## 2 Approach  
The aim is to enrich EHR extracts with data drawn from heterogeneous semantically rich information sources. To achieve a widely applicable solution, the starting points are: (i) EHR instances normalized according to a set of **EHR archetypes** (i.e., ISO13606 or OpenEHR archetypes), and (ii) augmentation content expressed in Semantic Web format (usually structured and stored as RDF datasets). Next, another set of EHR archetypes (hereinafter **augmentation archetypes**), modelled specifically to represent the augmentation content as normalized EHR extracts, allow homogenization of the format of the augmentation content and the initial EHR extract. A last EHR archetype called **augmented archetype**, which comprises the initial EHR archetype and augmentation archetypes, allows a seamless integration of the initial EHR extract and the augmentation content into the augmented EHR. Note that the augmented archetype may contain one or more augmentation archetypes. For coherency reasons, the structure of the augmentation archetype will be determined by the one of the initial EHR archetype. The process to define an augmented EHR relies on EHR standards, EHR archetypes, and mapping scenarios. Formally, this process can be defined in terms of two data exchange problems (data exchange 1 and 2 in Figure 1): to normalize augmentation contents into EHR extracts, and to combine initial EHR extracts with normalized augmentation contents into augmented EHR extracts. In our approach, a data exchange setting is defined by a 3-tuple <[SS], TS, M>. Here a tuple is used to formally define a data exchange operation, and should not be confused with the possible RDF tuples to which such operation can be applied. In each tuple, [SS] stands for one or more source schemas, TS for a target schema, and M for a set of mapping specifications between [SS] and TS. Furthermore, data exchanges at data level are represented by the transformation T, which applies the mapping definitions in M to transform instances of [SS] into instances of TS. The first data exchange problem is to normalize the augmentation content. This is defined at the schema level by the tuple <[SS1], TS1, M1>, where SS1, the data schema for Semantic Web contents, is a canonical representation of the augmentation content expressed in Semantic Web format. TS1, the augmentation archetype, is a canonical form  
Figure 1: Data exchange operations required to build augmented EHR extracts. (**a**) At schema level and (**b**) at data level depict the operations to normalize the augmentation content as an EHR extract, whereas (**c**) at schema level and (**d**) at data level show the operations to transform the initial EHR extract plus the normalized augmentation content into an augmented EHR extract. of the augmentation content expressed in EHR archetypes. Finally, M1 is the specification of relationships between elements in SS1 and elements in TS1. At the data level, the transformation T1 is an implementation of M1 intended to normalize the augmentation content into an EHR extract by converting instances of [SS1] into instances of TS1. The second data exchange problem is to combine initial EHR extracts with normalized augmentation contents into augmented EHR extracts. The tuple <[SS2-1, SS2-2], TS2, M2 > defines this data exchange at the schema level, where SS2-1 is the initial EHR archetype and SS2-2 is the augmentation archetype. The two source schemas are canonic representations of the initial EHR extract and the normalized augmentation content, respectively. The target schema TS2, the augmented archetype, is a canonical form of the augmented EHR extract. Finally, M2 represents the mapping specifications between elements in [SS2-1, SS2-2] and elements in TS2. At the data level, the transformation T2 is an implementation of M2 intended to generate augmented EHR extracts by converting instances of [SS2-1, SS2-2] into instances of TS2.