## 1 Introduction
### State of the Art  
The benefits of combining the EHR with contents from heterogeneous information sources are well known and have been widely described. Most works in this field have focused on transforming EHR data to a semantic representation to perform reasoning for secondary uses of the EHR. [PERSON] et al. [11] presented a solution for reasoning clinical archetypes using OWL ontologies and SWRL expressions. They argued that currently available archetype languages do not provide direct support for mapping to formal ontologies and then exploiting reasoning on clinical knowledge. They translated definitions expressed in the OpenEHR Archetype Definition Language (ADL) to formal OWL representation, and then applied SWRL rules to instances of clinical data. Similar approaches have been employed to improve secondary uses of the EHR. [PERSON] et al. [12] introduced an OWL representation of the clinical element model (CEM), to perform reasoning and consistency checking on derived models for secondary uses of the EHR. [PERSON] et al. [13] applied Semantic Web technologies to leverage the EHR standards for the identification of patient cohorts. Both works established approaches to convert EHR data into semantic representation for reasoning. However, EHR data expressed as semantic representation has a limited degree of interoperability in normalized health IT systems in compliance with the dual-model EHR architecture.  
The conversion of EHR data into semantic representation has been widely explored to improve the interoperability of EHR data. [PERSON] et al. [14] designed a method for EHR data mining using linked data. They transformed a de-identified version of the Sandford's STRIDE database [15] into a semantic clinical data warehouse to perform cohort selection, phenotyping profile, and identification of disease genes. [PERSON] et al. [16] presented a telehealth data management framework to support integrated care services for chronic patients. They built it upon HL7 and ontologies to obtain a semantically enriched representation of the EHR data following linked data principles. Nevertheless, none of recent works addressed the transformations in the opposite direction, from Semantic Web representation to a formal representation of the EHR.  
[PERSON] et al. [17] introduced Infobuttons, a method to access external knowledge resources from the EHR. It consists of a set of specifications defining URLs parametrized with data from the EHR extract. When the user clicks one of these links, additional information is retrieved from an external source and shown to the user as HTML. Although the Infobuttons approach has been incorporated as part of the HL7 standard, its main limitation is the lack of interoperability of this solution with EHR standards based on the dual model representation. Similarly, [PERSON] et al. [18] presented a method to obtain additional definitions of clinical terms. It starts from EHR extracts and queries a thesaurus to provide explanations of medical terms through a web service. [PERSON] et al. [19] introduced a non-standard solution to enrich EHRs with additional contents from the nursing service. It helps nurses to document and reason about patient data and about the clinician's understanding of patient data. The solutions of both [PERSON] et al. and [PERSON] et al. highlight the relevance of enriching EHR data with potentially relevant information from external sources. However, there is a lack of standardization of the additional information combined with EHR extracts, leading to a limited degree of interoperability of the additional information in normalized health IT systems. The augmented EHR presented in this paper aims to mitigate this limitation by enriching archetype-based EHR extracts with additional information from Semantic Web sources expressed in terms of EHR archetypes.