Abstract
Big Data Era has largely contributed in accelerating the development of large, high quality and valuable Knowledge Bases (\(\mathcal {K}\mathcal {B}\)) by academicians (e.g., Cyc, DBpedia, Freebase, and YAGO) and industrials (e.g., Knowledge Graph). On the other hand, serious studies have identified the crucial role of \(\mathcal {K}\mathcal {B}\) for analytical tasks, by offering analysts more entities (people, places, products, etc.). The availability of a huge, high quality and valuable \(\mathcal {K}\mathcal {B}\) may contribute on designing value-added approaches for business intelligence applications. In this paper, we first propose a novel approach for semantic \(\mathcal {D}\mathcal {W}\) design that considers \(\mathcal {K}\mathcal {B}\) in the life cycle. Secondly, based on graph formalization adapted to \(\mathcal {K}\mathcal {B}\), we produce conceptual multidimensional design and a semantic ETL process that orchestrates the graph data flows from data sources to the \(\mathcal {D}\mathcal {W}\) storage. Finally, all steps of our approach are illustrated using the YAGO \(\mathcal {K}\mathcal {B}\) and deployed in Oracle RDF Semantic Graph 12c.