Recent Advances and Future Challenges of Semantic Modeling

In recent years, the efforts of both governmental and commercial institutions to exchange and publish data have significantly increased. Data published by these institutions is usually heterogeneous in terms of structure and semantics, which in turn leads to a large effort in its utilization. One possible solution to ensure that the data can be easily found and accessed is semantic data management. Nevertheless, semantic data management has only been able to gain limited acceptance in everyday work as it requires the creation of a semantic mapping, e.g., in the form of a semantic model, between the data and the used conceptualization. However, this creation is an error prone and time-consuming process. In this paper, we investigate existing semantic modeling approaches and especially discuss their strengths and weaknesses for real-world use. Afterwards, we present future challenges and necessary research directions that the community needs to focus on in order to make the use of semantic modeling and thus, also semantic data management, acceptable in everyday business.