RDF predicate selection and RDF subject identification are two key steps for effectively processing the natural language queries by the semantic question answering systems. For subject identification, techniques such as Named Entity Recognition are used. For the relatively complex task of predicate selection, Natural Language Processing techniques can be utilized along with knowledge bases to select the semantically matching relationships. Semantic features can be extracted from queries utilizing phrase embeddings of the transformer models. In this paper firstly, a novel Predicate Selection approach on RDF Knowledge Bases has been proposed and implemented utilizing BERT-based phrase embeddings and custom logic for key part-of-speech token combinations. Secondly, the implemented prototype is benchmarked over combinations of key part of speech tokens and stop words for predicate selection accuracy over YAGO Date Facts, demonstrating the effectiveness of our approach. Benchmarking results also highlight the importance of verb stop words for the task of predicate selection.