Abstract
Data warehouses (DW) play a decisive role in providing an- alytical information for decision making. Multidimensional modeling is a special approach to modeling data, considered the foundation for build- ing data warehouses. With the explosive growth in the amount of hetero- geneous data (most of which external to the organization) in the latest years, the DW has been impacted by the need to interoperate and deal with the complexity of this new type of information, such as big data, data lakes and cognitive computing platforms, becoming evident the need to improve the semantic expressiveness of the DW. Research has shown that ontological theories can play a fundamental role in improving the quality of conceptual models, reinforcing their potential to support se- mantic interoperability in its various manifestations. In this paper we propose the application of ontological patterns, grounded in the Unified Foundational Ontology (UFO), for conceptual modeling in multidimen- sional models, in order to improve the semantic expressiveness of the models used to represent analytical data in a DW.