A Quality Assurance Workflow for Ontologies Based on Semantic Regularities View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Eleni Mikroyannidi , Manuel Quesada-Martínez , Dmitry Tsarkov , Jesualdo Tomás Fernández Breis , Robert Stevens , Ignazio Palmisano

ABSTRACT

Syntactic regularities or syntactic patterns are sets of axioms in an OWL ontology with a regular structure. Detecting these patterns and reporting them in human readable form should help the understanding the authoring style of an ontology and is therefore useful in itself. However, pattern detection is sensitive to syntactic variations in the assertions; axioms that are semantically equivalent but syntactically different can reduce the effectiveness of the technique. Semantic regularity analysis focuses on the knowledge encoded in the ontology, rather than how it is spelled out, which is the focus of syntactic regularity analysis. Cluster analysis of the information provided by an OWL DL reasoner mitigates this sensitivity, providing measurable benefits over purely syntactic patterns - an example being patterns that are instantiated only in the entailments of an ontology. In this paper, we demonstrate, using SNOMED-CT, how the detection of semantic regularities in entailed axioms can be used in ontology quality assurance, in combination with lexical techniques. We also show how the detection of irregularities, i.e., deviations from a pattern, are useful for the same purpose. We evaluate and discuss the results of performing a semantic pattern inspection and we compare them against existing work on syntactic regularity detection. Systematic extraction of lexical, syntactic and semantic patterns is used and a quality assurance workflow that combines these patterns is presented. More... »

PAGES

288-303

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-13704-9_23

DOI

http://dx.doi.org/10.1007/978-3-319-13704-9_23

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041629253


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