We have proposed a methodology of enriching data in electronic health records by semantic annotations that account for both ontological (represented by clinical terminologies) and epistemological (represented by health record information models) aspects [1] Each representation is then checked against a set of SPARQL 1.1 queries, by using Virtuoso 7.0 and the implementation of the OWL 2 RL profile as a set of SPARQL queries [2], as well as custom rules to address the needs of the specific representation. Not all the queries retrieve the three diagnosis instances even after the application of the OWL 2 RL rules. As an example, a query asking explicitly about heart failure diagnoses does not retrieve the instance of the leftmost form, where its type is described by using the axiom:

A DL reasoner is able to infer equivalence between 'Heart failure' and the expressions 'Organ failure' and isIncludedIn some Heart. However, in RDF it would be required to create a specific rule that each time that there is a triple ('Organ Failure' 'included in' Heart), then another one including 'Heart Failure' would be also created.

The drawback of the representation (i) is its complexity due to numerous intermediate blank nodes. The main drawback of (ii) is the need of keeping the track of the instances when generating the triples.

Although the OWL 2 RL captures a substantial fragment of OWL 2, it restricts expressiveness and is not able to capture existentially quantified expressions [3]. This entails that not all queries retrieve the desired data.

Our work presented here is preliminary and based on a concrete pattern of a semantic interoperability use case that is highly relevant for health care data. Further work will address more use cases and explore other ways of approximating DL reasoning, taking into account recent developments of triple stores.