Abstract
Ontology Design Patterns (ODP) have been proposed to facilitate ontology engineering. Despite numerous conceptual contributions for over more than a decade, there is little empirical work to support the often claimed benefits provided by ODPs. Determining ODP use from ontologies alone (without interviews or other supporting documentation) is challenging as there is no standard (or required) mechanism for stipulating the intended use of an ODP. Instead, we must rely on modelling features which are suggestive of a given ODP’s influence. For the purpose of determining the prevalence of ODPs in ontologies, we developed a variety of techniques to detect these features with varying degrees of liberality. Using these techniques, we survey BioPortal with respect to well-known and publicly available repositories for ODPs. Our findings are predominantly negative. For the vast majority of ODPs we cannot find empirical evidence for their use in biomedical ontologies.
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These characterisations are not as clear-cut as they might appear. The discussion on the submission for the ODP ContextSlices http://ontologydesignpatterns.org/wiki/Submissions:Context_Slices exemplifies differences of opinion on the matter in the research community.
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Substitutions are assumed to respect types, i.e., classes, properties, and individuals are only mapped to other classes, properties, and individuals respectively.
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The types of axioms we consider in this study are all subclasses of the OWLAxiom interface http://owlcs.github.io/owlapi/apidocs_5/org/semanticweb/owlapi/model/OWLAxiom.html of the well-known OWL API.
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The SubstitutionContainmentCheck has been abbreviated by SContainmentCheck for presentation purposes.
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The distance is implemented via https://rosettacode.org/wiki/Levenshtein_distance#Java. The similarity score between [0, 1] of two strings \(s_1, s_2\) is calculated by \(\frac{M - LevenstheinDistance(s_1, s_2)}{M}\) where M is \(\max (s_1.length, s_2.length)\).
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We also considered using the label of entities \(e_1\) from \(\mathcal {P}\). However, these either coincide with the ShortFormIRI of \(e_1\) or are slight variations thereof. Such variations are captured by our string comparison procedure.
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SubstitutionContainmentCheck has been abbreviated by SContainmentCheck for presentation purposes. Likewise for SubstitutionEntailmentCheck.
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Experiment 4 is not designed with two conditions for including or not including a given \(\mathcal {P}\)’s import closure as in Experiment 2. This is owed to the fact that ODPs focusing on logical modelling structures do not import other ontologies.
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Kindermann, C., Parsia, B., Sattler, U. (2019). Detecting Influences of Ontology Design Patterns in Biomedical Ontologies. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_18
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