Designing Innovative Linked Open Data and Semantic Technologies for Agro-environmental Modelling

In recent years, innovative applications exploiting Linked Open Data (LOD) and the Semantic Web have opened up, combined and cross referenced high volumes of high-quality data and created tremendous new opportunities for data users as well as data providers. However, in order to serve the broadest community of users, technologies need to be developed that can manage large, constantly updated datasets and streams that are published in formats that were not designed with cross source linking in mind. The EU-FP7 project SemaGrow aims to tackle this challenge by developing novel algorithms and methods for querying distributed triple stores, scalable and robust semantic indexing algorithms and effective ontology alignment. These innovations will be tested by applying them to data and knowledge intensive use cases from the agro-environmental domain. Aspects like the relatively large heterogeneity of datasets in this domain, their often explicit spatial and temporal dimensions resulting in relatively large volumes and their inherent nature of uncertainty provide additional challenges which are not usually dealt with till so far. This paper describes the architectural design of the SemaGrow infrastructure and how it integrates LOD concepts and a range of semantic technologies to meet these challenges. The presented SemaGrow use cases describe some concrete challenges and help understanding how applying these innovations will provide agro-environmental modellers with new opportunities to discover and combine distributed datasets for use in their models, to handle data gaps and achieve data volume reduction.