Semantic big data for tax assessment

Semantic Big Data is about the creation of new applications exploiting the richness and flexibility of declarative semantics combined with scalable and highly distributed data management systems. In this work, we present an application scenario in which a domain ontology, Open Refine and the Okkam Entity Name System enable a frictionless and scalable data integration process leading to a knowledge base for tax assessment. Further, we introduce the concept of Entiton as a flexible and efficient data model suitable for large scale data inference and analytic tasks. We successfully tested our data processing pipeline on a real world dataset, supporting ACI Informatica in the investigation for Vehicle Excise Duty (VED) evasion in Aosta Valley region (Italy). Besides useful business intelligence indicators, we implemented a distributed temporal inference engine to unveil VED evasion and circulation ban violations. The results of the integration are presented to the tax agents in a powerful Siren Solution KiBi dashboard, enabling seamless data exploration and business intelligence.