Semantic Modeling of Events Using Linked Open Data

Significant happenings in terms of spatio-temporal factors are called events. In the digital age, these events and their associated features are scattered in various databases on the Internet. The event data are in heterogeneous formats, which are often not machine-readable. This leads to a lack of unification of eventrelated knowledge across different domains and results in a research gap in terms of event modeling and representation. Specialized event models are needed to overcome this gap and integrate relevant information of different similar events occurring worldwide. Our research explores the problem of heterogeneity in specialized event modeling and takes modeling for refugee registration and repatriation events as a case study. Our research explores the problem of heterogeneity at the data level and proposes a solution to this problem in the field of refugee registration and repatriation events. Considering refugee migration is one of the biggest crises in the world. The proposed model is designed according to Semantic Web standards to ensure reusability and machine readability. The project uses Protege to model classes and ontology. Our ontology is called OntoEvent ontology and Karma is used for data mapping over ontology. Heterogeneity for the same concepts collected through the internet and through UNHCR (reports, excel sheets) is analyzed and resolved during the data modeling phase. As a result of this research, a timeline is designed to visualize events over time, along with a semantic data model and Linked Open Data representation of refugee data that we believe is of global significance. The W3C Ontology Validation Service has successfully validated the proposed OntoEvent ontology.