=Paper=
{{Paper
|id=Vol-1265/pd2
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1265/owled2014_submission_4.pdf
|volume=Vol-1265
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==None==
https://ceur-ws.org/Vol-1265/owled2014_submission_4.pdf
Approximating DL reasoning by
ontology-aware RDF querying
Catalina Martínez-Costa, Stefan Schulz
IMI, Medical University of Graz, Austria
{catalina.martinez,stefan.schulz}@medunigraz.at
Abstract. We present initial experiments of transforming an expressive OWL
DL model into RDF, with the goal of maximally preserving its semantics.
Based on a semantic interoperability use case (three different ways of encoding
the same medical information), we use OWL DL representation and reasoning
results as gold standards and compare them with the results of querying two de-
rived RDF representations enhanced by pre-computing of inferred triples by us-
ing the RL profile and approximate custom rules.
Keywords: OWL, Ontology, SPARQL, RDF, electronic health records
We have proposed a methodology of enriching data in electronic health records by
semantic annotations that account for both ontological (represented by clinical termi-
nologies) and epistemological (represented by health record information models)
aspects [1]. The figure depicts three hypothetic forms to record the same statement
“suspected heart failure caused by ischaemic heart disease”. The content of each form
is represented as an instance of the class Diagnostic statement. These instances are
typed by OWL DL TBox expressions, as shown for the rightmost form (d3):
Organ Failure Diagnosis Diagnosis Diagnosis
Organ Heart Suspected heart failure caused Heart Failure
by ischaemic heart disease
Status Suspected Status
Yes x Suspected
Caused by
ischaemic No
heart Unknown Cause
disease
Ischaemic heart
disease
d3 rdf:type 'Diagnostic statement' and 'is about situation' only 'Heart failure'
d3 rdf:type 'Diagnostic statement' and 'has information attribute' some 'Suspected '
d3 rdf:type 'Diagnostic statement' and 'is about situation' only ('Disorder'
and 'is caused by' some 'Ischaemic heart disease')
Anticipating scalability problems of expressive OWL DL T-boxes we approximate
the results obtained by OWL DL reasoning by an alternative approach that uses RDF,
SPARQL and materializations, triggered by axioms from the underlying OWL ontol-
ogy. We present two RDF-based representations: (i) the OWL/RDF serialization of
the original OWL DL axioms, and (ii) an RDF representation obtained as a simplifi-
cation of the OWL DL one, in which the subject and object of the RDF triple corre-
spond to ontology classes and the predicate to an OWL DL expression. For the d3
instance, which is of the type 'Diagnostic statement':
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 de-
scribed by using the axiom:
d1 rdf:type 'Diagnostic statement'
and 'is about situation' only ('Organ failure' and 'is included in' some 'Heart')
A DL reasoner is able to infer equivalence between 'Heart failure' and the expres-
sions '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 interme-
diate 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 ex-
pressiveness 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 se-
mantic 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 rea-
soning, taking into account recent developments of triple stores.
Acknowledgements. This work was funded by the SemanticHealthNet Network of Excellence within
the EU 7th FP, Call:FP7-ICT-2011-7, agreement 288408. http://www.semantichealthnet.eu/
References
1. SemanticHealthNet. http://www.semantichealthnet.eu/ (accessed September 2014).
2. W3C SPIN. http://www.w3.org/Submission/spin-overview/ (accessed September 2014).
3. Zhou, Y. et al. (2013). Making the most of your triple store: query answering in OWL 2 us-
ing an RL reasoner. Proc. of the 22nd international WWW conference , 1569-1580.