With the emergence of NoSQL multi-model databases (natively support- ing scalable and unified storage and querying of various data models such as graph, documents, key-values, relational, etc.) arise new opportunities for efficient representation and efficiently storing of data. Whereas tradi- tional systems relay on multiple databases and/or using databases that’s not optimized for the data that needs storing, multi-model databases allow for more flexibility and are built around the concept of database distribu- tion and availability. The multi-model structure also allow for one database to do what several databases nowadays are combined to do in a polyglot structure. Semantic data with its graph-oriented structure is one type of data struc- ture that could benefit from the use of multi-model databases, both for rep- resenting and storing the data. RDF is a popular model for semantic data, but RDF management systems are facing challenges when it comes to scal- ability and generality and the scalability challenge is particularly urgent. Working with RDF graphs, which are typically highly connected and dis- tributed, results in querying large volumes of data, thus making the scal- ability issue more pressing. Earlier approaches to make better storage for RDF data have been done through the use of relational databases, but even though they are optimized for data handling they are not very flexible and semantic data doesn’t necessarily fit within a pre-defined rigid schema in- side the relational database. NoSQL databases allow for better flexibility and do not enforce any pre-defined schema to the data stored, thus better supporting the variety of data within the semantic data domain. This thesis explores and defines different approaches to represent and store RDF data within a multi-model NoSQL database. Id identifies various as- pects of representing the RDF data structure into a multi-model data struc- ture and discusses their advantages and disadvantages. In addition, the v thesis also describes an approach to represent the semantic spacetime data model introduced by Mark Burgess, compering how two different semantic models (RDF and spacetime) can be represented in the same multi- model database. Furthermore, the thesis proposes a prototype implementation of the two representation and storage approach in ArangoDB — a popular multi-model database.