The Web contains vast amounts of semi-structured data in the form of HTML tables found on Web pages which may serve for various applications. One prominent application, which is often referred to Semantic Table Interpretation, is to exploit the semantics of a widely recognized knowledge bases (KB) by matching tabular data, including column headers and cell contents, to semantically rich descriptions of classes, entities and properties in Web KBs. In this paper, we focus on relational tables which are valuable sources of facts about real-world entities (persons, locations, organizations, etc.) and we propose a robust and efficient approach for bridging the gap between millions of Web tables and large-scale Knowledge graphs such as DBpedia. Our approach is holistic and fully unsupervised for semantic interpretation of Web tables based on the DBpedia Knowledge graph. Our approach covers three phases that heavily rely on word and entity pre-trained embeddings to uncover semantics of Web tables. Our experimental evaluation is conducted using the T2D gold standard corpus. Our results are very promising compared to several existing approaches of annotation in web tables.