Data and meta-data hierarchies are playing a central role in the development and deployment of many big data applications. They can be defined as structured information that describes, explains, locates, and makes it easier to retrieve, use, and manage information within a large corpus of resources. Since hierarchical data have significant roles in data annotation, search, and navigation, they are often carefully engineered; however, especially in dynamically evolving domains, meta-data from different domains and/or producers are rarely identical, even when describing the same data scope; thus, there is a need for techniques to find alignments between concepts in different structures. In this paper, we present a novel method that leverages dynamic big data information to improve concept matching among different static meta-data structures. This method captures the structural information inherent in hierarchical meta-data and enriches it with semantically coherent information extracted from external data to permit more precise matching operations among concepts across different hierarchies.