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The RICHFIELDS Framework for Semantic Interoperability of Food Information Across Heterogenous Information Systems

: In an EU-funded project RICHFIELDS, a data platform was designed with the aim to collect, link and harmo-nize, analyze, store, and deliver food- and nutrition-related data and information to various stakeholders. To integrate heterogenous food data sets, we propose a RICHFIELDS framework for semantic interoperability of food information, which is a combination of already developed NLP approaches for the food domain. The framework includes i) a food ontology to which foods are linked, ii) a part that explains how the relevant foods can be extracted and represented in a structured way, and iii) a similarity measure that is used to link the foods to the ontology. To evaluate the RICHFIELDS framework, we selected two distinct data sets from different food information systems. The experimental results provided promising results,i.e., 81.5% and 87.5% of the foods from the ﬁrst and the second data set, respectively, obtained a tag from the ontology (i.e., semantic annotation was performed). The annotations provided by the framework allow automatic integration of food information provided in both data sets.