## 1 Introduction  
The Electronic Health Record (EHR) constitutes a key piece of a patient's healthcare, particularly because the paradigm of healthcare services has been changing towards a patient-centered scenario [1]. The completeness and accuracy of the EHR has a direct impact on the patient's healthcare results [2]. Moreover, additional potentially relevant information may be useful to complement a patient's EHR data for several scenarios [3, 4], including secondary uses of the EHR or clinical decision support. Part of this useful content may be obtained from public data sources such as the Linked Open Data (LOD) cloud [5, 6], for example, the set of contraindicated drugs for a patient considering active medication and disorders. An integration of the EHR with other information sources, such as Semantic Web sources, could complement the EHR with such information. This work introduces the augmented EHR (or EHR enrichment), understood as an existing EHR complemented with additional contents from external data sources. These contents may be any kind of information and/or knowledge that may be of interest for inclusion in the EHR. There are several approaches to achieve the augmented EHR, ranging from transforming information from external sources into a normalized EHR data, to transforming EHR data to the format of such sources. In both cases, ensuring a seamless integration requires some sort of data transformation to obtain a representation of both contents expressed in the same format. Data transformation in turn requires defining the relationships existing between both sources. Taking into account the kind of additional information to be included in the EHR, the EHR enrichment can be considered at the instance level or the schema level. In the first case, the enrichment of the EHR is individually performed for each patient (for instance, including additional information related to a specific patient's medication), whereas in the second case, the enrichment is performedonly once and is valid for all patients (for instance, including additional explanations about the meaning of the \"active medication\" section in the EHR). Nonetheless, the EHR and the external sources are continuously evolving and changing, so in both cases, the EHR enrichment should be performed when necessary and several versions of augmented EHRs could be required to ensure it remains valid over time. This work proposes a preliminary approach to address the augmented EHR at instance level, enriching EHR extracts with contents from the Semantic Web, in addition to presenting a prototype to demonstrate its feasibility. Note that the concept of augmented EHR, as introduced here, comprises the inclusion of information from heterogeneous sources in the EHR, regardless of the initial representation of such information in their original sources. Although this work introduces a first approximation to build the augmented EHR with contents from the Semantic Web, the presented methodology can be adapted to obtain augmented EHRs using contents from other heterogeneous sources and/or even other EHRs. Specifically, the methodology is based on the identification and formal definition of bindings between an EHR and a Semantic Web source, starting from an EHR archetype. The binding, which must be designed manually, is used to identify and collect additional information (henceforth, the augmentation content) potentially relevant for the patient from Semantic Web sources. Finally, the augmentation content is normalized as an EHR extract and embedded within the original EHR, resulting in an augmented EHR extract that represents a seamless integration of both sources. Note that in this work \"EHR extract\" and \"EHR archetype instance\" are interchangeably used to refer to normalized patient's health data. Additionally, we introduce the augmented summary EHR, a use case where the original summary EHR is augmented with augmentation contents from two different Semantic Web sources.