Challenges, approaches and solutions in data integration for research and innovation

In order to be implemented by policy makers, science, technology, and innovation (science, technology, and innovation (STI)) policies and indicator building need data. Whenever we need data, we need a method for data management, and in the era of big data big data, a crucial role is played by data integration big dataintegration. Therefore, STI policies and indicator development need data integration. Two main approaches to data integration exist, namely procedural and declarative. In this chapter, we follow the latter approach and focus our attention on the ontology-based data integration (ontology-based dataintegration (OBDI)) paradigm. The main principles of OBDI are: (i)Leave the data where they are.(ii)Build a conceptual specification of the domain of interest (ontology), in terms of knowledge structures.(iii)Map such knowledge structures to concrete data sources.(iv)Express all services over the abstract representation.(v)Automatically translate knowledge services to data services. We introduce the main challenges of data integration for research and innovation (researchand innovation (R&I)) and show that reasoning over an ontology connected to data may be very helpful for the study of R&I. We also provide examples by using Sapientia, an ontology specifically defined for multidimensional research assessment.