## 3 Materials and Methods
These triples can be built using upper-level ontology predicates, intended to describe the relationships between different ontologies and/or terminologies. The choice of predicate varies according to the purpose of the augmentation content and its relationship to the EOI. Typically, predicates such as _match_ or _exactMatch_ from the SKOS ontology are appropriate to define relationships between coded values from different terminologies. We encourage the use of the exactMatch predicate for unambiguous matches. Additionally, other predicates from the SKOS mapping properties can be used to detail the mapping relationship between the EOI coded value and the data item in the target dataset: mappingRelation, closeMatch, exactMatch, broadMatch, narrowMatch, and relatedMatch. At runtime, an automatic procedure can generate the EOI binding triples starting from the elements obtained in (A): the initial EHR extract, the names of the involved terminologies, and the URL of an existing LOD dataset holding the augmentation content. Specifically, the procedure takes the following inputs:  
1. To identify the EOI coded values (subjects in the EOI binding triples): 1.1 Initial EHR extract 1.2 Path to the EOI in the initial EHR archetype
2. To establish the predicate for the EOI binding triples: 2.1 The predicate URL (for instance, _skos:exactMatch_)
3. To collect the equivalent data items in the target dataset (objects in the EOI binding triples): 3.1 The name of the source terminology/ontology employed by the EOI coded values 3.2 The URL of the target terminology/ontology returned by the terminology mapping service. Both inputs (3.1 and 3.2) are used to query the terminology mapping service and retrieve data items from the target terminology equivalent to the EOI coded values. To format the equivalent data items (3) as URLs from an existing LOD dataset: 4.1 The URL prefix of data items in the target dataset. This allows the use of data items obtained from the terminology/ontology mapping service as objects in the EOI binding triples. Otherwise, the data returned by the mapping service might not be exploitable through a query language. By using these inputs, it is possible to implement a procedure that extracts EOI coded values, queries the terminology mapping service for equivalent data items, and builds the EOI binding triples using the provided predicate. This algorithm returns the EOI binding triples as a result. Note that, for a given augmentation content, the only argument varying among different executions is the initial EHR extract (1.1). The rest of the arguments must be identified once at the time of design and remain valid for all of the executions. Similarly, the described procedure to build the EOI binding triples only needs to be implemented once and is valid for all executions. The example shows how this procedure works. Example: Continuing with the example introduced in (A), the next step is to develop the algorithm described in (B) to build the EOI binding triples. In the example, the algorithm takes the inputs: 1.1. An EHR extract of the medication EHR archetype that has a coded value for the drug Filgrastim (SNOMED-CT code _386948008_) in the EOI. 1.2. The path to the EOI (i.e., drug coded value) in the medication EHR archetype: _items[archetype_id=\"at0005\"]/parts[archetype_id=\"at0010\"]/value_. 2.1. The predicate skos:exactMatch, whose full URL is [[https://www.w3.org/2009/08/skos-reference/skos.html#exactMatch](https://www.w3.org/2009/08/skos-reference/skos.html#exactMatch)]([https://www.w3.org/2009/08/skos-reference/skos.html#exactMatch](https://www.w3.org/2009/08/skos-reference/skos.html#exactMatch))[27]. 3.1. The name of the source terminology to be used in the terminology/ontology mapping service: _SNOMEDCT_. 3.2. The name of the target dataset to build the EOI binding triples: _DrugBank_. 4.1. The URL prefix _[http://bio2 rdf.org/drugbank:_](http://bio2 rdf.org/drugbank:_)[28] pointing to a LOD representation of the DrugBank dataset.