## 2 Approach
Note that TS1 and SS2-2 are the same schemas and are designed to handle the same type of instances (i.e., augmentation content normalized as an EHR extract). The use case presented in this work (the augmented summary EHR) employs the W3C schema for query results [20] as the canonical form of Semantic Web contents (SS1), ISO13606 archetypes as the canonical representation of EHR extracts (SS2-1, SS2-2, TS1, and TS2), and the high level declarative mapping language of the LinkEHR platform [21] to define mapping specifications (M1 and M2) at the schema level, and to generate data transformations (T1 and T2) at the data level. Each EHR archetype may contain several items susceptible to be complemented with additional information from external sources (for example, coded values identifying a diagnosis). These items are the entities of interest (EOIs onwards). EOIs are represented by a path from the root of the EHR archetype to the EOI, and can be used to establish the parts of an EHR archetype that are potentially relevant to be augmented. Moreover, EOIs allow identification of the contents from the external sources that may be considered to be augmentation content to enrich the EHR. The EOIs are the binding between the EHR and the augmentation content, and are the starting point to explore external sources in search of related content. Some of these contents may be relevant in the context of the initial EHR archetype and are employed as the augmentation content. The process to design and create an augmented archetype for a given EHR archetype, using the Semantic Web as the external source, requires the following tasks to be performed and definitions at the schema level (Figure 2):  
1. Identify the EOIs in the initial EHR archetype. These entities are typically coded values and must be manually selected. Alternatively, free text values can be considered as EOIs whenever these values are translated into coded values (e.g., applying natural language processing methods or querying terminology repositories). 2. Identify a potentially interesting augmentation content from an external source. This requires selecting an information source related to the EOIs' coded values. Such a source must include data items representing the concepts of the EOIs (for instance, a set of drugs or a set of diseases), and must contain potentially relevant information to enrich the initial EHR archetype. The augmentation content will be collected from this source. 3. Ensure the feasibility of binding the EHR with the augmentation content by finding mappings between EOIs and equivalent data items in the external source. Typically, different terminologies use different codes to represent the same concept. This concept code mismatch among terminologies can be overcome by employing a terminology mapping service. In addition, VoID [22] descriptors may be useful for finding such mappings because they allow identification of the public datasets from the Semantic Web (such as those available in the LOD cloud) that contain coded values of the terminologies involved. 4. Build the binding between the EHR and the augmentation content. The binding must link EOIs from the initial EHR with equivalent data items in the external source,preserving the meaning of the EOIs in the context of the augmentation content. As this work is focused on collecting the augmentation content from Semantic Web sources, the binding (henceforth, the EOI binding triples) is expressed in RDF. The building of the EOI binding triples can be automatized with a procedure that, starting from the coded values of the EOI and the name of the involved terminologies, uses a terminology mapping service and information collected from VoID descriptors to generate the EOI binding triples. 5. Use the binding defined in step 4 to gather the augmentation content from the external source. A SPARQL query combines triples from the external source dataset and the EOI binding triples to collect the augmentation content.