Knowledge Graph of University Campus Issues and Application of Completion Methods

Contemporary societies face many urban issues. To address these issues, governments, corporations and individuals should disclose and share their related statistical and sensory data. However, existing published data appear in various formats and contain defects. Therefore, few problems have been solved using these data. In this research, we sought to address this problem, by considering a university campus as a microcosm of society, designed data integration schema, and consolidated data into a knowledge graph. We then, applied and modified existing completion methods. In particular, regarding the bicycle environment, we trained our knowledge graph and evaluated it with the conventional method and our proposed derivative method, respectively. Using approximately 650 parking data with various dates and times, our method correctly estimated 54.5 more bicycles than the conventional method by comparing each time's mean absolute error.