Ontology-Based Correlation Detection Among Heterogeneous Data Sets: A Case Study of University Campus Issues

For data-driven decision making, it is essential to build a data infrastructure that accumulates various data types. In such organizations as universities, industries, and government bodies, the integration of heterogeneous data and cross-sectional analysis have been an issue as these various data are distributed and stored in different contexts. Knowledge Graphs with a graphical structure that can flexibly change the schema are suitable for such heterogeneous data integration. In this study, we focused on a university campus as an example of a small organization and propose an ontology that enables the cross-sectional analysis of various data. In particular, we semantically interlinked the dimensions in the data model to enable the extraction of data across multiple domains from various perspectives. Then, the unstructured data collected were accumulated as knowledge Graphs based on the proposed ontology to build a data infrastructure. In addition, we found several correlations that could help in solving university campus issues and improving university management using the developed ontology-based data infrastructure.