Knowledge graphs have recently attracted significant attention from both industry and academia in scenarios that require exploiting large-scale heterogeneous data collections. Knowledge graphs are a type of database where the general structure is a network of entities, their semantic types, properties, and relationships. They support reasoning over the integrated information as the main application. This process is very closely linked to solving the problems of link prediction and query answering for the knowledge graph. The most common approach in tackling these tasks is to compute suitable numeric representations for each graph element, called graph embeddings. In this work, we present an approach to constructing a model that generates meaningful graph representations while maintaining as significant scalability and prediction performance as possible. During preprocessing, network analysis techniques provide graph features, which are utilized by a novel graph embedding model that integrates local representations, obtained using standard and state-of-the-art techniques, into a global picture. Evaluation results show that the approach performs significantly well on the link prediction and query answering tasks on data from Swisscom, achieving accuracy of more than 90% and 50% respectively, reproducing results reported in related work. Certain experiments on academic data confirm the possibility for even further improvement through more focused research.