering implicit information in RDF graphs, thus enabling thorough and accurate data validation. Current methods that incorporate entailment into SHACL validation often face efficiency challenges due to the resource-intensive nature of applying inference rules across entire datasets. In this doctoral work, we explore methods to enhance the efficiency of semantic-aware SHACL validation, presenting the problem statement, research questions, hypotheses. The paper concludes by our proposed method and sharing preliminary results from our research.